Nvidia Tesla GPU Computing. Revolutionizing High Performance Computing.

Nvidia Tesla GPU Computing. Revolutionizing High Performance Computing.
                                                                                                                                               REVOLUTIONIZING HIGH PERFORMANCE

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associated. All rights reserved. 09/2010
Nvidia Tesla GPU Computing. Revolutionizing High Performance Computing.

                                     The high performance computing (HPC) industry’s need for
                                     computation is increasing, as large and complex computational
                                     problems become commonplace across many industry segments.
                                     Traditional CPU technology, however, is no longer capable of scaling
                                     in performance sufficiently to address this demand.

                                     The parallel processing capability of the GPU allows it to divide
                                     complex computing tasks into thousands of smaller tasks that can
                                     be run concurrently. This ability is enabling computational scientists
                                     and researchers to address some of the world’s most challenging
                                     computational problems up to several orders of magnitude faster.
Nvidia Tesla GPU Computing. Revolutionizing High Performance Computing.
“                        The convergence of new, fast GPUs optimized
                                                                                     for computation as well as 3-D graphics
                                                                                     acceleration and industry-standard software
                                                                                     development tools marks the real beginning
                                                                                     of the GPU computing era.”

                                                                                     This advancement represents a
                                                                                                                                                             Nathan Brookwood
                                                                                                                                       Principal Analyst & Co-Founder, Insight64

                                                                                                                                               From climate modeling to advances in
                                                                                                                                                                                                 Adobe digital
                                                                                                                                                                                                content creation

                                                                                                                                                                                               MATLAB Computing
                                                                                                                                                                                                                        Video Transcoding
                                                                                                                                                                                                                         Elemental Tech

                                                                                                                                                                                                                        Weather Modeling
                                                                                                                                                                                                                                                      3D Ultrsound

                                                                                                                                                                                                                                                                              Gene Sequencing
                                                                                                                                                                                                                                                                               U of Maryland

                                                                                                                                                                                                                                                                                Medical Imaging
                                                                                                                                                                                                                                                                                                      Molecular Dynamics
                                                                                                                                                                                                                                                                                                      U of Illinois, Urbana

                                                                                                                                                                                                                                                                                                      Financial Simulation
                                                                                                                                                                                                  AccelerEyes      Tokyo Institute of Technology         RIKEN                    U of Utah                 Oxford
                                                                                     dramatic shift in HPC. In addition to                     medical tomography, NVIDIA® Tesla™
                       Co-processing refers to                                       dramatic improvements in speed,                           GPUs are enabling a wide variety of
                       the use of an accelerator,                                    GPUs also consume less power than                         segments in science and industry to
                       such as a GPU, to offload                                     conventional CPU-only clusters.                           progress in ways that were previously

                       the CPU to increase                                           GPUs deliver performance increases                        impractical, or even impossible, due

                       computational efficiency.                                     of 10x to 100x to solve problems                          to technological limitations.                WHY GPU COMPUTING?                            As sequential processors, CPUs               Tesla GPU computing is delivering
                                                                                     in minutes instead of hours–while                                                                                                                    are not designed for this type of            transformative increases in
                                                                                                                                                                                            With the ever-increasing demand                                                            performance for a wide range of HPC
                                                                                     outpacing the performance of                                                                                                                         computation, but they are adept
                                                                                                                                                                                            for more computing performance,                                                            industry segments.
                                                                                     traditional computing with x86-based                                                                                                                 at more serial based tasks such
                                                                                                                                                                                            the HPC industry is moving toward a
                                                                                     CPUs alone.                                                                                                                                          as running operating systems and
                                                                                                                                                                                            hybrid computing model, where GPUs
                                                                                                                                                                                                                                          organizing data. NVIDIA believes in
                                                                                                                                                                                            and CPUs work together to perform
                                                                                                                                                                                                                                          applying the most relevant processor
                                                                                                                                                                                            general purpose computing tasks.
                                                                                                                                                                                                                                          to the specific task in hand.
                              Conventional CPU computing                                                                                                                                    As parallel processors, GPUs excel
                        architecture can no longer support                                                                                                          Performance Advantage   at tackling large amounts of similar
                                   the growing HPC needs.                                                                                                                  by 2021
                                                                                                                                                                                            data because the problem can be split
                                Source: Hennesse]y &Patteson, CAAQA, 4th Edition.
                                                                                                                                                                                            into hundreds or thousands of pieces
                                                                                                                                                                                            and calculated simultaneously.

                                                                                                                               20% year

Performance vs VAX

                                                                                52% year

                                        25% year                                                                                                         GPU
                                                                                                                                                         Growth per year
                             1978    1980    1982    1984     1986    1988    1990   1992   1994   1996   1998   2000   2002    2004    2006                                   2016
                                                                                                                                                                                                                                                                                             NVIDIA TESLA
                                                                                                                                                                                                                                                                                             GPUS ARE REVOLUTIONIZING
Nvidia Tesla GPU Computing. Revolutionizing High Performance Computing.
GPU COMPUTING APPLICATIONS                                  The CUDA parallel computing
                                                                                                                                                                                               architecture, with a combination of
                                                                                                                                       Libraries and Middleware
                                                                                                                                                                                               hardware and software.
                                                                                                                           Language Solutions                          Device level APIs

                                                                                                                                                     Java and
                                                                                                                C           C++           Fortran     Python                       OpenCL™

Core comparison between
                                                                                                                          NVIDIA GPU CUDA Parallel Computing Architecture
       a CPU and a GPU.

                                 CPU                                           GPU
                           Multiple Cores                             Hundreds of Cores

                          Parallel                              critical portions of an application     CUDA PARALLEL                               With support for C++, GPUs based on
                          Acceleration                          to take advantage of the hundreds       COMPUTING                                   the Fermi architecture make parallel
                                                                of parallel cores in the GPU. The       ARCHITECTURE                                processing easier and accelerate
                          Multi-core programming with x86
                                                                rest of the application remains the                                                 performance on a wider array of
                          CPUs is difficult and often results                                           CUDA™ is NVIDIA’s parallel computing
                                                                same, making the most efficient use                                                 applications than ever before. Just a
                          in marginal performance gain when                                             architecture. Applications that
                                                                of all cores in the system. Running                                                 few applications that can experience
                          going from 1 core to 4 cores to 16                                            leverage the CUDA architecture can
                                                                a function of the GPU involves                                                      significant performance benefits
                          cores. Beyond 4 cores, memory                                                 be developed in a variety of languages
                                                                rewriting that function to expose                                                   include ray tracing, finite element
                          bandwidth becomes the bottleneck to                                           and APIs, including C, C++, Fortran,
                                                                its parallelism, then adding a few                                                  analysis, high-precision scientific
                          further performance increases.                                                OpenCL, and DirectCompute. The
                                                                new function-calls to indicate which                                                computing, sparse linear algebra,
                                                                                                        CUDA architecture contains hundreds
                          To harness the parallel computing     functions will run on the GPU or the                                                sorting, and search algorithms.          The next generation CUDA parallel
                                                                                                        of cores capable of running many
                          power of GPUs, programmers can        CPU. With these modifications, the                                                                                           computing architecture, codenamed
                                                                                                        thousands of parallel threads, while

                          simply modify the performance-        performance-critical portions of the                                                                                         “Fermi”.
                                                                application can now run significantly
                                                                                                        the CUDA programming model lets
                                                                                                                                                       I believe history
                                                                                                        programmers focus on parallelizing
                                                                faster on the GPU.
                                                                                                        their algorithms and not the
                                                                                                                                                    will record Fermi as a
                                                                                                        mechanics of the language.                  significant milestone.”
                                                                                                        The latest generation CUDA
                                                                                                                                                    Dave Patterson
                                                                                                        architecture, codenamed “Fermi”, is         Director, Parallel Computing
                                                                                                        the most advanced GPU computing             Research Laboratory, U.C.
                                                                                                        architecture ever built. With over
                                                                                                        three billion transistors, Fermi is         Co-author of Computer
                                                                                                        making GPU and CPU co-processing
                                                                                                                                                    Architecture: A Quantitative
                                                                                                        pervasive by addressing the full-
                                                                                                        spectrum of computing applications.

                                                                                                                                                                                               NVIDIA TESLA
                                                                                                                                                                                               GPUS ARE REVOLUTIONIZING
Nvidia Tesla GPU Computing. Revolutionizing High Performance Computing.
Solution Stone and his team
                                                                                                                      turned to the NVIDIA CUDA parallel
                                                                                                                      processing architecture running
                                                                                                                                                                and its mutations—but it also bought
                                                                                                                                                                them time to make other important
                                                                                                                                                                discoveries. Further calculations
                                                                                                                                                                                                          “    The benefits of
                                                                                                                                                                                                          GPU computing can
                                                                                                                      on Tesla GPUs to perform their            showed that genetic mutations which       be replicated in other
                                                                                                                      molecular modeling calculations and       render the swine or avian flu resistant   research areas as well.
                                                                                                                      simulate the drug resistance of H1N1      to Tamiflu had actually disrupted
                                                                                                                                                                                                          All of this work is made
                                                                                                                      mutations. Thanks to GPU technology,      the “binding funnel,” providing new
                                                                                                                                                                understanding about a fundamental
                                                                                                                                                                                                          speedier and more
                                                                                                                      the scientists could efficiently run
                                                                                                                      multiple simulations and achieve          mechanism behind drug resistance.         efficient thanks to GPU
  Ribosome for protein synthesis.                                                                                     potentially life-saving results faster.
                                                                                                                                                                In the midst of the H1N1 pandemic,
                                                                                                                                                                                                          technology, which for us
                                                                                                                      Impact The GPU-accelerated                the use of improved algorithms            means quicker results
                                                                                                                      calculation was completed in just         based on CUDA and running on Tesla        as well as dollars and
                                                                                                                      over an hour. The almost thousand-        GPUs made it possible to produce
                                                                                                                                                                                                          energy saved.” said
WHO BENEFITS FROM GPU               University of Illinois: Accelerated molecular                                     fold improvement in performance           actionable results about the efficacy
COMPUTING?                                                                                                            available through GPU computing           of Tamiflu during a single afternoon.
                                    modeling enables rapid response to H1N1
Computational scientists                                                                                              and advanced algorithms empowered         This would have taken weeks or
and researchers who are                                                                                               the scientists to perform “emergency      months of computing to produce
                                    Challenge          A first step in       This determination involved a                                                      the same results using conventional
using GPUs to accelerate                                                                                              computing” to study biological
                                    mitigating a global pandemic, like       daunting simulation of a 35,000-                                                   approaches.
their applications are                                                                                                problems of extreme current
                                    H1N1, requires quickly developing        atom system, something a group
seeing results in days                                                                                                relevance and share their results with
                                    drugs to effectively treat a virus       of University of Illinois, Urbana-
instead of months, even                                                                                               the medical research community.
                                    that is new and likely to evolve. This   Champaign scientists, led by John
minutes instead of days.
                                    requires a compute-intensive process     Stone, decided to tackle in a new way    This speed and performance increase

                                    to determine how, in the case of         using GPUs.                              not only enabled researchers to

                                    H1N1, mutations of the flu virus                                                  fulfill their original goal—testing
                                                                             Conducting this kind of simulation on
                                    protein could disrupt the binding                                                 Tamiflu’s efficacy in treating H1N1
                                                                             a CPU would take more than a month
                                    pathway of Tamiflu, rendering it
                                                                             to calculate...and that would only
                                    potentially ineffective.
                                                                             amount to a single simulation, not the
                                                                             multiple simulations that constitute a
                                                                             complete study.

                                                                                                                                                                                                           NVIDIA TESLA
                                                                                                                                                                                                           Case Study: University of Illinois
Nvidia Tesla GPU Computing. Revolutionizing High Performance Computing.
Using imaging devices like a CT          Impact      GPUs provide 20x more
                                                                                                                              scan, scientists are able to create      computational power and an order
                                                                                                                              a model of a patient’s circulatory       of magnitude more performance
                                                                                                                              system. From there, an advanced          per dollar to the application of
                                                                                                                              fluid dynamics simulation of the blood   image reconstruction and blood
                                                                                                                              flow through the patient’s arteries      flow simulation, finally making such
                                                                                                                              can be conducted on a computer to
                                           Harvard University:                                                                                                         advanced simulation techniques
                                                                                                                              identify areas of reduced endothelial    practical at the clinical level.
                                           Finding Hidden Heart Problems Faster                                               sheer stress on the arterial wall.       Without GPUs, the amount of
                                                                                                                              A complex simulation like this one       computing equipment—in terms of
                                           Challenge         Heart attacks are      the plaque could greatly improve
                                                                                                                              requires billions of fluid elements to   size and expense—would render a
                                           the leading cause of death worldwide.    patient care and save lives.
                                                                                                                              be modeled as they pass through an       hemodynamics approach unusable.
                                           Heart attacks are caused when
                                                                                    Solution       A team of researchers,     artery system. An area of reduced        Because it can detect dangerous
                                           plaque that has built up on artery
                                                                                    including doctors at Harvard Medical      sheer stress indicates that plaque       arterial plaque earlier than any
                                           walls dislodges and blocks the flow
                                                                                    School and Brigham & Women’s              has formed on the interior artery        other method, it is expected that
                                           of blood to the heart. Up to 80% of
                                                                                    Hospital in Boston, Massachusetts,        walls, preventing the bloodstream        this breakthrough could save
                                           heart attacks are caused by plaque
                                                                                    have discovered a non-invasive            from making contact with the inner       numerous lives when it is approved
                                           that is not detectable by conventional
                                                                                    way to find dangerous plaque in a         wall. The overall output of the          for deployment in hospitals and
    320 detector-row CT has enabled        medical imaging. Even viewing the
                                                                                    patient’s arteries. Tapping into the      simulation provides doctors with         research centers.
  single heart beat coronary imaging       20% that is detectable requires
  so that the entire coronary contrast                                              computational power of GPUs, they         an atherosclerotic risk map. The
                                           invasive endoscopic procedures which
     opacification can be evaluated at                                              can create a highly individualized        map provides cardiologists with the
                                           involve running several feet of tubing
       a single time point. The full 3D                                             model of blood flow within a patient in   location of hidden plaque and can
        course of the arteries, in turn,   into the patient in an effort to take
                                                                                    a study of blood flow which is called     serve as an indicator as to where
  allows researchers to simulate the       pictures of arterial plaque.
                                                                                    hemodynamics.                             stents may eventually need to
     blood flowing through it by using
                                           This level of uncertainty with regard                                              placed—and all of this knowledge
computational fluid flow simulations,                                               The buildup of plaque is highly
       and subsequently compute the        to the exact location of potentially                                               is gained without invasive imaging
                                                                                    correlated to the shape—or
             endothelial shear stress.     deadly plaque poses a significant                                                  techniques or exploratory surgery.
                                                                                    geometry—of a patient’s arterial
                                           challenge for cardiologists.
                                                                                    structure. Bends in an artery tend to
                                           Historically, it has been a guessing
                                                                                    be areas where dangerous plaque is
                                           game for heart specialists to
                                                                                    especially concentrated.
                                           determine if and where to place
                                           arterial stents in patients with
                                           blockages. Knowing the location of
                                                                                                                                                                                                              NVIDIA TESLA
                                                                                                                                                                                                              Case Study: Harvard University
Nvidia Tesla GPU Computing. Revolutionizing High Performance Computing.
                                                                                                                         Solution MotionDSP, a software            Using NVIDIA Tesla GPUs, MotionDSP’s
                                                                                                                         company based in San Mateo,               customers, which include a variety of             With the GPU, we’re
                                                                                                                         California, has developed “super-         military-funded research groups, are           bringing better video
                                                                                                                         resolution” algorithms that allow it      making UAVs safer and more reliable            to all ISR platforms,
                                                                                                                         to reconstruct video with better and      while reducing deployment costs,
                                                                                                                                                                                                                  including smaller UAVs.
                                                                                                                         cleaner detail, increased resolution      improving simulation accuracy and
                                                                                                                         and reduced noise. All of which are       dramatically boosting performance.
                                                                                                                                                                                                                  We’re helping these
                                                                                                                         ideal for the live streaming of video                                                    platforms perform to
                                                                                                                                                                   impact Using only CPUs to execute
                                                                                                                         from the cameras attached to a UAV.
                                                                                                                                                                   the kind of sophisticated video post-
                                                                                                                                                                                                                  their potential by being
                                        MotionDSP: GPUs increasing importance in                                         MotionDSP’s product, Ikena ISR,           processing algorithms required for             smarter about how
                                        Armed Forces                                                                     targets NVIDIA’s CUDA GPU parallel        effective reconnaissance would result in       we utilize COTS PC
                                                                                                                         computing architecture allowing it        up to six hours of processing for each one
                                                                                                                                                                                                                  technology.” said Sean
                                        Challenge         Unmanned Aerial       The challenge is that the resulting      to render, stabilize and enhance live     hour of video—not a viable solution when
                                        Vehicles (UAVs) represent the latest    images need to be rendered,              video faster and more accurately than     real-time results are critical. In contrast,
                                                                                                                                                                                                                  Varah, chief executive
                                        in high-tech weaponry deployed          stabilized and enhanced in real-time     its competitors. Ikena ISR features       Tesla GPUs enable MotionDSP’s Ikena            officer of MotionDSP.
                                        to strengthen and improve the           and across vast distances in order       computationally intense, advanced         ISR software to process any live video         “Our technology makes
                                        military’s capabilities. But with new   to be useful and accurate. Once they     motion-tracking algorithms that           source in real-time with less than 200ms
                                                                                                                                                                                                                  the impossible possible,
                                        technologies come new challenges,       have been processed, the images          provide the basis for sophisticated       of latency. Moreover, instead of requiring
                                        such as capturing actionable            can give infantry critical information   image stabilization and super-            expensive CPU-clustered computing
                                                                                                                                                                                                                  and this is making
 “Super-resolution” algorithms that     intelligence while flying at speeds     about potential challenges ahead—        resolution video reconstruction.          systems to complete the work, Ikena can        our military safer and
    allow MotionDSP to reconstruct      upwards of 140 mph, 10 miles above      the end goal being to ensure the         Perhaps most importantly, it can all be   perform at full capacity on a standard         better prepared.”
video with better and cleaner detail,   the earth.                              safety and protection of military        run on off-the-shelf Windows laptops      workstation small enough to fit inside of
           increased resolution and
                                                                                personnel in the field.                  and servers.                              the military’s field vehicles.
                     reduced noise.     One key feature of the UAV is that it
                                        is capable of providing a real-time     Using CPUs alone, this process is
                                        stream of detailed images taken with    very time consuming and doesn’t
                                        multiple different cameras situated     allow information to be viewed
                                        on the vehicle itself.                  in real-time. As a result, military
                                                                                action could be based on potentially
                                                                                outdated intelligence data and
                                                                                inaccurate guides.

                                                                                                                                                                                                                   NVIDIA TESLA
                                                                                                                                                                                                                   Case Study: MotionDSP
Nvidia Tesla GPU Computing. Revolutionizing High Performance Computing.
Bloomberg: GPUs increase accuracy and reduce
                                         processing time for bond pricing

                                         Challenge         Getting mortgage and
                                         buying a home is a complex financial
                                         transaction, and for lenders, the
                                         competitive pricing and management
                                         of that mortgage is an even greater
                                         challenge. Transactions involving
                                         thousands of mortgages at once are
                                         a routine occurrence in financial
                                         markets, spurred by banks that wish
                                                                                  Known as collateralized debt
                                                                                  obligations (CDO) and collateralized
                                                                                  mortgage obligations (CMO), baskets
                                                                                  of thousands of loans are publicly
                                                                                  traded financial instruments. For the
                                                                                  banks and institutional investors who
                                                                                  buy and sell these baskets, timely
                                                                                  pricing updates are essential because
                                                                                  of fast-changing market conditions.
                                                                                                                             “               One of the challenges Bloomberg always faces is that we have
                                                                                                                                             very large scale. We’re serving all the financial and business
                                                                                                                                             community and there are a lot of different instruments and
                                                                                                                                             models people want calculated. ”

                                                                                                                             This technique requires calculating
                                                                                                                             huge amounts of data, from interest
                                                                                                                                                                       In addition, the capital outlay for the
                                                                                                                                                                       new GPU-based solution was one-
                                                                                                                                                                                                                              Shawn Edwards,
                                                                                                                                                                                                                              CTO, Bloomberg

                                         to sell off loans to get their money
                                                                                  Bloomberg, one of the world’s leading      rate volatility to the payment behavior   tenth the cost of an upgraded CPU          GPU ACCELERATION
                                         back sooner.
                                                                                  financial services organizations, prices   of individual borrowers. These data-      solution, and further savings are being    Large calculations that
                                                                                  CDO/CMO baskets for its customers          intensive calculations can take hours     realized due to the GPU’s efficient        had previously taken up
                                                                                  by running powerful algorithms that        to run with a CPU-based computing         power and cooling needs.                   to two hours can now be
                                                                                  model the risks and determine the price.   grid. Time is money, and Bloomberg                                                   completed in two minutes.
                                                                                                                                                                       Impact      As Bloomberg customers
                                                                                                                             wanted a new solution that would allow                                               Smaller runs that had
                                                                                                                                                                       make CDO/CMO buying and selling
                                                                                                                             them to get pricing updates to their                                                 taken 20 minutes can now
                                                                                                                                                                       decisions, they now have access to
                                                                                                                             customers faster.                                                                    be done in just seconds.
                                                                                                                                                                       the best and most current pricing
  Financial engineering is integral to
                                                                                                                             Solution Bloomberg implemented            information, giving them a serious
today’s buying and selling decisions.
                                                                                                                             an NVIDIA Tesla GPU computing             competitive trading advantage in a
                                                                                                                             solution in their datacenter. By          market where timing is everything.
                                                                                                                             porting this application to run on the
                                                                                                                             NVIDIA CUDA parallel processing
                                                                                                                             architecture to harness the power of
                                                                                                                             GPUs, Bloomberg received dramatic
                                                                                                                             improvements across the board. Large
                                                                                                                             calculations that had previously taken
                                                                                                                             up to two hours can now be completed
                                                                                                                             in two minutes. Smaller runs that had
                                                                                                                             taken 20 minutes can now be done in
                                                                                                                             just seconds.

                                                                                                                                                                                                                 NVIDIA TESLA
                                                                                                                                                                                                                 Case Study: Bloomberg
Nvidia Tesla GPU Computing. Revolutionizing High Performance Computing.
This step change in performance            “Access to high
                                                                                                                               significantly increases the amount of
                                                                                                                               data that geoscientists can analyze in a
                                                                                                                               given timeframe. Plus, it allows them      high quality 3D
                                                                                                                               to fully exploit the information derived   computational tools on
                                                                                                                               from 3D seismic surveys to improve         a workstation platform
                                                                                                                               subsurface understanding and reduce
                                                                                                                               risk in oil and gas exploration and
                                                                                                                                                                          drastically changes
                                                                                                                               exploitation.                              the productivity
                                                                                                                               Impact NVIDIA CUDA is allowing ffA
                                                                                                                                                                          curve in 3D seismic
                                                                                                                               to provide scalable high performance       analysis and seismic
                                                                                                                               computation for seismic data on            interpretation, giving
                                        ffA: Accelerating 3D seismic interpretation                                            hardware platforms equipped with one       our users a real edge in
                                                                                                                               or more NVIDIA Tesla and Quadro FX
                                                                                                                               GPUs. The latest benchmarking results
                                                                                                                                                                          oil and gas exploration
                                        Challenge In the search for oil            Solution UK-based company ffA
                                        and gas, the geological information        provides world leading 3D seismic           using Tesla GPUs have produced             and de-risking field
streamling with GPUs                    provided by seismic images of the          analysis software and services to the       performance improvements of up to          development.”
for Better Results                                                                                                             37x versus high end dual quad core
                                        earth is vital. By interpreting the data   global oil and gas industry. Its software
                                        produced by seismic imaging surveys,       tools extract detailed information from     CPU workstations.
           40                           geoscientists can identify the likely      3D seismic data, providing a greater

                                        presence of hydrocarbon reserves and
                                        understand how to extract resources
                                        most effectively. Today, sophisticated
                                                                                   understanding of complex 3D geology,
                                                                                   improving productivity and reducing
                                                                                   uncertainty within the interpretation
                                                                                                                               “  CUDA-based
                                                                                                                               PUGs power large

           20                           visualization systems and                  process. The sophisticated tools are        computation tasks
                                        computational analysis tools are used      compute-intensive so it can take            and interactive
                                        to streamline what was previously a        hours, or even days, to produce results
                                        subjective and labor intensive process.    on conventional high performance
                                                                                   workstations.                               workflows that we
            0                           Today, geoscientists must process
                Quad-Core     Tesla
                                        increasing amounts of data as              With the recent release of its
                                                                                                                               could not hope to
                  CPU       GPU + CPU
                                        dwindling reserves require them            CUDA enabled 3D seismic analysis            implement effectively
The latest benchmarking                 to pinpoint smaller, more complex          application, ffA users routinely achieve    otherwise.” said Steve
results using Tesla GPUs                reservoirs with greater speed and          over an order of magnitude speed-up         Purves, ffA Technical                                                 Data courtesy of RMOTC

have produced performance               accuracy.                                  compared with performance on high
improvements of up to 37x                                                          end multi-core CPUs.
versus high end dual quad
core CPU workstations.

                                                                                                                                                                                                        NVIDIA TESLA
                                                                                                                                                                                                        Case Study: ffA
Nvidia Tesla GPU Computing. Revolutionizing High Performance Computing.
The Shift to Parallel Computing
and the Path to Exascale

Approximately every 10 years, the world of supercomputing experiences a
fundamental shift in computing architectures. It was around 10 years ago when
cluster-based computing largely superseded vector-based computing as the
de facto standard for large-scale computing installations, and this shift saw
the supercomputing industry move beyond the petaFLOP performance barrier.
With the next performance target being exascale-class computing, it is time for
a new shift in computing architectures— the move to parallel computing.

The shift is already underway.

                                                                                  NVIDIA TESLA
                                                                                  GPUS ARE REVOLUTIONIZING
“                Future computing architectures will be hybrid
                                        systems with parallel-core GPUs working in
                                        tandem with multi-core CPUs.”

                                        In November 2008, Tokyo Institute
                                        of Technology became the first
                                        supercomputing center to enter
                                                                                                       Jack Dongarra,
                                                         University Distinguished Professor at University of Tennessee

                                                                                   What is even more impressive than
                                                                                  its overall performance is the power
                                                                                  that it consumes. Jaguar delivers
                                                                                                                           “   Five years from
                                                                                                                           now, the bulk of
                                                                                                                           serious HPC is going
                                                                                                                           to be done with some
                                                                                                                           kind of accelerated
                                                                                                                           architecture.” said
                                                                                                                           Steve Scott, CTO,
                                                                                                                           Cray, Inc.
                                                                                                                                                                       and tertiary oil recovery using multi-
                                                                                                                                                                       scale simulation of porous materials,
                                                                                                                                                                       the simulation of nano- and micro-
                                                                                                                                                                       flow in chemical and bio-chemical
                                                                                                                                                                       processes, and much more.

                                                                                                                                                                       These computational problems
                                                                                                                                                                       represent a tiny fraction of the entire
                                                                                                                                                                       landscape of computational challenges
                                                                                                                                                                       that we face today and these problems
                                                                                                                                                                       are not getting any smaller. The sheer
                                                                                                                                                                       quantity of data that many scientists,
                                                                                                                                                                       engineers and researchers must               “Nebulae”, powered by 4640 Tesla
                                        the Top500 with a GPU-based               1.77 petaFLOPs, yet it consumes                                                      analyze is increasing exponentially and      20-series GPUs, is one of the fastest
                                                                                                                                                                                                                    supercomputers in the world.
                                        hybrid system —a system that              more than 7 megawatts of power                                                       supercomputing centers are over-
                                        uses GPUs and CPUs together to            to do so. To put that into context, 7                                                subscribed as demand is outpacing
                                        deliver transformative increases          megawatts is enough energy to power                                                  the supply of computational resources.
                                                                                                                           CAS is one of the world’s most dynamic
                                        in performance without breaking           15,000 homes. In contrast, Nebulae                                                   If we are to maintain our rate of
                                                                                                                           research and educational facilities.
                                        the bank with regards to energy           delivers 1.27 petaFLOPs, yet it does                                                 innovation and discovery, we must take
                                                                                                                           Prof. Wei Ge, Professor of Chemical
                                        consumption. The system, called           this within a power budget of just                                                   computational performance to a level
                                                                                                                           Engineering at the Institute of Process
                                        TSUBAME 1.2, entered the list at          2.55 megawatts. That makes it twice                                                  where it is 1000 times faster than what
                                                                                                                           Engineering at CAS, introduced GPU
                                        number 24.                                as power-efficient as Jaguar. This                                                   it is today. The GPU is a disruptive force
                                                                                                                           computing to the Beijing facility in 2007
   TSUBAME 1.2, by Tokyo Institute of                                             difference in computational throughput                                               in supercomputing and represents
                                        Fast forward to June 2010 and                                                      to help them with discrete particle and
  Technology, was the first Tesla GPU                                             and power is owed to the massively                                                   the only viable strategy to successfully
                                        hybrid systems have started to make                                                molecular dynamics simulations. Since
based hybrid cluster to reach Top500.                                             parallel architecture of GPUs, where                                                 build exascale systems that are
                                        appearances even higher up the list.                                               then, parallel computing has enabled
                                                                                  hundreds of cores work together                                                      affordable and not prohibitive to build
                                        “Nebulae”, a system installed at                                                   the advancement of research in dozens
                                                                                  within a single processor, delivering                                                and operate.
                                        Shenzhen Supercomputing Center                                                     of other areas, including: real time
                                                                                  unprecedented compute density for
                                        in China, equipped with 4640 Tesla                                                 simulations of industrial facilities, the
                                                                                  next generation supercomputers.
                                        20-series GPUs, made its entry into                                                design and optimization of multi-phase
                                        the list at number 2, just one spot       Another very notable entry into          and turbulent industrial reactors using
                                        behind Oak Ridge National Lab’s           this year’s Top500 was the Chinese       computational fluid dynamics, the
                                        “Jaguar”, the fastest supercomputer       Academy of Sciences (CAS). The “Mole     optimization of secondary
                                        in the world.                             8.5” supercomputer at CAS uses 2200
                                                                                  Tesla 20-series GPUs to deliver 207
                                                                                  teraFLOPS, which puts it at number 19
                                                                                  in the Top500.

                                                                                                                                                                                                                      NVIDIA TESLA
                                                                                                                                                                                                                      The Shift to Parallel
                                                                                                                                                                                                                      Computing and the Path to
World’s First Computational GPU

                                  The Tesla brand of products is    Highly Reliable
                                  designed for high-performance     > ECC protection for
                                  computing, and offers exclusive     uncompromised data
                                  computing features.                 reliability
                                  Superior Performance              > Stress tested for zero error
                                  > Highest double precision          tolerance
                                    floating point performance      > Manufactured by NVIDIA
                                  > Large HPC data sets             > Enterprise-level support
                                    supported by larger on-board      that includes a three-year
                                    memory                            warranty
                                  > Faster communication with       Designed for HPC
                                    InfiniBand using NVIDIA
                                                                    > Integrated into leading OEM
                                                                      including workstations,
                                                                      servers, and blades

                                                                                                     NVIDIA TESLA
                                                                                                     GPUS ARE REVOLUTIONIZING
Tesla Data Center Products
                                                                                  Available from OEMs and certified resellers, Tesla GPU computing products are
                                                                                  designed to supercharge your computing cluster.

                                                                                  Highest Performance, Highest Efficiency

                                                                                                      Performance                                                Performance/$                         Performance/watt
                                                                                                         Gflops                                                    Gflops/$K                             Gflops/kwatt

                                                                                  750                                                              70                                        800
                                                                                                                                                                                5x           600
                                                                                  450                                                              40
TESLA GPU COMPUTING SOLUTIONS                                                                                                                                                                400
The Tesla 20-series GPU computing solutions are designed from the ground up
for high-performance computing and are based on NVIDIA’s latest CUDA GPU          150
architecture, code named “Fermi”. It delivers many “must have” features for HPC                                                                    10

including ECC memory for uncompromised accuracy and scalability, C++ support,       0                                                               0                                          0
and 7x double precision performance compared to CPUs. Compared to the typical                CPU Server          GPU-CPU                                   CPU Server         GPU-CPU               CPU Server    GPU-CPU
                                                                                                                  Server                                                       Server                              Server
quad-core CPUs, Tesla 20-series GPU computing products can deliver equivalent
performance at 1/10th the cost and 1/20th the power consumption.                        CPU 1U Server: 2x Intel Xeon X5550 (Nehalem) 2.66 GHz, 48 GB memory, $7K, 0.55 kw
                                                                                        GPU-CPU 1U Server: 2x Tesla C2050 + 2x Intel Xeon X5550, 48 GB memory, $11K, 1.0 kw             GPU-CPU server solutions deliver up
                                                                                                                                                                                        to 8x higher Linpack performance.

                                                                                  Tesla M2050/M2070 GPU Computing                             Tesla S2050 GPU Computing System
                                                                                  Modules enables the use of GPUs and                         is a 1U system powered by 4 Tesla
                                                                                  CPUs together in an individual server                       GPUs and connects to a CPU server.
                                                                                  node or blade form factor.

                                                                                                                                                                                          NVIDIA TESLA
                                                                                                                                                                                          GPU computing SOLuTIONS
Tesla Workstation Products                                                         DEVELOPER ECOSYSTEM AND WORLDWIDE EDUCATION
           Workstations powered by Tesla GPUs        Designed to deliver cluster-level performance on a workstation, the NVIDIA Tesla   In just a few years, an entire software ecosystem has developed around the CUDA
             outperform conventional CPU-only        GPU Computing Processors fuel the transition to parallel computing while making    architecture—from more than 350 universities worldwide teaching the CUDA
           solutions in life science applications.   personal supercomputing possible—right at your desk.                               programming model, to a wide range of libraries, compilers and middleware that
                                                                                                                                        help users optimize applications for GPUs.

           Highest Performance, Highest Efficiency                                                                                      The NVIDIA GPU Computing Ecosystem

           MIDG: Discontinuous         AMBER Molecular           FFT: cuFFT 3.1 in          OpenEye ROCS              Radix Sort                                                                                          A rich ecosystem of software
           Galerkin Solvers for        Dynamics (Mixed           Double Precision           Virtual Drug              CUDA SDK                                                                                            applications, libraries, programming
           PDEs                        Precision)                                           Screening                                                                                                                     language solutions, and service
                                                                                                                                                                                                                          providers supporting the CUDA
           120                             8                       8                        180                          8                                         RESEARCH &                                             parallel computing architecture.
                                                                                                                                                                    EDUCATION        LIBRARIES
                                           7                       7                        160                          7
                                           6                       6                        140                          6                         INTEGRATED
            80                                                                                                                                   DEVELOPMENT                                      MATHEMATICAL
                                           5                       5                        120                          5

            60                             4                       4                        100                          4
                                                                                                                                                   LANGUAGES                                      CONSULTANTS,
                                           3                       3                         80                          3
            40                                                                                                                                         & APIs                                     TRAINING, &
                                           2                       2                         40                          2                                                                        CERTIFICATION
                                           1                       1                         20                          1                                           ALL MAJOR       TOOLS
             0                             0                       0                          0                          0                                          PLATFORMS        & PARTNERS

                    Intel Xeon X5550 CPU
                    Tesla C2050

                                                     Tesla C2050/C2070 GPU Computing
                                                     Processor delivers the power of
                                                     a cluster in the form factor of a
                                                                                                                                                                                                                            NVIDIA TESLA
                                                                                                                                                                                                                            GPU computing ECOSYSTEM
NVIDIA’s CUDA architecture has the industry’s most robust language and API            In addition to the CUDA C development tools, math libraries, and hundreds of
                                         support for GPU computing developers, including C, C++, OpenCL, DirectCompute,        code samples in the NVIDIA GPU computing SDK, there is also a rich ecosystem of
                                         and Fortran. NVIDIA Parallel Nsight, a fully integrated development environment       solution providers:
                                         for Microsoft Visual Studio is also available. Used by more than six million
                                         developers worldwide, Visual Studio is one of the world’s most popular                Libraries and Middleware Solutions

                                         development environments for Windows-based applications and services. Adding          >   Acceleware FDTD libraries              GPU Debugging Tools
                                         functionality specifically for GPU computing developers, Parallel Nsight makes the    >   CUBLAS, complete BLAS library*         >   Allinea DDT
                                         power of the GPU more accessible than ever before.                                    >   CUFFT, high-performance FFT            >   Fixstars Eclipse plug-in
                                                                                                                                   routines*                              >   NVIDIA cuda-gdb*
                                                                                                                               >   CUSP                                   >   NVIDIA Parallel Nsight for Visual
                                                                                                                               >   EM Photonics CULA Tools,                   Studio
                                                                                                                                   heterogeneous LAPACK                   >   TotalView Debugger
                                                                                                                                                                          GPU Performance Analysis Tools
                                                                                                                               >   NVIDIA OptiX and other AXEs*
                                                                                                                               >   NVIDIA Performance Primitives for      >   NVIDIA Visual Profiler*
                                                                                                                                   image and video processing             >   NVIDIA Parallel Nsight for Visual
                                                                                                                                   (www.nvidia.com/npp)*                      Studio
                                                                                                                               >   Thrust                                 >   TAU CUDA
                                                                                                                                                                          >   Vampir
                                                                                                                               Compilers and Language Solutions
                                                                                                                               >   CAPS HMPP                              Cluster and Grid Management
                                                                                                                               >   NVIDIA CUDA C Compiler (NVCC),
                                                                                                                                   supporting both CUDA C and CUDA        >   Bright Cluster Manager
                                                                                                                                   C++*                                   >   NVIDIA system management
                                                                                                                               >   Par4All                                    interface (nvidia-smi)
                                                                                                                               >   PGI CUDA Fortran                       >   Platform Computing
                                                                                                                               >   PGI Accelerator Compilers for C        Math Packages
                                                                                                                                   and Fortran
                                                                                                                                                                          >   Jacket by AccelerEyes
                                                                                                                               >   PyCUDA
                                                                                                                                                                          >   Mathematica 8 by Wolfram
                                                                                                                                                                          >   MATLAB Distributed Computing
                                         NVIDIA Parallel Nsight Development
                                                                                                                                                                              Server (MDCS) by Mathworks
                                                                                                                                                                          >   MATLAB Parallel Computing
      NVIDIA Parallel Nsight software    NVIDIA Parallel Nsight software is the industry’s first development environment for                                                  Toolbox (PCT) by Mathworks
integrates CPU and GPU development,      massively parallel computing integrated into Microsoft Visual Studio, the world’s
 allowing developers to create optimal   most popular development environment. It integrates CPU and GPU development,
        GPU-accelerated applications.
                                         allowing developers to create optimal GPU-accelerated applications.

                                         Parallel Nsight supports Microsoft Visual Studio 2010, advanced debugging
                                         and analysis capabilities, as well as Tesla 20-series GPUs, built on the latest
                                         generation CUDA parallel computing architecture codenamed “Fermi”. For more
                                         information, visit developer.nvidia.com/object/nsight.html.

                                                                                                                                                                                                                  NVIDIA TESLA
                                                                                                                                                                                                                  GPU computing ECOSYSTEM
Consulting and Training                    Education and Certification

Consulting and training services are      >   CUDA Certification (www.nvidia.
available to support you in porting           com/certification)

applications and learning about           >   CUDA Center of Excellence
developing with CUDA. For more
                                          >   CUDA Research Centers (research.
information, visit www.nvidia.com/
                                          >   CUDA Teaching Centers (research.
                                          >   CUDA and GPU computing books

*   Available with the latest CUDA toolkit at www.nvidia.com/getcuda

For more information about the CUDA Certification Program,
visit www.nvidia.com/certification.                                               To learn more about NVIDIA Tesla, go to www.nvidia.com/tesla.
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