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生命科学、气象行业 高性能计算解决方案及成功案例分享

生命科学、气象行业 高性能计算解决方案及成功案例分享. 凌巍才 高性能计算产品技术顾问 戴尔（中国）有限公司. 内容. 生命科学高性能计算解决方案 GPU 加速解决方案 高性能存储解决方案 WRF V3.3 ( 气象行业应用 ) 在 Dell R720 服务器 程序测试及优化 g cc 编译器器 Intel 编译器 成功案例分享. 生命科学 HPC GPU 方案. 在生命科学领域中 很多用户采用 GPU 加速解决方案. CPU + GPU 计算. HPCC GPU 异构平台.

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生命科学、气象行业 高性能计算解决方案及成功案例分享

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  1. 生命科学、气象行业高性能计算解决方案及成功案例分享生命科学、气象行业高性能计算解决方案及成功案例分享 凌巍才 高性能计算产品技术顾问 戴尔（中国）有限公司 Confidential

  2. 内容 • 生命科学高性能计算解决方案 • GPU加速解决方案 • 高性能存储解决方案 • WRF V3.3 ( 气象行业应用) 在 Dell R720 服务器程序测试及优化 • gcc 编译器器 • Intel 编译器 • 成功案例分享 Confidential

  3. 生命科学HPC GPU 方案

  4. 在生命科学领域中很多用户采用GPU加速解决方案在生命科学领域中很多用户采用GPU加速解决方案 Confidential

  5. CPU + GPU 计算 Confidential

  6. HPCCGPU 异构平台 Confidential

  7. 支持GPU的 Dell 服务器方案(2012年,12代服务器) Internal Solutions External Solutions (PowerEdge C) Confidential

  8. GPU 扩展箱方案 (GPU外置方案)DellPowerEdge C410x PCIe EXPANSION CHASSIS CONNECTING 1-8 HOSTS TO 1-16 PCIe Great for: HPC including universities, oil & gas, biomed research, design, simulation, mapping, visualization, rendering, and gaming • 3U chassis, 19” wide, 143 pounds • PCI express modules: 10 front, 6 rear • PCI form factors: HH/HL and FH/HL • Up to 225W per module • PCIe inputs: 8PCIe x16 IPASS ports • PCI fan out options: x16 to 1 slot, x16 to 2 slot, x16 to 3 slot, x16 to 4 slot • GPUs supported: NVIDIA M1060, M2050, M2070 (TBD) • Thermals: high-efficiency 92mm fans; N + 1 fan redundancy • Management: On-board BMC; IPMI 2.0; dedicated management port • Power supplies: 4 x 1400W hot-plug, high efficiency PSUs; N+1 power redundancy • Services vary by region: IT Consulting, Server and Storage Deployment, Rack Integration (US only), Support Services Confidential

  9. PowerEdge C410x PCIe模块 • Serviceable PCIe module (taco) capable of supporting any half-height, half-length (HH/HL) or full-height/half-length (FH/HL) cards • FH/FL cards supported with extended PCIe module • Future-proofing on next generations of NVIDIA and AMD ATI GPU cards Power connector for GPGPU card LED Board-to-board connector for X16 Gen PCIe signals and power GPU card Confidential

  10. PowerEdge C410x Configurations • Enabling HPC applications to optimize cost / performance equation off single x16 1 GPU / x16 8GPU/7U 2 GPU / x16 16GPU/7U x16 x16 x16 x16 Host PCI Switch GPU Host PCI Switch GPU HIC HIC x16 C6100 C410x GPU C6100 C410x iPass cable 7U = (1) C410x + (2) C6100 iPass cable 7U = (1) C410x + (2) C6100 3 GPU / x16 12GPU/5U 4 GPU / x16 16GPU/5U x16 x16 x16 x16 Host PCI Switch GPU Host PCI Switch GPU HIC HIC x16 x16 C6100 GPU GPU C6100 x16 x16 iPass cable GPU GPU iPass cable C410x x16 GPU C410x 5U = (1) C410x + (1) C6100 5U = (1) C410x + (1) C6100 GPU/U ratios assume PowerEdge C6100 host with 4 servers per 2U chassis Confidential

  11. Flexibility of the PowerEdge C410x • Increases to 8:1 possible with dual x16 x16 GPU x16 GPU iPass cable x16 iPass cable x16 GPU GPU x16 x16 x16 x16 GPU PCI Switch PCI Switch GPU Host Host HIC HIC HIC HIC x16 x16 x16 x16 GPU PCI Switch PCI Switch GPU x16 x16 GPU GPU iPass cable iPass cable C410x x16 GPU x16 GPU C410x Confidential

  12. PowerEdge C6100 Configurations “2:1 Sandwich” Details • Two C6100 • 8 system boards • 2S Westmere, 12 DIMM slots, QDR IB, up to 6 drives per host • Single port x16 HIC (iPASS) • Single C410x • 16 GPUs (fully populated) • PCIe x8 per GPU • Total space = 7U C410x C6100 C6100 Summary C6100 “2:1 Sandwich” One Dell C410x (16 GPUs) Two C6100 (8 nodes) One x16 slot for each node to 2 GPUs 7U total 16 GPUs total 8 nodes total (2 GPUs per board) Note: This configuration is equivalent to using the C6100 and the NVIDIA S2050 but this configuration is more dense Confidential

  13. PowerEdge C6100 Configurations “4:1 Sandwich” Details C410x • One C6100 • 4 system boards • 2S Westmere, 12 DIMM slots, QDR IB, up to 6 drives per host • Single port x16 HIC (iPASS) • Single C410x • 16 GPUs (fully populated) • PCIe x4 per GPU • Total space = 5U C6100 Summary C6100 “4:1 Sandwich” One Dell C410x (16 GPUs) One C6100 (4 nodes) One x16 slot for each node to 4 GPUs 5U total 16 GPUs total 4 nodes total (4 GPUs per board) Confidential

  14. PowerEdge C6100 Configurations “8:1 Sandwich” (Possible Future Development) Details C410x • One C6100 • 4 system boards • 2S Westmere, 12 DIMM slots, QDR IB, up to 6 drives per host • Single port x16 HIC (iPASS) • Two C410x • 32 GPUs (fully populated) • PCIe x2 per GPU • Total space = 8U • See later table for metrics C410x C6100 Summary C6100 “8:1 Sandwich” Two Dell C410x (32 GPUs) One C6100 (4 nodes) One x16 slot for each node to 8 GPUs 8U total 32 GPUs total 4 nodes total (8 GPUs per board) Confidential

  15. PowerEdge C6145 Configurations “8:1 Sandwich” 5U of Rack Space Details • One C6145 • 2 system boards • 4S MagnyCours, 32 DIMM slots, QDR IB, up to 12 drives per host • 3 x Single port x16 HIC (iPASS) + 1 x Single port onboard x16 HIC (iPASS) • One C410x • 16 GPUs (fully populated) • PCIe x4-x8 per GPU • Total space = 5U C6145 C410x Details C6145 “16:1 Sandwich” One Dell C410x (16 GPUs) One C6145 (2 nodes) Two-Four HIC slots for each node to 16 GPUs 5U total 16GPUs total 2 nodes total (16 GPUs per board) Dell Confidential

  16. PowerEdge C6145 Configurations “16:1 Sandwich” 8U of Rack Space Details • One C6145 • 2 system boards • 4S MagnyCours, 32 DIMM slots, QDR IB, up to 12 drives per host • 3 x Single port x16 HIC (iPASS) + 1 x Single port onboard x16 HIC (iPASS) • Two C410x • 32 GPUs (fully populated) • PCIe x4 per GPU • Total space = 8U C410x C6145 C410x Details C6145 “16:1 Sandwich” Two Dell C410x (32 GPUs) One C6145 (2 nodes) Four HIC slots for each node to 16 GPUs 8U total 32 GPUs total 2 nodes total (16 GPUs per board) Dell Confidential

  17. PowerEdge C410x Block Diagram GPUs x 16 Switch Level 2 x 4 Switch Level 1 x 8 Host Connections x 8

  18. C410X BMC控制台配置界面

  19. GPU 扩展箱支持服务器列表 HIC/C410x Support Matrix • Dell external GPU solution support • Hardware Interface Card (HIC) in PCIe slot connects to external GPU(s) in C410x • Dell ‘slot validates’ NVIDIA interface cards to verify power, thermals, etc.

  20. 生命科学应用测试: GPU-HMMER 1.8X 2.7X 2.8X 2.9X Dell High Performance Computing

  21. GPU:Host Scaling : GPU-HMMER Speedup 1.8X 3.6X 7.2X 3.6X Dell High Performance Computing

  22. GPU:Host Scaling: NAMD Speedup 4.7X 8.2X 15.2X 9.5X Dell High Performance Computing

  23. GPU:Host Scaling : LAMMPS JL-Cut Speedup 8.5X 13.5X 14.4X 14.0X Dell High Performance Computing

  24. 生命科学存储方案

  25. 生命科学计算、数据容量增长率

  26. Clients Meta Data Server (MDS) OSS OSS OSS … The Lustre Parallel File System • Key Lustre Components: • Clients (compute nodes) • “Users” of the file system where applications run • The Dell HPC Cluster • Meta Data Server (MDS) • Holds meta-data information • Object Storage Server (OSS) • Provides back-end storage for the users’ files • Additional OSS units increase throughput linearly

  27. Confidential

  28. InfiniBand (IPoIB) NFS Performance: Sequential Read • Peaks: • NSS Small: 1 node doing IO (fairly level until 4 nodes) • NSS Medium: 4 nodes doing IO (not much drop-off) • NSS Large: 8 nodes doing IO (good performance over range)

  29. Infiniband (IPoIB) NFS Performance: Sequential Write • Peaks: • NSS Small: 1 node doing IO (steady drop off to 16 nodes) • NSS Medium: 2 nodes doing IO (good performance for up to 8 nodes) • NSS Large: 4 nodes doing IO (good performance over range)

  30. Confidential

  31. WRF V3.3 应用程序测试调优

  32. Dell 测试环境 • Dell R720 • cpu : 2x Intel Sandy Bridge E5- 2650, • Memory: 8x 8MB (64GB Memory) • Harddisk: 2x 300 GB 15Krpm (Raid 0) • BIOS Setting • disable HT • memory optimized • High Performance enable ( Power Max) • OS • Redhat Enterprise Linux 6.3 Confidential

  33. Gcc测试 • gcc, gfortran, gc++ • Zlib 1.2.5 • HDF5 1.8.8 • Netcdf 4 • WRF V3.3 Confidential

  34. 测试结果 • 输出文件 wrf : 2011年11月30日至 2011年12月5日 (13H9M53S) • wrf.exe starts at:Sun Apr 29 09:35:36 CST 2012… • wrf: SUCCESS COMPLETE WRF • wrf.exe completed at:Sun Apr 29 22:45:29 CST 2012 Confidential

  35. 配置文件 • # Settings for x86_64 Linux, gfortran compiler with gcc (smpar) • DMPARALLEL = 1 • OMPCPP = -D_OPENMP • OMP = -fopenmp • OMPCC = -fopenmp • SFC = gfortran • SCC = gcc • CCOMP = gcc • DM_FC = mpif90 -f90=$(SFC) • DM_CC = mpicc -cc=$(SCC) • FC = $(SFC) • CC = $(SCC) -DFSEEKO64_OK • LD = $(FC) • RWORDSIZE = $(NATIVE_RWORDSIZE) • PROMOTION = # -fdefault-real-8 # uncomment manually • ARCH_LOCAL = -DNONSTANDARD_SYSTEM_SUBR • CFLAGS_LOCAL = -w -O3 -c -DLANDREAD_STUB • LDFLAGS_LOCAL = • CPLUSPLUSLIB = • ESMF_LDFLAG = $(CPLUSPLUSLIB) • FCOPTIM = -O3 -ftree-vectorize -ftree-loop-linear -funroll-loops • FCREDUCEDOPT= $(FCOPTIM) • FCNOOPT= -O0 • FCDEBUG = # -g $(FCNOOPT) • FORMAT_FIXED = -ffixed-form • FORMAT_FREE = -ffree-form -ffree-line-length-none • FCSUFFIX = • BYTESWAPIO = -fconvert=big-endian -frecord-marker=4 • FCBASEOPTS_NO_G = -w $(FORMAT_FREE) $(BYTESWAPIO) • FCBASEOPTS = $(FCBASEOPTS_NO_G) $(FCDEBUG) • MODULE_SRCH_FLAG = • TRADFLAG = -traditional • CPP = /lib/cpp -C -P • AR = ar • ARFLAGS = ru • M4 = m4 -G • RANLIB = ranlib • CC_TOOLS = $(SCC) Confidential

  36. Wrf.out …. WRF NUMBER OF TILES FROM OMP_GET_MAX_THREADS = 16 WRF TILE 1 IS 1 IE 250 JS 1 JE 10 WRF TILE 2 IS 1 IE 250 JS 11 JE 20 WRF TILE 3 IS 1 IE 250 JS 21 JE 30 WRF TILE 4 IS 1 IE 250 JS 31 JE 39 WRF TILE 5 IS 1 IE 250 JS 40 JE 48 WRF TILE 6 IS 1 IE 250 JS 49 JE 57 WRF TILE 7 IS 1 IE 250 JS 58 JE 66 WRF TILE 8 IS 1 IE 250 JS 67 JE 75 WRF TILE 9 IS 1 IE 250 JS 76 JE 84 WRF TILE 10 IS 1 IE 250 JS 85 JE 93 WRF TILE 11 IS 1 IE 250 JS 94 JE 102 WRF TILE 12 IS 1 IE 250 JS 103 JE 111 WRF TILE 13 IS 1 IE 250 JS 112 JE 120 WRF TILE 14 IS 1 IE 250 JS 121 JE 130 WRF TILE 15 IS 1 IE 250 JS 131 JE 140 WRF TILE 16 IS 1 IE 250 JS 141 JE 150 WRF NUMBER OF TILES = 16 ….. Confidential

  37. 系统资源分析 CPU • CPU: (mpstat –P ALL) • Linux 2.6.32-257.el6.x86_64 (r720)      04/29/2012      _x86_64_        (16 CPU) • 04:06:40 PM  CPU    %usr   %nice    %sys %iowait    %irq   %soft  %steal  %guest   %idle • 04:06:40 PM  all   85.27    0.00    2.62    0.01    0.00    0.00    0.00    0.00   12.10 • 04:06:40 PM    0   85.71    0.00    2.58    0.01    0.00    0.00    0.00    0.00   11.69 • 04:06:40 PM    1   85.05    0.00    2.77    0.05    0.00    0.04    0.00    0.00   12.09 • 04:06:40 PM    2   85.26    0.00    2.69    0.00    0.00    0.00    0.00    0.00   12.05 • 04:06:40 PM    3   85.24    0.00    2.65    0.01    0.00    0.00    0.00    0.00   12.10 • 04:06:40 PM    4   87.36    0.00    1.90    0.00    0.00    0.00    0.00    0.00   10.73 • 04:06:40 PM    5   84.97    0.00    2.70    0.00    0.00    0.00    0.00    0.00   12.33 • 04:06:40 PM    6   85.23    0.00    2.64    0.00    0.00    0.00    0.00    0.00   12.13 • 04:06:40 PM    7   84.97    0.00    2.71    0.00    0.00    0.00    0.00    0.00   12.32 • 04:06:40 PM    8   85.33    0.00    2.60    0.00    0.00    0.00    0.00    0.00   12.06 • 04:06:40 PM    9   85.32    0.00    2.57    0.00    0.00    0.00    0.00    0.00   12.11 • 04:06:40 PM   10   84.88    0.00    2.77    0.00    0.00    0.00    0.00    0.00   12.35 • 04:06:40 PM   11   84.93    0.00    2.69    0.00    0.00    0.00    0.00    0.00   12.38 • 04:06:40 PM   12   85.16    0.00    2.62    0.00    0.00    0.00    0.00    0.00   12.21 • 04:06:40 PM   13   85.00    0.00    2.69    0.00    0.00    0.00    0.00    0.00   12.31 • 04:06:40 PM   14   84.91    0.00    2.75    0.00    0.00    0.00    0.00    0.00   12.34 • 04:06:40 PM   15   85.02    0.00    2.65    0.00    0.00    0.00    0.00    0.00   12.33 Confidential

  38. 系统资源分析 (Memory) • Memory : (free)                total       used       free     shared    buffers     cached Mem:      65895488   32823072   33072416          0      38220   26885024 -/+ buffers/cache:    5899828   59995660 Swap:     66027512          0   66027512 Confidential

  39. 系统资源分析 (IO, HDD) IO: (iostat) Device:            tpsBlk_read/s   Blk_wrtn/s   Blk_readBlk_wrtn sda               9.01       125.71      2063.47    3096354   50823660 dm-0              0.64        12.63         1.99     311170      49016 dm-1              0.01         0.10         0.00       2576          0 dm-2            258.17       112.05      2061.48    2759698   50774616 HDD : (df) Filesystem           1K-blocks      Used Available Use% Mounted on /dev/mapper/vg_r720-lv_root                       51606140   5002372  43982328  11% / tmpfs                 32947744        88  32947656   1% /dev/shm /dev/sda1               495844     37433    432811   8% /boot /dev/mapper/vg_r720-lv_home                      458559680  58258760 377007380  14% /home Confidential

  40. Intel 测试 Confidential

  41. Confidential

  42. Intel links • http://software.intel.com/en-us/articles/building-the-wrf-with-intel-compilers-on-linux-and-improving-performance-on-intel-architecture/ • http://software.intel.com/en-us/articles/wrf-and-wps-v311-installation-bkm-with-inter-compilers-and-intelr-mpi/ • http://www.hpcadvisorycouncil.com/pdf/WRF_Best_Practices.pdf Confidential

  43. Intel Compilers Flags Confidential

  44. Intel 调优 http://software.intel.com/en-us/articles/performance-hints-for-wrf-on-intel-architecture/ • 1。Reducing MPI overhead: • -genv I_MPI_PIN_DOMAIN omp • -genv KMP_AFFINITY=compact • -perhost • 2。 Improving cache and memory bandwidth utilization: • numtiles = X • 3。Using Intel® Math Kernel Library (MKL) DFT for polar filters: • Depending on workload, Intel® MKL DFT may • provide up to 3x speedup of simulation speed • 4。Speeding up computations by reducing precision: • -fp-model fast=2 -no-prec-div -no-prec-sqrt Confidential

  45. 案例分享

  46. 华大基因研究院

  47. 清华大学生命科学院

  48. Success References in Life Science • 国内 • Beijing Genome Institute (BGI) • Tsinghua University Life Institute • Beijing Normal University • Jiang Su Tai Cang Life Institute • The 4th Military Medical University • … • 国外 • David H. Murdock Research Institute • Virginia Bioinformatics Institute • University of Florida speeds up memory intensive gene • UCSF • National Center for Supercomputing Applications • … Confidential

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