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Using A100 MIG to Scale Astronomy Scientific Output
The document discusses how Nvidia's A100 GPU with Multi-Instance GPU (MIG) capability can help scale up scientific output for astronomy projects like IceCube and LIGO. The A100 is much faster than previous GPUs, but MIG allows it to be partitioned so multiple jobs or processes can leverage the GPU simultaneously. This results in 200-600% higher throughput compared to using a single GPU, by better utilizing the massive parallelism of the A100. MIG makes the powerful A100 GPU practical for these CPU-bound scientific workloads.
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Using A100 MIG to Scale Astronomy Scientific Output
- 1. Using A100 MIGto Scale Astronomy Scientific Output Presented by Igor Sfiligoi – UC San Diego Prepared in collaboration with David Schultz, Benedikt Riedel, James A. Clark and Frank Würthwein April 2021
- 2. Image Credit: NASA'sGoddard Space Flight Center The night sky may look peaceful. But there are many really violent events going on! This artist's rendering shows the tidal disruption event named ASASSN-14li, where a star wandering too close to a 3-million-solar-mass black hole was torn apart. The debris gathered into an accretion disk around the black hole.
- 3. The most violentevents cannot be seen with “traditional” methods! Multi-messenger astronomy
- 4. High energy neutrinos cantravel long distances in straight line. Produce Cherenkov light when passing through ice.
- 5. Really big eventscan distort space-time creating gravitational waves. Can be detected with laser interferometry. Image credit: LIGO/T. Pyle This illustration shows the merger of two black holes and the gravitational waves that ripple outward as the black holes spiral toward each other. LIGO Livingston, Louisiana 4km 4km
- 6. Both detectors requireextensive compute power to properly reconstruct the observed events.
- 7. Optical Properties • Combiningall the possible information • These features are included in simulation • We’re always be developing them Nature never tell us a perfect answer but obtained a satisfactory agreement with data! The need for calibration • Natural medium • Hard to calibrate properly • Dropped a detector into a grey box • The ice is very clear, but… • Is it uniform? • How has construction changed the ice? • Drastic changes in reconstructed position with different ice models
- 8. GPUs great fitfor calibration purposes • Too complex for a parametrized approach • Needs brute force approach • Basically, Ray-Tracing • Starting from MC produced events • Switched to GPU Ray-Tracing in 2011 • At that point 1x GPU ~ 200x CPU cores
- 9. Really excited aboutthe A100 • Great speed growth in core IceCube compute benchmark 0 100 200 300 400 500 T4 P100 P40 V100 A100 Mphotons/s V100 = 1.7x T4 P100 = 1.1x T4 A100 = 3.3x T4 A100 = 2.9x P100 A100 = 1.9x V100 (Image credit: Nvidia)
- 10. 0 20 40 60 80 100 120 140 0 100 200 300 400 500 T4 P100 P40V100 A100 But actual application speed improvement much lower • IceCube CPU-based code cannot keep GPU busy! A100 = 1.9x T4 A100 = 1.4x V100 Benchmark Mphotons/s Application jobs/day With oversize=3
- 11. 0 20 40 60 80 100 120 140 0 50 100 150 200 250 300 350 400 450 T4 P100 P40V100 A100 2x A100 MIG 3x A100 MIG 7x A100 MIG A100 MIG (Multi-Instance GPU) to the rescue • With 7x MIG partitions we are back to theoretical speedup A100 = 3.5x T4 A100 = 2.5x V100 Benchmark Mphotons/s Application jobs/day Actually… Event better With oversize=3
- 12. 0 20 40 60 80 100 120 140 0 50 100 150 200 250 300 350 400 450 T4 P100 P40V100 A100 2x A100 MIG 3x A100 MIG 7x A100 MIG A100 MIG (Multi-Instance GPU) to the rescue • With 7x MIG partitions we are back to theoretical speedup A100 = 3.5x T4 A100 = 2.5x V100 Benchmark Mphotons/s Application jobs/day Actually… Event better We did try to share a single GPU between multiple jobs, but no real advantage observed Each job believes it has a full GPU to itself. A slower GPU, but we mostly care about throughput.
- 13. 0 50 100 150 200 250 300 0 50 100 150 200 250 300 350 400 450 500 T4 V100 A1002x A100 MIG 3x A100 MIG 7x A100 MIG A100 MIG (Multi-Instance GPU) to the rescue • And the story get better for faster codes! A100 = 4.3x T4 A100 = 4.3x V100 Benchmark Mphotons/s Application jobs/day With oversize=5 V100 was already too fast for IceCube in fast mode 2x A100 MIG runtime identical to no_MIG A100 runtime! 2x HTC (When precision less important)
- 14. 0 20 40 60 80 100 120 140 0 50 100 150 200 250 300 350 400 450 T4 P100 P40V100 A100 2x A100 MIG 3x A100 MIG 7x A100 MIG A100 MIG (Multi-Instance GPU) to the rescue Benchmark Mphotons/s Application jobs/day Actually… Event better We did try to share a single GPU between multiple jobs, but no real advantage observed Each job believes it has a full GPU to itself. A slower GPU, but we mostly care about throughput. https://docs.nvidia.com/datacenter/tesla/mig-user-guide/index.html Relatively easy to setup But requires a reboot to be enabled
- 15. A few wordsabout LIGO
- 16. Gravitational wave parameterestimation • Gravitational wave data: • Very noisy time series of strain measurements • Sources like binary black hole coalescence induce a well-modelled signal • Infer source parameters (masses, spins, etc) by solving inverse problem: • matching waveform model “templates” to the data PRL 118, 221101 (2017) Computationally expensive • Computing posterior probability density functions • 1k -1M likelihood evaluations
- 17. RIFT = Rapidparameter inference for gravitational wave sources via Iterative FiTting • Highly parallelizable, grid-based algorithm: • Start with discrete grid of waveforms • Monte-Carlo integrate likelihood over extrinsic, “nuisance” parameters (e.g., distance, sky-location) • Gaussian process interpolation → approximate continuous likelihood function • Interpolation ported to GPUs using cupy • 1x Nvidia Quadro P2000 GPU ~ 20x Intel Xeon Silver 4116 CPU cores • Requires MC input, which is currently CPU-only
- 18. LIGO still inthe process to transition • All GPUs too fast! • Workflow heavily single-CPU-core-bound Image Credit: T. Pyle/Caltech/MIT/LIGO Lab 0 20 40 60 80 100 120 140 GTX1080TI V100 A100 jobs/day A100 = 1.3x GTX1080TI A100 ~ V100 Code crushing on Turing platforms. GTX1080TI ~ T4 Coming soon, but not yet ready: GPU-based Monte-Carlo integration RIFT
- 19. Multi-process the wayto go • All GPUs still too fast! • Linear scaling from 1 to 3 processes per GPU Image Credit: T. Pyle/Caltech/MIT/LIGO Lab A100 = 1.3x GTX1080TI A100 ~ V100 0 100 200 300 400 GTX1080TI V100 A100 jobs/day 3 processes/GPU 1 process/GPU 3x RIFT
- 20. Multi-process the wayto go • All GPUs still too fast! • Linear scaling from 1 to 3 processes per GPU Image Credit: T. Pyle/Caltech/MIT/LIGO Lab A100 = 1.3x GTX1080TI A100 ~ V100 0 100 200 300 400 GTX1080TI V100 A100 jobs/day 3 processes/GPU 1 process/GPU 3x No reason to stop at 3 processes/GPU. But makes scheduling harder. RIFT
- 21. 0 500 1000 1500 2000 2500 GTX1080TI V100 A1003x A100 MIG 7x A100 MIG jobs/day A100 MIG helps pack more processes • A100 7xMIG still fast enough to feed 3 processes • Each MIG partition can be scheduled independently 3 processes/GPU 1 process/GPU Image Credit: SXS, the Simulating eXtreme Spacetimes 7x more throughput with marginal per-job slowdown RIFT
- 22.
- 23. MIG makes A100a great GPU • The A100 GPU is fast • Too fast for IceCube and LIGO to fully use the whole GPU! • Splitting the GPU with MIG makes it much easier to use • Required for IceCube • Convenient for LIGO • Enables 200% - 600% throughput on identical HW! 0% 100% 200% 300% 400% 500% 600% V100 A100 3x A100 MIG 7x A100 MIG jobs/day IceCube Regular IceCube Fast LIGO (3x)
- 24. Acknowledgments This work waspartially funded by the US National Science Foundation (NSF) though grants OAC-1941481, OAC-1841530, OAC-1826967 and OPP-1600823.