Personal Volunteer Computing - Computing Frontier 2019 Sardinia, Italy, May 1st 2019 - Erick Lavoie
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Personal Volunteer
Computing
Erick Lavoie, Laurie Hendren
McGill University
Computing Frontier 2019
Sardinia, Italy, May 1st 2019
Motivation Paradigms
Applicability Future
This presentation will cover 4 aspects: the motivation for introducing a new distributed computing paradigm, its comparison to existing ones, its applicability today, and
possible future directions.
Pioneer Cycle
Faster and More
Technical Innovation
Efficient Devices
New Applications
Industry Growth and and Services /
More Devices Sold More Profitable
Operations
The success of computing technologies has been fuelled by a virtuous cycle of:
1. technical innovation, that leads to
2. better machines,
3. opening new applications and services, and more profitable operations,
4. that grow industrial applications and sell more devices, that generate resources for new innovation
Billions Smartphones Sold (Source: Gartner) Slowing of Moore’s Law
1.6 1E-03 that it isn’t. His 1988 publication reviewed funda-
Landauer 1988
1E-05 mental research demonstrating the possibility of an
kT (300)
1.2 IB M
energy-conserving form of computation. As in today’s
1E-07
Switching energy (J)
Intel
circuits, the devices in energy-conserving circuits
ITRS
0.8 1E-09
Rapid V reduction would store enough energy—many times kT—to
1E-11 Slower V reduction reliably distinguish the digital state from the inevi-
0.4 1E-13 Clock freq. plateaus
THE END OF MOORE’S 1E-15
LAW table thermal noise. For good engineering reasons,
today’s circuits dissipate that stored energy every
0
2014 2015 2016 2017 2018 1E-17 time a device is switched. In contrast, energy-
1E-19 1988 extrapolation conserving circuits would dissipate only a small frac-
1E-21 tion of the stored energy in each switching event. In
1940 1960 1980 2000 2020 2040 such circuits, there’s no fundamental lower bound
Personal Laptop Collection! Year on the energy efficiency of digital computation.
100.00
Figure 1. Minimum switching energy dissipation in
1 of information.
A lthough Dynamic random access
no commercially viablememory
energy-
SRAM
logic devices used in computing systems CGPas a function (DRAM),
conserving the slower
computing but denser
systems and
emerged therefore
in the less
1990s
CGP/M1 pitch (µm)
of time.
10.00 Black diamonds replicate dataM1 from Rolf
pitch expensive memory
or later, digital that usually
quantum computing,resides
stillon memory
in its infancy,
SRAM area (µm2)
Landauer,5 and the dashed line is Landauer’s 1988 chips peripheral to the processor, uses one FETTo
exemplifies the energy-conserving approach.
extrapolation of the historic trend toward kT (evaluated and one capacitor to store a bit. Flash memory, theaf-
at T =1.00
300 K), indicated by the dotted line. Triangles 0.1 show what did happen in the commercial sector
and Xs are published values from IBM and Intel,
very densewe
ter 1988, butadded
ratherdataslowtomemory
Figure 1that storesswitch-
showing data
respectively, compiled by Chi-Shuen Lee and Jieying when the power is off, uses one
ing energies for minimum channel width CMOSFET with a specially
0.10
Luo at Stanford University during their PhD thesis designed gate structure
FET technologies based to store one bit (or
on technical more re-
publications
research with one of the authors (Wong). Open squares cently,
from IBM several andbits).
Intel.Thus
For each
a while,of these dominant
switching energy
are values
0.01 from the 2013 International Technology 0.01 devices in today’s memory hierarchy is subject to
1990 1995 2000 2005 2010
Roadmap for Semiconductors (ITRS). The data is 2015 2020 continued to drop rapidly, as IBM led the industry
Year scaling constraints similar to those
in rapidly reducing operating voltage. Roughly fol- for the FETs
available at https://purl.stanford.edu/gc095kp2609.
Current, up-to-date data can be accessed at https:// used
lowingin logic, plus additional
the elegant scaling rulesconstraints
laid outunique to
by Robert
Figure 2. Three key measures of integration
nano.stanford.edu/cmos-technology-scaling-trend. density as a each device. 6
function of time. Blue dots show static random access
Dennard and colleagues, each successive genera-
Despite theselower
daunting problems, we’redevices
very
This feedback loop has been so successful that, for example, there has now been enough memory smartphones
(SRAM) density. produced
Green trianglesto M1 pitch,onetion
provide
show for of
everysmaller,human voltage,
on earth.lowerBut powerthat also
the minimum wire-to-wire spacing in the first wiring
optimistic
was also faster.aboutIncreasingly
the prospectspotentfor further
CMOS dramatic
technol-
led to electronic “waste” in the form of under-used or even discarded but still-working older devices:
layer.Figure
Red squares here
show isormy personalpitch, collection of laptop
advances and phones
in computing technology. as aAftersample.
decades
1 shows aCGPvery contacted
importantgate exponential ogy extended the long run of exponential increases
the minimum spacing between transistors. Progress of progress centered on miniaturization of the
trend in information technology that already shows in microprocessor clock frequency7—a key mea-
is still rapid, but there’s evidence of slowing in recent 5 CMOS transistor, we see a growing potential for
At the same time the slowing of Moore’s law means that these older devices can be used longer,
a sharp
years. slowing
Data asofnewer
compiled progress. generations
In 1988,
from published bring
by Chi- smaller
Rolf Landauer
literature sure relative
of computing improvements.
performance—that had begun
advances based on the discovery and implemen-
published some remarkable data on energy
Shuen Lee at Stanford University during his PhD thesis dissipation with the Intel 4004 in 1972. And the ever smaller,
tation of truly new devices, integration processes,
in computing that had been collected over many years ever cheaper transistors enabled rapid elaboration
research with one of the authors (Wong). The data is
Both phenomenon open an opportunity for a new cycle, which we call the Seral Cycle. available at https://purl.stanford.edu/gc095kp2609. and architectures for computing. By truly new
by his IBM colleague Robert Keyes. From the 1940s of computer architecture. For example, the intro-
Current, up-to-date data can be accessed at https:// devices, we mean devices that operate by physical
through the 1980s, a span that includes the replace- duction in the 1990s of sophisticated approaches to
nano.stanford.edu/cmos-technology-scaling-trend. principles that are fundamentally different from
ment of vacuum tubes by bipolar transistors, the in- instruction-level parallelism (superscalar architectures)
the operating principle of the FET and are there-
vention of the integrated circuit, and the early stages further multiplied the system-level performance
fore not subject to its fundamental limits, particu-
of the replacement of bipolar transistors by field-effect gains from increasing clock speed. By the late 1990s,
performance gains have been muted. Furthermore, larly the voltage scaling limit. By truly new integra-
transistors
the (FETs), the typical
more straightforward energy dissipated
elaborations of the stan- in a tion
CMOS had displaced
technologies, we mean the monolithic
more power-hungry
integrationbi-
digital switching event dropped exponentially by over polar transistor
dard von Neumann computer architecture have in three dimensions in a fine-grained manner from its last remaining applications
that
10 orders
already of magnitude.
been implemented, We7 replotted that data in
and prospects for sig- immerses in high-performance
memory within computing. Despite these
computational units.tri-
Figure 1 along with Landauer’s extrapolation of the umphs, the historic
nificant performance gains from further increases And by truly new architectures, we mean circuit- rate of reduction in switching
trend
in toward switching
parallelism energies
appear limited even onatthetheorder of the and
multicore energy couldn’t be
higher-level maintainedthat
architectures through
are muchthe 1990s
moreas
thermal
level. 14,15fluctuation energy, kT, evaluated at T = 300 K. the FET approached
It’s no surprise then that the replacement energy efficient than the von Neumann architec- some fundamental constraints
Landauer
cycle for was well aware
computing that the of
equipment switching
all sortsenergy to itsparticularly
has ture, further development.
for the important algorithms and
would not approach kT around 2015, not with the es-
lengthened. An increasing number of semiconductor applications of the coming decades. We now touch
tablished complementary
manufacturers are findingmetal-oxide-semiconductor
profits by investing in de- briefly Physical on someConstraints on Continued
of the emerging research concepts
(CMOS) device and circuit technology. His extrapo- Miniaturization
velopment of product attributes, such as architectures that fuel our optimism.
lation
for was a way
improved of highlighting
memory access, thatthe possibility
have little of,
to and
do Note that this slowing of a highly desirable ex-
perhaps the need for,
with smaller feature size. a new way of computing. ponential
New Devices trend
for in
Logicinformation technology—the
Some have
Figure mistaken
2 shows progress theinkTthree
per switching
key indicatorsevent As break
we in slopeseveral
write, so evident in Figure
distinct physical1—has noth-
principles
as a fundamental lower bound on the energy con- ing to do with
of the achievable integration density for complex are known by which a voltage-gated switch (that the approach of switching energy
Seral Cycle
Valorize “Old” Devices
Technical Innovation
from Pioneering Cycle
New Applications
Generate Surpluses/ and Services
Donations with Low Margins
and Low Capital
In this cycle, the innovation consists in extracting more value out of older devices produced by the current Pioneering Cycle. As the cost of devices has already been
been borne for previous applications, they can be applied to newer applications with low margins and little capital. This could potentially generate surpluses that could
fuel further innovation.
Seral Cycle
Valorize “Old” Devices
Technical Innovation
from Pioneering Cycle
Distributed
Computing
Paradigm(s)?
New Applications
Generate Surpluses/ and Services
Donations with Low Margins
and Low Capital
But our field lacks wide-spread paradigms that fit that cycle. Let’s review what I consider the three existing major ones of distributed computing.
Cloud Computing
Paradigm Target Users
…
Resource Providers
…
First, Cloud computing is based on a computing market, where the computing resources are provided by major private companies that both use the infrastructure for
world-wide services and rent the same infrastructure to smaller companies, which has enabled some like Uber and AirBnB to grow to major platforms in a few years.
However, Clouds are inaccessible to those with no financial means or instruments and their economical management requires standardized homogenous resources at
scale. Therefore they cannot leverage previous heterogeneous personal devices.
Grid Computing
Paradigm Target Users
Resource Providers
Second, Grid Computing started with the vision of a computing utility that could integrate the computing resources from multiple participating organizations under
standard interfaces, similar to the way the electric grid has been developed. However, it survives today as a Scientific Utility for research groups that share common
computing infrastructure and is funded by governments.
In its current form, the Grid Resources are reserved to those with administrative permissions, typically employees of governmental organizations and students. They are
therefore not available to the general public. Moreover, similar to Clouds, the Grid approach also relies on mostly homogeneous and recent hardware.
Volunteer Computing
Paradigm Target Users
By Bonvallite - Own work, CC BY-SA 3.0,
https://commons.wikimedia.org/w/index.php?curid=23522958
Resource Providers
By Sam Howzit - V For Vendetta, CC BY 2.0,
https://commons.wikimedia.org/w/index.php?curid=42480624
As a third paradigm, Volunteer Computing relies on participants putting their resources in common to, for example, help research teams to discover life in the Universe or
design new drugs. BOINC, as the most popular tool for Volunteer Computing, relies on anonymous participants on the Internet to share computing cycles on their
computer. However, those participants cannot be trusted and the BOINC tools rely on dedicated hardware to reach hundred of thousands of participants.
We believe the complexity of the BOINC tools and the cost of acquiring dedicated hardware are two factors that limit the adoption of the paradigm beyond the current
niche.
Personal Volunteer Computing
Paradigm Target Users
Anyone with significant computing
needs but limited resources!
By Bonvallite - Own work, CC BY-SA 3.0,
https://commons.wikimedia.org/w/index.php?curid=23522958
Resource Providers
By Chocolatechocolate128 - Own work, By Mamhe Adw oaa - Own work, CC BY-SA 4.0,
CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=47546765 https://commons.wikimedia.org/w/index.php?curid=77324736
We therefore propose Personal Volunteer Computing as a more personal approach based on simpler tools, leveraging the inherent trust between friends and family and
focusing on smaller applications, to open the approach to anyone with significant computing needs but limited resources, financial or others, to satisfy them.Personal Volunteer Computing
Overview of Personal Volunteer Computing
Overview of Personal Volunteer Computing
Overview of Personal Volunteer
PersonalProjects
Personal Projects Computing
Personal
Personal Devices
Devices
Personal Projects Personal Devices
Overview
Personal of
Projects Personal Volunteer
PersonalComputing
Devices
Personal Projects Personal Devices
Personal Social Network Personal Tools
Personal Social Network Personal Tools
Personal
Personal Social
Social
Personal
Network
SocialNetwork
Network
Personal Tools
Personal
Personal ToolsTools
Said differently, Personal Volunteer Computing targets personal projects, uses all personal devices and those of the community of the project initiator, and provides
personal tools rather than global platforms. We believe this combination of characteristics has not been the focus of much research in the last decades and could provide
significant benefits to those that are not well served by the major paradigms of today.
Can Personal Volunteer Computing provide computing benefits given today’s devices?http://192.168.1.53:5000
Tablet Desktop Laptop
1 2
3 f(x) = …
Pando
To answer the question, we used Pando, a tool we built along that paradigm. It applies a function on every value of a stream but distributes the processing among a
dynamic set of participating devices. Each device performs the processing in their browser.tion, they want to parallelize the rendering of individual 5 module . exports [ / pando /1.0.0 ] = function (
frames, while still obtaining them in the correct order. cameraPos , cb ) {
Animation Rendering (raytracing) 6 try {
7 var pixels = render ( parseFloat ( cameraPos ) )
8 cb ( null , zlib . gzipSync ( new Buffer ( pixels ) ) .
toString ( base64 ) )
Collatz 9 } catch ( err ) {
10 cb ( err )
11 }
12 }
Figure 1. Rotation animation around a 3D scene. Figure 2. JavaScript programming interface example fo
Crypto-Mining rendering with raytracing.
(Bitcoin Proof-of-Work) ImageIf this
Processing Machine
were a professional project, our user Learning
could have 1 Thoselast three operations take a negligible amount of time compared
relied on professional solutions [20, 26]. However,
Agent Trainingthese are rendering the image.
3
Random-Testing
of Concurrent
Interleavings
of Pando Workers
We used the tool for six applications, including testing the correctness of the implementation of Pando itself, as well as synthesizing animations, or training a machine
learning agent. All are CPU-bound.Novena MacBook Pro Asus Laptop
(Linux Arm) 2016 (Windows Intel)
iPhone SE MacBook Air iPhone 4S
(2016, not shown) 2011 (2011)
We measured the collective computing contributions of my collection of laptops and phones, that includes a Linux ARM laptop, a Windows laptop, a MacBook Pro, and
two phones.MBPro 2016 iPhone SE Asus Laptop
MBAir 2011 Novena iPhone 4S
100 3.8 4.1 3.2 5.2
4.2
13 14.9 13.7 7.5 10
14.5
15.9 14
75 15.1 16.7 17.6
15.2
44.3 21.6
18.7 15.8
11.9 13.5
50
48.4 50.8 49.3 49.8 12.3
37.9
25
27.1
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An
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We obtained the following results. This graph shows the relative contribution of each device to the total throughput, normalized to a hundred percentage.MBPro 2016 iPhone SE Asus Laptop
MBAir 2011 Novena iPhone 4S
100 3.8 4.1 3.2 5.2
4.2
13 14.9 13.7 7.5 10
14.5
15.9 14
75 15.1 16.7 17.6
15.2
44.3 21.6
18.7 15.8
11.9 13.5
50
48.4 50.8 49.3 49.8 12.3
37.9
25
27.1
0
tz
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t.
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de
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la
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Ra
im
p
ge
ag
ry
An
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LA
Im
M
First, we can see that the iPhone 4S has an insignificant contribution, which illustrates that not all older devices are still useful.MBPro 2016 iPhone SE Asus Laptop
MBAir 2011 Novena iPhone 4S
100 3.8 4.1 3.2 5.2
4.2
13 14.9 13.7 7.5 10
14.5
15.9 14
75 15.1 16.7 17.6
15.2
44.3 21.6
18.7 15.8
11.9 13.5
50
48.4 50.8 49.3 49.8 12.3
37.9
25
27.1
0
tz
g
t.
r.
.
g
ss
de
in
in
es
la
e
in
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An
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But it also shows that all other devices, some as old as 2011, can collectively provide as much computing power as a top-of-the-line laptop from 2016.iPhone 4S/MBAir 2011 (1 core)
iPhone SE/MBPro 2016 (1 core)
1
3.28
11
1 0.77
0.63 0.64
0.55
0
0.47
0.25 0.3
0.07 0.1
0
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Interestingly, as I happened to have pairs of phones and laptop models from 2011 and 2016, I computed the performance ratio between a single core on each. This
showed that the gap between the two is clearly closing. In the image processing case, the phone was significantly faster but that was due to Safari performing
optimizations that Firefox was not doing on the MacBook Pro. Using Safari on the MacBook Pro provided similar results as for the other applications.Picture of
Experimental
Setup
As a second experiment, I invited my colleague to participate with their personal phones. That brought fun community dynamics to the experiment. It was also
significantly easier to convince colleagues I interact with regularly to contribute than anonymous strangers on the Internet.Lenovo P2a42 2016
7%
LG G6 H870 2017 Wileyfox Storm 2016
11% 5%
Xiaomi redmi note 6 pro
12%
Honor
5%
Samsung Galaxy S7 Zenfone 3
13% 4%
Samsung A3 2016
4%
Zenfone 2
Huawei P10 lite 2017 2%
15% Huawei P10 lite 2017
iPhone SE
1%
19%
Here is the break down of the relative contributions of the phones they brought on the random testing application.Lenovo P2a42 2016
7%
LG G6 H870 2017 Wileyfox Storm 2016
11% 5%
Xiaomi redmi note 6 pro
12%
Honor
5%
Samsung Galaxy S7 Zenfone 3
13% 4%
Samsung A3 2016
4%
Zenfone 2
Huawei P10 lite 2017 2%
15% Huawei P10 lite 2017
iPhone SE
1%
19%
First we can see that the iPhone SE contributed the most, although this could be due to performance scaling because it was plugged in while all other devices were
running from their battery.Lenovo P2a42 2016
7%
LG G6 H870 2017 Wileyfox Storm 2016
11% 5%
Xiaomi redmi note 6 pro
12%
Honor
5%
Samsung Galaxy S7 Zenfone 3
13% 4%
Samsung A3 2016
4%
Zenfone 2
Huawei P10 lite 2017 2%
15% Huawei P10 lite 2017
iPhone SE
1%
19%
This also seems to be the case for the Huawei phone as the slowest one’s screen locked during the experiment and probably went into power-saving mode.Lenovo P2a42 2016
7%
LG G6 H870 2017 Wileyfox Storm 2016
11% 5%
Xiaomi redmi note 6 pro
12%
Honor
5%
Samsung Galaxy S7 Zenfone 3
13% 4%
Samsung A3 2016
4%
Zenfone 2
Huawei P10 lite 2017 2%
15% Huawei P10 lite 2017
iPhone SE
1%
19%
Second, the performance difference between the fastest and slowest phones was significant, we observed a factor of 9 between the extremes.MacBook Pro 2016 (2 cores)
12 Smartphones (1 core/each)
2400
1800
1200
600
0
Random-Testing
Nonetheless, they collectively outperformed the Macbook Pro 2016, showing that there is value in using a collection of smartphones for computations.
Where do we go from here? Beside adding support for more applications and obtaining more and more second-hand devices, how could we extend the scope of
Personal Volunteer Computing in the future?Solar-powered Flashcrowd?
Picture of
Experimental
Setup
By Suntactics - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=26538312
Starting with a first example, as the data centre crowd as already realized, the energy source and intensity of computing devices has become significant. What if we gave
all participants inexpensive solar panels to power our computations from sunlight or other renewable energy? We could end up with solar-powered computing
flashcrowds!40k solar-powered CPU cores?
~= 2016’s Dell SuperComputing in South Africa!
Image Source: By Kondah - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=57730956
If we were to scale that up to entire stadiums of sport supporters, we could even reach the computing capabilities of a SuperComputer installed in Africa in 2016!
Source: https://www.zdnet.com/article/dells-new-supercomputer-is-the-fastest-in-africa/Asynchronous Intermittent
Computations?
As a second example, what if we synchronized computing operations with the availability of energy? It could be beneficial for long-running asynchronous computations,
perhaps some forms of indexation or machine learning training?Seral Cycle Personal Volunteer
Computing
Technical Valorize “Old” Devices
Innovation from Pioneering Cycle
Applications
Devices
Personal
Social Network
New Applications
Generate Surpluses/ and Services Tool
Donations with Low Margins
and Low Capital
Old Laptops ~= Macbook Pro
+ iPhone SE 2016
Decent. Renewable Energy?
Smartphone / Laptop
Intermittent computations?
12 Smartphones > Macbook Pro
2016
In summary, based on an innovation cycle that reuses older devices, I have proposed the Personal Volunteer Computing paradigm for Distributed Computing that targets
personal applications, personal devices, contributions from friends and family, and personal tools. Using Pando, built along these lines, we have shown various trends
that show that old devices can provide significant computing power. We finally proposed to extend the paradigm to take into account decentralized energy sources and
increase the scope of applications.Seral Cycle Personal Volunteer
Computing
Technical Valorize “Old” Devices
Innovation from Pioneering Cycle
Applications
Devices
Personal
Social Network
New Applications
Generate Surpluses/ and Services Tool
Donations with Low Margins
and Low Capital
?
Old Laptops ~= Macbook Pro
+ iPhone SE 2016
Decent. Renewable Energy?
Smartphone / Laptop
Intermittent computations?
12 Smartphones > Macbook Pro
2016
Questions??
Cloud Grid Volunteer Personal
Volunteer
Lower infras. Lower infras. Lower op. Lowest op.
Motivation costs & hard. costs
costs & risks costs
Paradigm Market Scientific Commons Commons
Utility
Ress.
Single Cie. Mult. org. General Public Friends & Family
Providers
Target Users Customers Researchers Researchers General Public
Funders Customers Governments Governments General Public?Edge/Gray P2P Volunteer Personal
Volunteer
Lower op. Lower op. Lowest op.
Motivation Reliability costs
costs & hard. costs
Paradigm Market Commons Commons
—
Target Users Customers Researchers Researchers General Public
Ress. End users — General Public Friends & Family
Providers
Platform P2P Tool
Trusted
op. Algo. op. Friends & Family
Parties
Global Global Persistent Transient
Design Platform Platform Tool Tool
Centralized Centralized
Coordination Centralized Distributed (disjoint) (disjoint)
Dedicated All Dedicated
Coordinators Servers User
Devices Server
DevicePersonal
Volunteer
Lowest op.
Motivation & hard. costs
Personal Applications
Paradigm Commons
General Public
Target Users
Ress. Friends & Family Personal Devices
Providers
Trusted Friends & Family Personal Social Network
Parties
Transient
Design Tool
Centralized
Personal Tool
Coordination (disjoint)
User
Coordinators Device
Can that work today?You can also read
