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IceCube simulation with PPC on GPUs

IceCube simulation with PPC on GPUs. p hoton p ropagation c ode g raphics p rocessing u nit. Dmitry Chirkin, UW Madison. IceCube simulation with PPC. Photon propagation code PPC: 2009 - now. Photonics: 2000 – up to now. Photonics: conventional on CPU.

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IceCube simulation with PPC on GPUs

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  1. IceCube simulation with PPC on GPUs photon propagation code graphics processing unit Dmitry Chirkin, UW Madison

  2. IceCube simulation with PPC Photon propagation code PPC: 2009 - now Photonics: 2000 – up to now

  3. Photonics: conventional on CPU • First, run photonics to fill space with photons, tabulate the result • Create such tables for nominal light sources: cascade and uniform half-muon • Simulate photon propagation by looking up photon density in tabulated distributions • Table generation is slow • Simulation suffers from a wide range of binning artifacts • Simulation is also slow! (most time is spent loading the tables)

  4. Direct photon tracking with PPC photon propagation code • simulating flasher/standard candle photons • same code for muon/cascade simulation • using precise scattering function: linear combination of HG+SAM • using tabulated (in 10 m depth slices) layered ice structure • employing 6-parameter ice model to extrapolate in wavelength • tilt in the ice layer structure is properly taken into account • transparent folding of acceptance and efficiencies • precise tracking through layers of ice, no interpolation needed • precise simulation of the longitudinal development of cascades and • angular distribution of particles emitting Cherenkov photons

  5. Approximation to Mie scattering Simplified Liu: Henyey-Greenstein: fSL Mie: Describes scattering on acid, mineral, salt, and soot with concentrations and radii at SP

  6. Dependence on g=<cos(q)> and fSL g=<cos(q)> fSL 0.8 0 0.9 0 0.95 0 0.9 0.3 0.9 0.5 0.9 1.0 flashing 63-50  64-50

  7. Ice tilt in ppc Measured with dust loggers (Ryan Bay)

  8. Photon angular profile from thesis of Christopher Wiebusch

  9. PPC simulation on GPU graphics processing unit execution threads propagation steps (between scatterings) photon absorbed new photon created (taken from the pool) threads complete their execution (no more photons) Running on an NVidia GTX 295 CUDA-capable card, ppc is configured with: 448 threads in 30 blocks (total of 13440 threads) average of ~ 1024 photons per thread (total of 1.38 . 107 photons per call)

  10. Photon Propagation Code: PPC • There are 5 versions of the ppc: • original c++ • "fast" c++ • in Assembly • for CUDA GPU • icetray module All versions verified to produce identical results comparison with i3mcml http://icecube.wisc.edu/~dima/work/WISC/ppc/

  11. ppc icetray module • at http://code.icecube.wisc.edu/svn/projects/ppc/trunk/ • uses a wrapper: private/ppc/i3ppc.cxx, compiled by cmake system into the libppc.so • additional library libxppc.so is compiled by cmake • Set GPU_XPPC:BOOL=ON or OFF • or can also be compiled by running make in private/ppc/gpu: • “make glib” compiles gpu-accelerated version (needs cuda tools) • “make clib” compiles cpu version (from the same sources!)

  12. ppc example script run.py if(len(sys.argv)!=6): print "Use: run.py [corsika/nugen/flasher] [gpu] [seed] [infile/num of flasher events] [outfile]" sys.exit() … det = "ic86" detector = False … os.putenv("PPCTABLESDIR", expandvars("$I3_BUILD/ppc/resources/ice/mie")) … if(mode == "flasher"): … str=63 dom=20 nph=8.e9 tray.AddModule("I3PhotoFlash", "photoflash")(…) os.putenv("WFLA", "405") # flasher wavelength; set to 337 for standard candles os.putenv("FLDR", "-1") # direction of the first flasher LED … # Set FLDR=x+(n-1)*360, where 0<=x<360 and n>0 to simulate n LEDs in a # symmetrical n-fold pattern, with first LED centered in the direction x. # Negative or unset FLDR simulates a symmetric in azimuth pattern of light. tray.AddModule("i3ppc", "ppc")( ("gpu", gpu), ("bad", bad), ("nph", nph*0.1315/25), # corrected for efficiency and DOM oversize factor; eff(337)=0.0354 ("fla", OMKey(str, dom)), # set str=-str for tilted flashers, str=0 and dom=1,2 for SC1 and 2 ) else:

  13. ppc-pick and ppc-eff • ppc-pick: restrict to primaries below MaxEpri • load("libppc-pick") • tray.AddModule("I3IcePickModule<I3EpriFilt>","emax")( • ("DiscardEvents", True), • ("MaxEpri", 1.e9*I3Units.GeV) • ) • ppc-eff: reduce efficiency from 1.0 to eff • load("libppc-eff") • tray.AddModule("AdjEff", "eff")( • ("eff", eff) • )

  14. ppc homepage http://icecube.wisc.edu/~dima/work/WISC/ppc

  15. GPU scaling Original: 1/2.08 1/2.70 CPU c++: 1.00 1.00 Assembly: 1.25 1.37 GTX 295: 147 157 GTX/Ori: 307 424 C1060: 104 112 C2050: 157 150 GTX 480: 210 204 Uses cudaGetDeviceProperties() to get the number of multiprocessors, Uses cudaFuncGetAttributes() to get the maximum number of threads On GTX 295: 1.296 GHz Running on 30 MPs x 448 threads Kernel uses: l=0 r=35 s=8176 c=62400 On GTX 480: 1.401 GHz Running on 15 MPs x 768 threads Kernel uses: l=0 r=40 s=3960 c=62400 On C1060: 1.296 GHz Running on 30 MPs x 448 threads Kernel uses: l=0 r=35 s=3992 c=62400 On C2050: 1.147 GHz Running on 14 MPs x 768 threads Kernel uses: l=0 r=41 s=3960 c=62400

  16. GTX 480 vs. GTX 295 • GTX 480 has 1 GPU • 480 MPs in 15 cores • 32 MPs per core • 4 single-precision sFPUs • 60 sFPUs per GPU • 480 cores per card • 60 sFPUs per card (!) shared memory • up to 48Kb of per core • up to 720 Kb per card • GTX 295 has 2 GPUs • 240 MPs in 30 cores • 8 MPs per core • 2 single-precision sFPUs • 60 sFPUs per GPU • 480 cores per card • 120 sFPUs per card Shared memory: • 16Kb per core • 960 Kb per card Why is ppc not a factor 2 faster on GTX 480 GPU than on GTX 295 GPU?

  17. Kernel time calculation Run 3232 (corsika) IC86 processing on cuda002 (per file): GTX 295: Device time: 1123741.1 (in-kernel: 1115487.9...1122539.1) [ms] GTX 480: Device time: 693447.8 (in-kernel: 691775.9...693586.2) [ms] If more than 1 thread is running using same GPU: Device time: 1417203.1 (in-kernel: 1072643.6...1079405.0) [ms] 3 counters: 1. time difference before/after kernel launch in host code 2. in-kernel, using cycle counter: min thread time 3. max thread time Also, real/user/sys times of top: gpus 6 cpus 1 cores 8 files 693 Real 749m4.693s User 3456m10.888s sys 39m50.369s Device time: 245312940.1 216887330.9 218253017.2 [ms] files: 693 real: 64.8553 user: 37.8357 gpu: 58.9978 kernel: 52.4899 [seconds] 81%-91% GPU utilization

  18. Concurrent execution time Thread 1: CPU GPU CPU GPU Thread 2: GPU CPU GPU CPU Create track segments CPU CPU CPU CPU One thread: GPU GPU GPU GPU Process photon hits Copy photon hits from GPU However: have 2 buffers: 1 on host and 1 on GPU! Just need to synchronize before the buffers are re-used Need 2 buffers for track segments and photon hits Copy track segments to GPU

  19. Typical run times • corsika: run 3232: 10493 10.0345 sec files • ic86/spx/3232 on cuda00[123] (53.4 seconds per job) • 1.2 days of real detector time in 6.5 days • nugen: run 2972: 9993 200000-event files; E^-2 weighted • ic86/spx/2972 on cudatest (25.1 seconds per job) • entire 10k set of files in 2.9 days  this is enough for an atmnu/diffuse analysis! • Considerations: • Maximize GPU utilization by running only mmc+ppc parts on the GPU nodes • still, IC40 mmc+ppc+detector was run with ~80% GPU utilization • run with 100% DOM efficiency, save all ppc events with at least 1 MC hit • apply a range of allowed efficiencies (70-100%) later with ppc-eff module

  20. Use in analysis PPC run on GPUs was already used in several analyses already published or in progress. The ease of changing the ice parameters facilitated propagation of ice uncertainties through the analysis, as all “systematics” simulation sets are simulated in roughly the same amount of time, with no extra overhead. A similar quality uncertainty analysis based on photonics simulation would have taken much longer because of the large CPU cost of the initial table generation.

  21. DAG (Directed Acyclical Graph) -based simulation • Separate simulation segments into tasks • Assign task to a node in DAG

  22. GPU-based simulation • We have recently began experimenting with GPU-based implementation of portions of IceCube simulation. • DAG assigns separate tasks to different compute nodes • Execution of photon propagation simulation on dedicated GPU nodes. • For many simulations GPU segment of chain is much faster than the rest of the simulation. • Small number GPU-enabled machines can consume the data from large pool lf CPU cores. generator generator generator generator Dedicated GPU cluster PPC Detector Detector

  23. GPU-based simulation • Optimal DAG differ depending on the specific simulation

  24. Current Status of GPU-based Simulation Production • NuGen and CORSIKA simulation currently running on Madison cluster: NPX3+CUDA • Testing optimal DAG configurations to take advantage of GPUs • Current Condor queue has option for selecting machines with GPUs • There are multiple cores and multiple GPUs on each machine • Condor assigns environment variable ${_CONDOR_SLOT}which is used as parameter to select a GPU on PPC. • SGE, PBS IceProd plugins being writtend to support DAGs in order to incorporate other non-Condor sites: • NERSC Dirac and Tesla and clusters in Dortmund, DESY, Alberta • Other IceCube sites report to have GPUs available and will be incorporated into production.

  25. GPU/PPC production: coincident neutrino-CR muons

  26. Our initial GPU cluster • 4 computers: • 1 cudatest • 3 cuda nodes (cuda001-3) • Each has • 4-core CPU • 3 GPU cards, each with 2 GPUs (i.e. 6 GPUs per computer) • Each computer is ~ $3500

  27. Our initial cluster nvidia-smi -lsa GPU 0: Product Name : GeForce GTX 295 Serial : 1803836293359 PCI ID : 5eb10de Temperature : 87 C GPU 1: Product Name : GeForce GTX 295 Serial : 2497590956570 PCI ID : 5eb10de Temperature : 90 C GPU 2: Product Name : GeForce GTX 295 Serial : 1247671583504 PCI ID : 5eb10de Temperature : 100 C GPU 3: Product Name : GeForce GTX 295 Serial : 2353575330598 PCI ID : 5eb10de Temperature : 105 C GPU 4: Product Name : GeForce GTX 295 Serial : 1939228426794 PCI ID : 5eb10de Temperature : 100 C GPU 5: Product Name : GeForce GTX 295 Serial : 2347233542940 PCI ID : 5eb10de Temperature : 103 C cudatest: Found 6 devices, driver 2030, runtime 2030 0(1.3): GeForce GTX 295 1.296 GHz G(939261952) S(16384) C(65536) R(16384) W(32) l1 o1 c0 h1 i0 m30 a256 M(262144) T(512: 512,512,64) G(65535,65535,1) 1(1.3): GeForce GTX 295 1.296 GHz G(939261952) S(16384) C(65536) R(16384) W(32) l0 o1 c0 h1 i0 m30 a256 M(262144) T(512: 512,512,64) G(65535,65535,1) 2(1.3): GeForce GTX 295 1.296 GHz G(939261952) S(16384) C(65536) R(16384) W(32) l0 o1 c0 h1 i0 m30 a256 M(262144) T(512: 512,512,64) G(65535,65535,1) 3(1.3): GeForce GTX 295 1.296 GHz G(938803200) S(16384) C(65536) R(16384) W(32) l0 o1 c0 h1 i0 m30 a256 M(262144) T(512: 512,512,64) G(65535,65535,1) 4(1.3): GeForce GTX 295 1.296 GHz G(939261952) S(16384) C(65536) R(16384) W(32) l0 o1 c0 h1 i0 m30 a256 M(262144) T(512: 512,512,64) G(65535,65535,1) 5(1.3): GeForce GTX 295 1.296 GHz G(939261952) S(16384) C(65536) R(16384) W(32) l0 o1 c0 h1 i0 m30 a256 M(262144) T(512: 512,512,64) G(65535,65535,1) 2 and 3 4 and 5 0 and 1 PSU 3 GTX 295 cards, each with 2 GPUs

  28. BAD multiprocessors (MPs) clist cudatest 0 1 2 3 4 5 cuda001 0 1 2 3 4 5 cuda002 0 1 2 3 4 5 cuda003 0 1 2 3 4 5 #badmps cuda001 3 22 cuda002 2 20 cuda002 4 10 Configured: xR=5 eff=0.95 sf=0.2 g=0.943 Loaded 12 angsens coefficients Loaded 6x170 dust layer points Loaded 16028 random multipliers Loaded 42 wavelenth points Loaded 171 ice layers Loaded 3540 DOMs (19x19) Processing f2k muons from stdin on device 2 Total GPU memory usage: 83053520 photons: 13762560 hits: 991 Error: TOT was a nan or an inf 1 times! Bad MP #20 photons: 13762560 hits: 393 photons: 13762560 hits: 570 photons: 13762560 hits: 501 photons: 13762560 hits: 832 photons: 13762560 hits: 717 CUDA Error: unspecified launch failure [dima@cuda002 gpu]$ cat mmc.1.f2k | BADMP=20 ./ppc 2 > /dev/null Configured: xR=5 eff=0.95 sf=0.2 g=0.943 Loaded 12 angsens coefficients Loaded 6x170 dust layer points Loaded 16028 random multipliers Loaded 42 wavelenth points Loaded 171 ice layers Loaded 3540 DOMs (19x19) Processing f2k muons from stdin on device 2 Not using MP #20 Total GPU memory usage: 83053520 photons: 13762560 hits: 871 … photons: 1813560 hits: 114 Device time: 31970.7 (in-kernel: 31725.6...31954.8) [ms] Total GPU memory usage: 83053520 photons: 13762560 hits: 938 Error: TOT was a nan or an inf 9 times! Bad MP #20 #20 #20 #20 photons: 13762560 hits: 442 photons: 13762560 hits: 627 CUDA Error: unspecified launch failure Disable 3 bad GPUs out of 24: 12.5% Disable 3 bad MPs out of 720: 0.4%! Failure rates:

  29. The IceWave Cluster • 3 x DELL PowerEdge C410x • 48 Tesla M2070 GPGPU • 21504 GPU cores • 48 TFlops single precision • 24 TFlops double precision • 6 x DELL PowerEdge C6145 • 24 AMD Opteron™ 6100 Processors • 288 CPU cores • 7 TFlops double precision • QDR Infiniband Interconnect • Allows high speed MPI applications • ~ $ 200,000

  30. Basic Elements • DELL PowerEdge C410x • 16 PCIe Expansion chassis • Use of C2050 TESLA GPUs () • Flexible assignment of GPUs toServersAllows 1-4 GPUs per server

  31. Basic Elements • DELL PowerEdge C6145 • 2 x 4 CPU Servers • AMD Opteron™ 6100 (Magny-Cours) • 12 cores per processor • 48-96 cores per 2U server • 192 GB per 2U server

  32. Concluding remarks • PPC (photon propagation code) is used by IceCube for photon tracking • Precise treatment of the photon propagation, includes all known effects (longitudinal development of particle cascades, ice tilt, etc.) • PPC can be run on CPUs or GPUs; running on a GPU is 100s of times faster than on a CPU core • We use DAG to run PPC routinely on GPUs for mass production of simulated data • GPU computers can be assembled with NVidia or AMD video cards •  however, some problems exist in consumer video cards •  bad MPs can be worked around in PPC •  computing-grade hardware can be used instead

  33. Extra slides

  34. Oversize DOM treatment • Oversized DOM treatment (designed for minimum bias compared to oversize=1): • oversize only in direction perpendicular to the photon •  time needed to reach the nominal (non-oversized) DOM surface is added • re-use the photon after it hits a DOM and ensure the causality in the flasher simulation • The oversize model was chosen carefully to produce the best possible agreement with the nominal x1 case (see next slide). nominal DOM oversized DOM oversized ~ 5 times photon This is a crucial optimization, however: Some bias is unavoidable since DOMs occupy larger space: x1: diameter of 33 cm x5: 1.65 m x16: 5.3 m This could lead to ~5-10% variation of the individual DOM simulated rates.

  35. Timing of oversized DOM MC xR=1 default  64-48 Flashing 63-50  63-48 xR=1 default do not track back to detected DOM do not track after detection no ovesize delta correction! do not check causality del=(sqrtf(b*b+(1/(e.zR*e.zR-1)*c)-D)*e.zR-h del=e.R-OMR  64-52

  36. ice density: 0.9216 mwe T=221.5-0.00045319*d+5.822e-6*d2-273.15 (fit to AMANDA data) handbook of chemistry and physics T.Gow's data of density near the surface Fit to (1-p1*exp(-p2*d))*f(T(d))*(1+0.94e-12*9.8*917*d)

  37. Device enumeration nvidia-smi -a GPU 0: Product Name : GeForce GTX 295 PCI ID : 5eb10de Temperature : 68 C GPU 1: Product Name : GeForce GTX 295 PCI ID : 5eb10de Temperature : 73 C GPU 2: Product Name : GeForce GTX 480 PCI ID : 6c010de Temperature : 106 C GPU 3: Product Name : GeForce GTX 295 PCI ID : 5eb10de Temperature : 90 C GPU 4: Product Name : GeForce GTX 295 PCI ID : 5eb10de Temperature : 91 C cuda002: Found 5 devices, driver 3010, runtime 3010 0(2.0): GeForce GTX 480 1.401 GHz G(1610285056) S(49152) C(65536) R(32768) W(32) l0 o1 c0 h1 i0 m15 a512 M(2147483647) T(1024: 1024,1024,64) G(65535,65535,1) 1(1.3): GeForce GTX 295 1.242 GHz G(938803200) S(16384) C(65536) R(16384) W(32) l0 o1 c0 h1 i0 m30 a256 M(2147483647) T(512: 512,512,64) G(65535,65535,1) 2(1.3): GeForce GTX 295 1.242 GHz G(939327488) S(16384) C(65536) R(16384) W(32) l0 o1 c0 h1 i0 m30 a256 M(2147483647) T(512: 512,512,64) G(65535,65535,1) 3(1.3): GeForce GTX 295 1.242 GHz G(939327488) S(16384) C(65536) R(16384) W(32) l0 o1 c0 h1 i0 m30 a256 M(2147483647) T(512: 512,512,64) G(65535,65535,1) 4(1.3): GeForce GTX 295 1.242 GHz G(939327488) S(16384) C(65536) R(16384) W(32) l0 o1 c0 h1 i0 m30 a256 M(2147483647) T(512: 512,512,64) G(65535,65535,1) 0 2 3 and 4 3 and 4 1 and 2 0 and 1 PSU 2 GTX 295 cards, 1 GTX 480 card

  38. Fermi vs. Tesla Flasher f2kmuon 35587.8 13797.0 34246.7 10990.8 40989.2 13563.2 40423.2 11514.9 29114.5 11361.4 27343.8 8760.0 27346.6 8755.5 29024.8 11429.2 27631.5 8833.2 27630.9 8833.3 28950.1 9079.5 28955.1 9073.2 cudatest: Found 6 devices, driver 2030, runtime 2030 0(1.3): GeForce GTX 295 1.296 GHz G(939261952) S(16384) C(65536) R(16384) W(32) l1 o1 c0 h1 i0 m30 a256 M(262144) T(512: 512,512,64) G(65535,65535,1) tesla: Found 1 devices, driver 3000, runtime 3000 0(1.3): Tesla C1060 1.296 GHz G(4294770688) S(16384) C(65536) R(16384) W(32) l1 o1 c0 h1 i0 m30 a256 M(2147483647) T(512: 512,512,64) G(65535,65535,1) fermi: Found 1 devices, driver 3000, runtime 3000 0(2.0): Tesla C2050 1.147 GHz G(2817982464) S(49152) C(65536) R(32768) W(32) l1 o1 c0 h1 i0 m14 a512 M(2147483647) T(1024: 1024,1024,64) G(65535,65535,1) beta: Found 1 devices, driver 3010, runtime 3010 0(2.0): Tesla C2050 1.147 GHz G(2817982464) S(49152) C(65536) R(32768) W(32) l1 o1 c0 h1 i0 m14 a512 M(2147483647) T(1024: 1024,1024,64) G(65535,65535,1) 11: arch=11 make gpu 12: arch=12 make gpu (default/best) 1x: arch=12 make gpu with -ftz=true -prec-div=false -prec-sqrt=false 20: arch=20 make gpu 2x: arch=20 make gpu with -ftz=true -prec-div=false -prec-sqrt=false

  39. Other • Consider: • building production computers with only 2 cards, leaving a space in between • using 6-core CPUs if paired with 3 GPU cards • 4-way Tesla GPU-only servers a possible solution • Consumer GTX card much faster than Tesla/Fermi cards • GTX 295 was so far found to be a better choice than GTX 480 • but: no longer available! • Reliability: • 0.4% loss of advertised capacity in GTX 295 cards • however: 2 of 3 affected cards were “refurbished” • do cards deteriorate over time? The failed MPs did not change in ~3 months

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