Global Marketing1 Confidential

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

Global Marketing2 Confidential

• 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

内容

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生命科学HPC GPU 方案

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在生命科学领域中很多用户采用 GPU 加速解决方案

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CPU + GPU 计算

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HPCC GPU 异构平台

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支持 GPU 的 Dell 服务器方案 (2012 年 ,12 代服务器 )

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C6220 C6220 C6145 C6145

T620

R720

C410x C410x C410x C410x

C6220 C6145

GPU:Socket Ratio 1:1 2:1 1:1 2:1 2:1 1:1Total System Boards 8 4 4 2 1 1Total HIC 8 4 8 4 0 0IB Capable Yes Yes Yes Yes Yes* YesTotal GPU 16 16 16 16 4 2Per GPU B/W 8 4 8 4 4 16MSRP (M2075) $117,000 $86,900 $114,000 $85,250 $19,000 $13,000Power Envelope (est) 5.525 kW 4.118 kW 5.030 kW 3.802 kW Theoretical GFLOPs TBD TBD 9,326 8,932 2,431 1,401 Est. GFLOPs TBD TBD 2,891 1,697 TBD TBD GFLOPS/Rack U TBD TBD 413 339 486 701 $/GFLOPS TBD TBD 39 50 8 9 Rack Size 7 5 7 5 5 2GPU/Rack U 2.3 3.2 2.3 3.2 0.8 1.0

External Solutions (PowerEdge C) Internal Solutions

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GPU 扩展箱方案 (GPU 外置方案 )Dell PowerEdge C410x

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PCIe EXPANSION CHASSIS CONNECTING 1-8 HOSTS TO 1-16 PCIe

• 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

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Great for: HPC including universities, oil & gas, biomed research, design, simulation, mapping, visualization, rendering, and gaming

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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 connectorfor GPGPU card

Board-to-board connector for X16 Gen PCIesignals and power

GPU card

LED

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4 GPU / x16 16GPU/5U3 GPU / x16 12GPU/5U

2 GPU / x16 16GPU/7U1 GPU / x16 8GPU/7U

PowerEdge C410x Configurations• Enabling HPC applications to optimize cost /

performance equation off single x16

PCISwitch GPU

GPUGPU

x16

GPU

HostPCISwitch GPU

GPUGPU

x16Host

PCISwitch GPUx16Host PCI

Switch GPUGPU

x16

GPU/U ratios assume PowerEdge C6100 host with 4 servers per 2U chassis

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HICx16

iPass cable

C410x

HIC

C410xiPass cable

x16

x16

x16

HIC

C410x

x16

x16

x16

x16

iPass cable

Host HIC

C410x

x16

x16

iPass cable

7U = (1) C410x + (2) C6100

7U = (1) C410x + (2) C6100

5U = (1) C410x + (1) C6100

5U = (1) C410x + (1) C6100

C6100

C6100

C6100

C6100

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Flexibility of the PowerEdge C410x• Increases to 8:1 possible with dual x16

PCISwitch GPU

GPUGPU

x16

GPU

Host

PCISwitch

GPUGPUGPUGPUx16

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PCISwitch GPU

GPU

x16

Host

PCISwitch

GPUGPUx16

x16

x16

x16

x16

C410x

x16

x16

x16

x16

x16

x16

x16

x16

C410x

iPass cable

HICHIC

iPass cable

HICHIC

iPass cable

iPass cable

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PowerEdge C6100 Configurations “2:1 Sandwich”

C410x

C6100

C6100

C6100 “2:1 Sandwich”One Dell C410x (16 GPUs)Two C6100 (8 nodes)One x16 slot for each node to 2 GPUs7U total

16 GPUs total8 nodes total (2 GPUs per board)

• 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

Note: This configuration is equivalent tousing the C6100 and the NVIDIA S2050but this configuration is more dense

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Details

Summary

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PowerEdge C6100 Configurations “4:1 Sandwich”

C410x

C6100

C6100 “4:1 Sandwich”One Dell C410x (16 GPUs)One C6100 (4 nodes)One x16 slot for each node to 4 GPUs5U total

16 GPUs total4 nodes total (4 GPUs per

board)

• 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

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Details

Summary

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PowerEdge C6100 Configurations “8:1 Sandwich” (Possible Future Development)

C410x

C6100

C6100 “8:1 Sandwich”Two Dell C410x (32 GPUs)One C6100 (4 nodes)One x16 slot for each node to 8 GPUs8U total

32 GPUs total4 nodes total (8 GPUs per board)

• 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

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Details

Summary

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PowerEdge C6145 Configurations “8:1 Sandwich”

C6145

C6145 “16:1 Sandwich”One Dell C410x (16 GPUs)One C6145 (2 nodes)Two-Four HIC slots for each node to 16 GPUs5U total

16 GPUs total2 nodes total (16 GPUs per

board)

Details

5U of Rack Space

C410x

• 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

Details

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PowerEdge C6145 Configurations “16:1 Sandwich”

C410x

C6145

C6145 “16:1 Sandwich”Two Dell C410x (32 GPUs)One C6145 (2 nodes)Four HIC slots for each node to 16 GPUs8U total

32 GPUs total2 nodes total (16 GPUs per

board)

Details

8U of Rack Space

C410x

• 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

Details

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PowerEdge C410x Block Diagram

GPUs x 16

Switch Level 2 x 4

Switch Level 1 x 8

Host Connections

x 8

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C410X BMC 控制台配置界面

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GPU 扩展箱支持服务器列表• 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.

HIC/C410x Support Matrix

ServerC410x

SupportPlanned Support

DateC6100 Yes NowC6105 RTS+ Now – BIOS

1.7.1 or later

C6145 RTS NowC1100 Yes NowPrecision R5500 Yes Now – Disable

SSC in BIOS

R710 Yes NowM610x Yes NowR410 Yes NowR720 RTS RTSR720xd RTS RTSR620 RTS RTSC6220 RTS RTS

生命科学应用测试 : GPU-HMMER

Dell High Performance Computing 20

415 983 1419 22930

2000

4000

6000

8000

10000

12000GPU-HMMER CPU vs. GPU

CPUC410x / C6100 (1)

Length of HMM

Wal

l Clo

ck (s

)

2.9X 2.8X

2.7X

1.8X

GPU:Host Scaling : GPU-HMMER

Dell High Performance Computing 21

415 983 1419 22930

1000200030004000500060007000

GPU-HMMER: GPU Scaling

C410x / C6100 (1)C410x / C6100 (2)C410x / C6100 (4)Internal 2-x16 (2)

Length of HMM

Wal

l clo

ck (s

) Speedup 1.8X 3.6X 7.2X 3.6X

GPU:Host Scaling: NAMD

Dell High Performance Computing 22

STMV0

0.20.40.60.8

11.21.41.6

0.10

0.47

0.82

1.52

0.95

NAMD

CPUC410x / C6100 (1)C410x / C6100 (2)C410x / C6100 (4)Internal 2-x16 (2)

Step

s/Se

cond

Speedup 4.7X 8.2X 15.2X 9.5X

GPU:Host Scaling : LAMMPS JL-Cut

Dell High Performance Computing 23

256000 500000 10001880

200400600800

100012001400160018002000

LAMMPS LJ GPU Scaling

C410x / C6100 (1)C410x / C6100 (2)C410x / C6100 (4)Internal 2-x16 (2)

Number of Particles

Wal

l clo

ck (s

) Speedup 8.5X 13.5X 14.4X 14.0X

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生命科学存储方案

生命科学计算、数据容量增长率

The Lustre Parallel File System• Key Lustre Components:

1.Clients (compute nodes)• “Users” of the file system where applications run• The Dell HPC Cluster

2. Meta Data Server (MDS)•Holds meta-data information

3. Object Storage Server (OSS)• Provides back-end storage for the users’ files• Additional OSS units increase throughput linearly

Meta Data Server (MDS)

Clients

OSS OSS OSS…

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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)

1 2 4 8 16 24 320

200000

400000

600000

800000

1000000

1200000

1400000

1600000

NSS IPoIB Sequential Reads

NSS SmallNSS MediumNSS Large

Threads (Nodes)

Thro

ughp

ut K

B/s

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)

1 2 4 8 16 24 320

200000

400000

600000

800000

1000000

1200000

1400000

1600000

NSS IPoIB Sequential Writes

NSS SmallNSS MediumNSS Large

Threads (Nodes)

Thro

ughp

ut K

B/s

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WRF V3.3 应用程序测试调优

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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

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

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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

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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) Confidential37

Wrf.out

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…. 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…..

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

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系统资源分析 (Memory)• Memory : (free)  

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total used free shared buffers cached

Mem: 65895488 32823072 33072416 0 38220 26885024

-/+ buffers/cache: 5899828 59995660

Swap: 66027512 0 66027512

系统资源分析 (IO, HDD)

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IO: (iostat)Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtnsda 9.01 125.71 2063.47 3096354 50823660dm-0 0.64 12.63 1.99 311170 49016dm-1 0.01 0.10 0.00 2576 0dm-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

Intel 测试

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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

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Intel Compilers Flags

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Intel 调优

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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

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案例分享

华大基因研究院

清华大学生命科学院

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– …

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谢谢！