Keynote snir sc

Supercomputing: The Next 10 Years

Marc Snir Argonne Na.onal Laboratory & University of Illinois at Urbana-‐Champaign

Past

Those who cannot remember the past are condemned to repeat it (Santayana)

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MCS -‐-‐ Marc Snir

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The Last Great Extinction

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Core Count of leading Top500 System

The aJack of the killer micros

ShiL from bipolar vector processor to clusters of MOS microprocessors

1990: The Attack of the Killer Micros (Eugene Brooks, 1990)

§  Bipolar technology had hit a power wall (nitrogen cooling) §  Alterna.ve materials were too expensive /not ready (gallium arsenide) §  An alterna.ve “good enough” technology was ready

–  MOS microprocessors had been around 20 years and were a fast growing market

–  MOS had a clear evolu.on path (“Moore’s Law”)

§  MOS was no beJer than bipolar (in 1991)

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Cray C90 •  244 MHz •  Vector •  Vector registers •  16 shared-‐memory nodes

CM5 •  32 MHz •  Scalar •  Cache •  1024 message-‐

passing nodes

§  New paradigm took a while to establish itself (CM1, CM2, KSR…) §  Change in technology led to change in vendors and business model §  Technology shiL required a long and painful process of code rewrite

Present

The past no longer is and the future is not yet (St. Augus.ne)

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20 Years of (Near) Stability

§  One dominant programming model: Message-‐Passing (MPI) §  One major shiL – from single core to mul.core –  Easy since one can treat each core as a node

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Increasing Instability

§  Heterogeneous memory: NUMA, noncoherent shared memory, scratchpads…

§  Heterogenous processing: GPUs, accelerators, big-‐small cores (NVIDIA, Xeon Phi, ARM big.LITTLE))

§  Hybrid Memory Cube & near-‐memory processing §  No standard programming model

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accelerators

On Our Way to the Next Extinction? §  History repeats itself: –  CMOS technology has hit a power wall •  Clock speed is not raising

–  Alterna.ve materials are (too) expensive /not ready (gallium arsenide and other III-‐V materials; nanowires, nanotubes)

While power consump0on is an urgent challenge, its leakage or sta0c component will become a major industry crisis in the long term, threatening the survival of CMOS technology itself, just as bipolar technology was threatened and eventually disposed of decades ago (ITRS 2011)

§  History does not repeat itself: –  There is a much larger industrial base –  An alterna.ve “good enough” technology IS NOT ready –  There is much more code that needs to be rewriJen if new model is needed (>200MLOCs)

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Future

It is difficult to make predic.ons, especially about the future (Yogi Berra)

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The End of Moore’s Law is Coming

§  Moore’s Law: The number of transistors per chip doubles every two/three years

§  Stein’s Law: If something cannot go forever, it will stop

§  Ques.on is not whether but when will Moore’s Law stop

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The 7nm Wall

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ANL-‐LBNL-‐ORNL-‐PNNL

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(courtesy J. Aldun)

The End of the Road (?)

§  Quantum tunneling becomes a major obstacle as devices shrinks –  7-‐5nm feature size has long been predicted to be the lower limit for CMOS devices •  ITRS predicts 7.5nm will be reached in 2024

§  7.5nm ~ 30 atoms of silicon –  No much room for further miniaturiza0on, independent of technology!

–  Room for clock increase (new materials, quantum effect gates, cryogenic devices…)

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The Last Mile is the Most Expensive Mile

§  New technologies are needed –  New materials (e.g., III-‐V, germanium thin channels, nanowires, nanotubes

or graphene) –  New structures (e.g., 3D transistor structures) –  New packages (e.g., HMC, photonics) –  New lithography –  Control or tolerance of large variances (safety margins, resilience, aging)

§  New technologies are expensive –  NRE increases faster than profits – forces consolida.on –  Only two companies can sustain the investments needed to go below 22nm

(Intel and Samsung) [Heck, Kaza, Pinner] §  Less compe..on & larger investments = slower progress

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The Future Is Not What It Was

19 November 2013

ANL-‐LBNL-‐ORNL-‐PNNL

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(courtesy J. Aldun)

The Path of Least Resistance – Other than Moore

§  Industry goal is not increased performance; it is increased ROI. Industry will increasingly invest in alterna.ves as increasing performance becomes more expensive –  Low power, low cost –  New markets: MEMS, sensors –  System on a chip (smartphone, tablet) ✗  Fewer good commodity building blocks for HPC –  No low-‐power/high-‐flops/high-‐resilience CPU ✔ More opportuni.es for semi-‐custom and integra.on of mul.ple vendor IP on a chip

§  New business model for supercompu.ng? –  Semi-‐custom & system on a chip integrator

Exascale

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Identified Issues

§  Scale (billion threads) §  Power (10’s of MWaJs) –  Communica<on: > 99% of power is consumed by moving operands across the memory hierarchy and across nodes

–  Reduced memory size: (communica.on in .me) §  Resilience: Something fails every hour; the machine is never

“whole” –  Trade-‐off between power and resilience

§  Asynchrony: Equal work ≠ equal .me –  Power management –  Error recovery

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My Main Concerns

§  Uncertainly about underlying HW architecture –  Slower progress of IC will necessitate faster progress of architecture –  May not converge to a new, stable model –  It is not about por.ng applica.ons to a new programming model – it is about designing applica.ons for portability

§  Increased soFware complexity –  Simula.ons of complex systems + uncertainty quan.fica.on + op.miza.on…

–  Support of complex workflows (e.g., in situ analysis) –  SoLware management of power and failures –  Heterogeneity –  Scale and .ght coupling (tail of distribu.on maJers!) –  Hypothesis: soLware will con.nue to be dominant cause of failures

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Conclusion

§  Moore’s Law is slowing down; the slow-‐down has many fundamental consequences – only a few of them explored in this talk

§  HPC is the “canary in the mine”: –  issues appear earlier because of size and .ght coupling

§  Op.mis.c view of the next decades: no stasis. –  A frenzy of innova.on to con.nue pushing current ecosystem, followed by frenzy of innova.on to use totally different compute technologies

§  Pessimis.c view: The end is coming

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Backup

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Do We Care?

§  It’s all about Big Data Now, simula.ons are passé. §  B***t §  All science is either physics or stamp collec0ng. (Ernest

Rutherford) –  In Physical Sciences, experiments and observa.ons exist to validate/refute/mo.vate theory. “Data Mining” not driven by a scien.fic hypothesis is “stamp collec.on”.

§  Simula.on is needed to go from a mathema.cal model to predic.ons on observa.ons. –  If system is complex (e.g., climate) then simula.on is expensive –  OLen, models are stochas.c and predic.ons are sta.s.cal – complica.ng both simula.on and data analysis

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Observation Meets Data: Cosmology Computation Meets Data: The Argonne View

Mapping the Sky with Survey Instruments

Observations: Statistical error bars will ‘disappear’ soon!

Emulator based on Gaussian Process Interpolation in High-

Dimensional Spaces

Supercomputer Simulation Campaign

Markov chain Monte Carlo

‘PrecisionOracle’

‘Cosmic Calibration’

LSST Weak Lensing

HACC+CCF (Domain science+CS+Math+Stats

+Machine learning)

CCF= Cosmic Calibration Framework

w = -1w = - 0.9

LSSTHACC=Hardware/Hybrid Accelerated Cosmology Code(s)

Wednesday, September 19, 12

(courtesy Salman Habib) Record-‐breaking applica.on: 3.6 Trillion par.cles, 14 Pflop/s

Keynote snir sc

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