Parallel Processing in NCAR Command Language for Performance Improvement

Transcription

1 Parallel Prcessing in NCAR Cmmand Language fr Perfrmance Imprvement Ping Gu, University f Wyming Mentr: Wei Huang, NCAR C- Mentr: Dave Brwn, NCAR August 1, 2013

2 Intrductin and Mtivatin ² The NCAR Cmmand Language (NCL): an interpreted language designed specifically fr scientific data analysis and visualizatin [1] Widely used in the climate and atmspheric sciences cmmunity Over 600 built-in functins and prcedures fr prcessing and manipulating data Available as pre-cmpiled binaries r surce cde ² Prject Mtivatin: try t use all available resurces t speed up NCL When data size is large, NCL cnsumes large amunt f time. There are lts f available resurces: CPUs and GPUs. ² Parallel Prcessing in NCL: OpenMP (Open Multi-Prcessing) CUDA (Cmpute Unified Device Architecture) OpenCL (Open Cmputing Language) MPI (Message Passing Interface) --- nt used in ur prject 2

3 OpenMP ² OpenMP (Open Multi-Prcessing): an API that supprts multi-platfrm shared memry multiprcessing prgramming in C, C++, and Frtran, n mst prcessr architectures and perating systems [2] ² OpenMP is an implementatin f multithreading, which explits frk-jin parallelism mdel. ² OpenMP cnsists f a set f cmpiler directives, library rutines, and envirnment variables: OpenMP directives: v Fr C and C++: #pragma mp cnstruct [clause [clause] ] v Fr Frtran: C$OMP r!$omp r *$OMP cnstruct [clause [clause] ] v Cnstructs: parallel regins, wrk sharing, data envirnment, synchrnizatin, and runtime functins/envirnment variables. Run-time library rutines: used fr a variety f purpses Envirnment variables: used fr cntrlling the executin f parallel cde v e.g. setenv OMP_NUM_THREADS 4 3

4 GPU Cmputing ² GPU (Graphics Prcessing Unit): a single-chip prcessr specifically designed fr prcessing graphics data. GPU has evlved int a highly parallel, multithreaded, manycre prcessr with tremendus cmputatinal hrsepwer and very high memry bandwidth [3] [3] [3] CPU GPU ² GPU Cmputing: using a GPU + a CPU t accelerate general-purpse scientific and engineering applicatins. Any prgram using a GPU fr cmputatin is an integrated hst + device prgram. The hst parts are serial cde and the device parts are highly parallel SPMD (Single-Prgram Multiple-Data) kernel cde. ² The Prcedure f GPU Cmputing: Initialize and select the GPU Allcate device (GPU) memry space Cpy data frm hst (CPU) t device Launch kernels frm the hst Cpy results back frm device t hst Deallcate device memry space 4

5 CUDA ² CUDA (Cmpute Unified Device Architecture): a parallel cmputing platfrm and prgramming mdel created by NVIDIA and implemented by NVIDIA GPUs [4] ² CUDA Thread Hierarchy and Memry Hierarchy: [3] [3] 5

6 OpenCL ² OpenCL (Open Cmputing Language): a framewrk fr writing prgrams that execute acrss hetergeneus platfrms cnsisting f CPUs, GPUs, and ther prcessrs [5] ² The Prcedure f An OpenCL Prgram: Get a list f available platfrms Select device Create Cntext Create cmmand queue Create memry bjects Read kernel file Create prgram bject Cmpile kernel Create kernel bject Set kernel arguments Execute kernel Read memry bject Free bjects 6

7 Experimental Platfrms ² NCAR s Caldera (ne nde): OpenMP: Intel(R) Xen(R) E CPU OpenCL/CUDA: NVIDIA Tesla M2070-Q GPU Platfrms #Cres Clck Speed (GHz) Intel(R) Xen(R) E CPU NVIDIA Tesla M2070-Q GPU

8 Parallel Prcessing Using OpenMP ² Speeding Up Built-in Functins: cvcrm_xy dim_avg_n efunc and efunc_ts ² GPROF (GNU Prfiling Tl) [6]: t find ut the parts f the cde that are time-cnsuming Adding the -pg ptin while cmpilatin t enable prfiling Prducing the prfiling data (gmn.ut) by executing the cde Generating an analysis file by running the prfiling tl v e.g. gprf $NCARG/ni/src/ncl/ncl gmn.ut 8

9 cvcrm_xy ² T calculate a cvariance r crrelatin matrix given tw separate arrays [1] Sftware Perfrmance (Secnds) Native # Threads Perfrmance (Secnds) Speedup With OpenMP

10 dim_avg_n ² T cmpute the average f a variable's given dimensin(s) at all ther dimensins [1] Sftware Perfrmance (Secnds) Native # Threads Perfrmance (Secnds) Speedup With OpenMP

11 efunc and efunc_ts ² efunc: t cmpute empirical rthgnal functins (EOFs, aka: Principal Cmpnent Analysis) [1] ² efunc_ts: t calculate the time series f the amplitudes assciated with each eigenvalue in an EOF [1] Sftware Native Perfrmance (Secnds) efunc efunc_ts # Threads efunc Speedup efunc_ts Speedup With OpenMP

12 Speeding Up FFT Using GPU ² Speeding Up Built-in Functin cfftf ² OpenCL Implementatin ² CUDA Implementatin---Using CUFFT (NVIDIA CUDA Fast Furier Transfrm) library [7] Create an FFT plan cnfiguratin fr a specified signal size and data type Call executin functin(s) t perfrm the transfrm with the defined specificatins Once the plan is n lnger needed, the resurces shuld be released 12

13 cfftf --- With CUDA ² T perfrm a frward cmplex discrete furier transfrm f a real peridic sequence [1] ² CUDA CUFFT Implementatin: supprting any arbitrary input sizes (# Pints) # Pints Perfrmance (Secnds) Native With CUDA Native_cfftf 1 st cfftf 2 nd cfftf

14 cfftf --- With CUDA Lg2(T): (s) FFT Transfrm Size 1st cfftf 2nd cfftf Native_cfftf 1 st cfftf >> 2 nd cfftf Reasn: GPU Setup Time is included GPU Setup Time: 0.62 ~ 0.63 (s) 1536-pint FFT: 2 nd cfftf Exec. Time Native_cfftf Exec. Time pint FFT: 1 st cfftf Exec. Time Native_cfftf Exec. Time 14

15 cfftf --- With OpenCL ² OpenCL Implementatin: supprting input sizes (# Pints) written in the frm 2 a # Pints Perfrmance (Secnds) Native With OpenCL Native_cfftf 1 st cfftf 2 nd cfftf

16 cfftf --- With OpenCL Lg2(T): (s) FFT Transfrm Size 1st cfftf 2nd cfftf Native_cfftf 1 st cfftf >> 2 nd cfftf Reasn: GPU Setup Time is included GPU Setup Time: 0.62 ~ 0.63 (s) 2048-pint FFT: 2 nd cfftf Exec. Time Native_cfftf Exec. Time pint FFT: 1 st cfftf Exec. Time Native_cfftf Exec. Time 16

17 Issues and Discussins ² OpenMP: significant speedup fr sme functins, but nt fr FFT ² CUDA: limited t NVIDIA GPUs ² OpenCL: prtable, duble-precisin flating-pint supprt Available n NVIDIA GPUs with cmpute capability 1.3 and abve [3] Nt available n ur current Mac laptps equipped with the GPU device (AMD Raden HD 6750M) 17

18 Cnclusins ² OpenMP, CUDA, and OpenCL can imprve NCL perfrmance. ² Speedup depends n the prblem size. Future Wrk ² Apply OpenMP t mre NCL functins ² Replace sme NCL libraries with CUDA libraries if pssible 18

19 References [1] The NCAR Cmmand Language (Versin 6.1.2) [Sftware]. (2013). Bulder, Clrad: UCAR/NCAR/CISL/VETS. [2] OpenMP-Wikipedia: [3] CUDA C Prgramming Guide: [4] CUDA-Wikipedia: [5] OpenCL-Wikipedia: [6] GPROF: [7] CUFFT: 19

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