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Use of GPUs in ALICE (and elsewhere) Thorsten Kollegger

Use of GPUs in ALICE (and elsewhere) Thorsten Kollegger. TDOC-PG | CERN | 17.07.2013. GPUs for General Purpose Computing. In the last 5+ years, increased usage of GPUs (or more general accelerator cards) in High Performance Computing Systems. Top 500 list - 2013. NVIDIA. AMD.

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Use of GPUs in ALICE (and elsewhere) Thorsten Kollegger

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  1. Use of GPUs in ALICE (and elsewhere)Thorsten Kollegger TDOC-PG | CERN | 17.07.2013

  2. GPUs for General Purpose Computing In the last 5+ years, increased usage of GPUs (or more generalaccelerator cards) in High Performance Computing Systems Top 500 list - 2013 NVIDIA AMD Intel Use of GPUs in ALICE and elsewhere | Thorsten Kollegger | ECFA HL-LHC TDOC-PG | 17.07.2013

  3. GPUs for General Purpose Computing Driven by (theoretical) peak performance GPU: O(1) TFLOP/s (NVIDIA TESLA K20: 3.2 TFLOP/s) CPU: O(0.1) TFLOP/s (Intel Xeon E5-2690 : 243 GFLOP/s) Can this theoretical peak performance be used efficiently for the typical HEP workload? Use of GPUs in ALICE and elsewhere | Thorsten Kollegger | ECFA HL-LHC TDOC-PG | 17.07.2013

  4. GPGPU Processing Model Few complex CPU cores vsmany simple GPU cores Programming Languages: CUDA,OpenCL OpenACC, OpenMP, OpenHMPP, TBB, MPI • Pre-Conditions for effective GPU speed-up of applications • Computationally intensive — Time needed for computing much larger then time need for data transfer to GPU • Massively parallel — Hundreds of independent computing tasks Use of GPUs in ALICE and elsewhere | Thorsten Kollegger | ECFA HL-LHC TDOC-PG | 17.07.2013

  5. What to expect? Typical success stories of GPGPU usage report >x100 speedup However: The expected speedup is strongly depending on workloads. Comparing optimized multi-core CPU versions with optimized GPU versions for most workloads speedup’s of ~5 are measured Use of GPUs in ALICE and elsewhere | Thorsten Kollegger | ECFA HL-LHC TDOC-PG | 17.07.2013

  6. GPGPUs in HEP Lots of R&D activities in the experiments ongoing Mostly focused on Trigger or High-Level-Trigger systems,HW decisions easier than in heterogeneous GRID systems R&D projects I know of, for sure incomplete: • ALICE, ATLAS, CMS, LHCb @ LHC, CERN • NA62 @ SPS, CERN • CBM, PANDA @ FAIR, GSI, Germany • STAR @ RHIC, BNL, USA • GEANT 4 • … ALICE HLT is using GPUs in production since 2010/2011 Use of GPUs in ALICE and elsewhere | Thorsten Kollegger | ECFA HL-LHC TDOC-PG | 17.07.2013

  7. ALICE HLT • Input data rate: ~1 kHz, 20 GByte/s • Event size ranging from <1 MByte (p+p) • to 80 MByte (centralPb+Pb) • Full online reconstructionincludingtrackingof TPC+IST • (intermediate) resultsreplacerawdatatolimitstoragespace • Compute nodes (CN/CNGPU) • Full event reconstruction • 32+32 nodes with NVIDIA GTX 480/580 GTX580 newly installed in 2011 Use of GPUs in ALICE and elsewhere | Thorsten Kollegger | ECFA HL-LHC TDOC-PG | 17.07.2013

  8. ALICE HLT TPC Tracker TPC tracking algorithm based on Cellular Automaton approach Optimized for multi-core CPUs tofulfill latency requirements 2009 ported to CUDA for use on NVIDIA GTX285 consumer cards, changed to use single precision 2010 ported to GTX480 2011 added GTX580, fully commisioned Use of GPUs in ALICE and elsewhere | Thorsten Kollegger | ECFA HL-LHC TDOC-PG | 17.07.2013

  9. ALICE HLT TPC Tracker Speedup 4-fold Speedup compared to optimized CPU version Note: frees CPUs on CN for other operations (tagging/trigger) Use of GPUs in ALICE and elsewhere | Thorsten Kollegger | ECFA HL-LHC TDOC-PG | 17.07.2013

  10. ALICE HLT GPU Experience Experience quite promising, will continue/expand in Run 2 Allowed to reduce system size by factor 3 Stable operation even with consumer hardware Comes with some cost Initial porting to CUDA, change to SP: 1.5 PhD students/1 year Every new GPU generation requires re-tuning (even same chip) Need to support two versions (CPU for simulation, GPU) Full loading of GPU requires quite some effort: currently at 67% Use of GPUs in ALICE and elsewhere | Thorsten Kollegger | ECFA HL-LHC TDOC-PG | 17.07.2013

  11. GPUsintheNA62TDAQsystem TheuseoftheGPUatthesoftware levels(L1/2)is“straightforward”:put thevideocardinthePC. Noparticularchangestothehardware areneeded Themainadvantagesistoexploitthe powerofGPUstoreducethenumber of PCs intheL1 farms GPU GPU L1 PC L2 PC 1MHz 100kHz RO board L0TP L1TP GPU TheuseofGPUatL0ismore challenging: Fixedand smalllatency (dimension oftheL0 buffers) Deterministicbehavior (synchronoustrigger) Veryfastalgorithms(highrate) RO board L0 GPU L0TP 10 MHz 10 MHz 1MHz Max1mslatency Slide from GianlucaLamann(CERN) Use of GPUs in ALICE and elsewhere | Thorsten Kollegger | ECFA HL-LHC TDOC-PG | 17.07.2013 11

  12. Some recent trends Direct transfer of data from e.g. network to GPU w/o involving CPU (AMD: DirectGMA, NVIDIA: GPU Direct 2) APUs: Integrate GPU with CPUs on a chip NVIDIA Tegra: ARM+GPU AMD Fusion: x86+GPU Graphicscard SDIInput/Outputcard SDIout GPU FPGA SDIin PCIe bus Peer-to-peertransfers(DirectGMA) CPU Memory CPU Use of GPUs in ALICE and elsewhere | Thorsten Kollegger | ECFA HL-LHC TDOC-PG | 17.07.2013

  13. Where we are… GPGPUs can provide a significant benefit today mainly for tightly-controlled systems, e.g. Trigger & HLT- reduced infrastructure cost <-> development cost main issue is programming complexity & maintenance- will there be a common programming language/library? avoid vendor lock-in…- do we need the ultimate performance? Highly-parallel programming model will be also relevant for effective use of future many-core CPUs GPUs evolving more and more into independent compute units Use of GPUs in ALICE and elsewhere | Thorsten Kollegger | ECFA HL-LHC TDOC-PG | 17.07.2013

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