1 / 23

Porting the physical parametrizations on GPU using directives

Eidgenössisches Departement des Innern EDI Bundesamt für Meteorologie und Klimatologie MeteoSchweiz. Porting the physical parametrizations on GPU using directives. X. Lapillonne, O. Fuhrer. Outline. Physics with 2d data structure Porting the physical parametrization to GPU using directives

waldo
Download Presentation

Porting the physical parametrizations on GPU using directives

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Eidgenössisches Departement des Innern EDIBundesamt für Meteorologie und Klimatologie MeteoSchweiz Porting the physical parametrizations on GPU using directives X. Lapillonne, O. Fuhrer

  2. Outline • Physics with 2d data structure • Porting the physical parametrization to GPU using directives • Running COSMO on an hybrid GPU-CPU system

  3. New data structure • 2D data fields inside the physics packages with one horizontal and one vertical dimensions: f(nproma,ke), with nproma = ie x je / nblock. • Goals: • Physics package could be shared with ICON code • Blocking strategy: all physics parametrization could be computed while data remains in the cache • organize_physics should be structured as follow: call init_radiationcall init_turbulence … do ib=1,nblock call copy_to block call organize_radiation … call organize_turbulence call copy_back end do • Note : an omp parallelization could be introduced around the block loop where data inside organise_scheme is in block form t_b(nproma,ke)Routines below organize_scheme will be shared with ICON. Fields are passed via argument list: call fesft(t_b(:,:), …

  4. Current status • Base code: COSMO 4.18 • 2d version of microphysics (hydci_pp), radiation (Ritter-Geleyn), turbulence (turbtran+turbdiff). • For the moment microphysics and radiation are in separate block loop. The turbulence scheme is copying 3d fields (i.e turbdiff(t(:,je,:) …) Next steps All 3 parametrizations (microphysics + radiation + turbulence) in a common block loop Performance analysis OMP parallelization (?) Longer term All parametrization required for operational runs should be inside the block loop and in 2 dimensional form

  5. Outline • Physics with 2d data structure • Porting the physical parametrization to GPU using directives • Running COSMO on an hybrid GPU-CPU system

  6. Computing on Graphical Processing Units (GPUs) Benefit from the highly parallel architecture of GPUs Higher peak performance at lower cost / power consumption. High memory bandwidth

  7. Execution model Data Transfer … … … … Parallel threads Host (CPU) Device(GPU) Copy data from CPU to GPU(CPU and GPU memory are separate) Load specific GPU program (Kernel) Execution: Same kernel is executed by all threads, SIMD parallelism (Single instruction, multiple data) Copy back data from GPU to CPU Sequential Kernel Sequential

  8. 3 different kernels Array “a” remains on the GPU between the different kernel calls The directive approach, an example note : PGI directives !$acc data region local(a,b) !$acc update device(b) !initialization !$acc region do kerneldo i=1,Ndo k=1,nlev a(i,k)=0.0D0end do end do !$acc end region ! first layer !$acc region do i=1,N a(i,1)=0.1D0end do !$acc end region ! vertical computation !$acc region do kernel do i=1,N do k=2,nlev a(i,k)=0.95D0*a(i,k-1)+exp(-2*a(i,k)**2)*b(i,k) end do end do !$acc end region !$acc update host(a) !$acc end data region !$acc data region local(a,b) !$acc update device(b) !initialization !$acc region do k=1,nlevdo i=1,N a(i,k)=0.0D0end do end do !$acc end region ! first layer !$acc region do i=1,N a(i,1)=0.1D0end do !$acc end region ! vertical computation !$acc region do k=2,nlevdo i=1,N a(i,k)=0.95D0*a(i,k-1)+exp(-2*a(i,k)**2)*b(i,k) end do end do !$acc end region !$acc update host(a) !$acc end data region Loop reordering N=1000, nlev=60: t= 555 μs t= 225 μs

  9. Physical parametrizations on GPU using directives • Physical parametrizations are tested using standalone code. • Currently ported parametrizations: • PGI : microphysics (hydci_pp), radiation (fesft), turbulence (only turbdiff yet) • OMP– acc (Cray) : microphysics, radiation • GPU optimizaiton: loop reordering, replacement of arrays with scalars • Note: hydci_pp, fesft and turbdiff subroutines represents respectively 6.7%, 8% and 7.3% of the total execution time of a typical cosmo-2 run. • Current version of OMP-acc are a subset of PGI directives and it is possible to write PGI code so that there is almost a one to one translation to omp-acc. • First investigation show similar performance between the two compilers, but would need further analysis

  10. Results, Fermi card using PGI directives Test domain: nx x ny x nz = 80 x 60 x 60 Peak performance of a Fermi card for double precision is 515 GFlop/s, i.e. we are getting respectively 5%, 4.5% and 2.5% peak performance for the microphysics, radiation and turbulence schemes Theoretical bandwith is 140 GB/s, but maximum achievable is around 110 GB/s

  11. Results: Comparison with CPU Parallel CPU code run on 12 cores AMD magny-cours CPU – however there are no mpi-communications in these standalone test codes. Note: Expected performance would be between 3x and 5x and depending whether the problem is compute or memory bandwith bound. Overhead of data transfer for microphysics and turbulence is very large.

  12. Comments on the observed performance • The microphysics has the largest compute intensity (with respect to memory access) and as such is more suited for the GPU. • The lower speed up observed for the radiation is quite relative, and essentially comes from the fact that it is very well optimized and is vectorized on the CPU (~9% Peak performance) • The turbulence scheme requires more memory access. Next steps Port turbtran subroutine with pgi + additional test and optimizations (october 2011) Further investigation of radiation and turbulence schemes with Cray directives (november 2011) GPU version of microphysics + radiation + turbulence inside COSMO (november-december 2011)

  13. Outline • Physics with 2d data structure • Porting the physical parametrization to GPU using directives • Running COSMO on an hybrid GPU-CPU system

  14. Possible future implementations in COSMO Phys. parametrization Dynamic Microphysics Turbulence Radiation I/O GPU GPU GPU GPU Phys. parametrization Dynamic Microphysics Turbulence Radiation I/O GPU Data movement for each routine “Full GPU” : Data remain on device, only send to CPU for I/O and communication Directives C++ - CUDA

  15. GPUs Running COSMO-2 on Hybrid-system Multicores Processor One (or more) multicores CPU Domain decomposition One GPU per subdomain.

  16. Summary Porting of the microphysics, radiation and turbulence scheme on GPU was successfully carried out using a directive based approach Comparing with a 12 cores CPU, a speed up between 2.4x and 6.5x was observed using one Fermi GPU card These results are within the expected values considering hardware properties The large overhead of data transfer shows that the “full GPU” approach (i.e. data remains on the GPU, all computation on the device) is the prefered approach for COSMO

  17. Additional slides

  18. Comparison between PGI and OMP-acc !$acc data region local(a) !time loop do itime=1,nt !initialization !$acc region do k=1,nlevdo i=1,N a(i,k)=0.0D0end do end do !$acc end region ! first layer !$acc region do kernel do i=1,N a(i,1)=0.1D0end do !$acc end region ! vertical computation !$acc region do kernel do i=1,Ndo k=2,nlev a(i,k)=0.95D0*a(i,k-1)+exp(-2*a(i,k)**2)*a(i,k)end do end do !$acc end region end do ! end time loop !$acc update host(a) !$acc end data region !$omp acc_data acc_shared(a) !time loop do itime=1,nt !initialization !$omp acc_region_loop do k=1,nlevdo i=1,N a(i,k)=0.0D0end do end do !$omp end acc_region loop ! first layer !$omp acc_region_loop do i=1,N a(i,1)=0.1D0end do !$omp end acc_region_loop ! vertical computation !$omp acc_region_loop kernel do i=1,Ndo k=2,nlev a(i,k)=0.95D0*a(i,k-1)+exp(-2*a(i,k)**2)*a(i,k)end do end do !$omp end acc_region_loop end do ! end time loop !$omp acc_update host(a) !$omp end acc_data

  19. Craypat infos MAIN_ / mo_gscp_dwd_hydci_pp_ _ (x10) ------------------------------------------------------------------------ User time (approx) 2.999 secs 7197500711 cycles System to D1 refill 2.434M/sec 7300271 lines System to D1 bandwidth 148.576MB/sec 467217344 bytes D2 to D1 bandwidth 1025.770MB/sec 3225672832 bytes L2 to System BW per core 140.940MB/sec 443203504 bytes HW FP Ops / User time 435.162M/sec 1308546592 ops 4.5%peak(DP) MAIN_ / src_radiation_fesft_ (x1) ------------------------------------------------------------------------ User time (approx) 7.226 secs 17342858074 cycles 100.0%Time System to D1 refill 11.380M/sec 82232710 lines System to D1 bandwidth 694.569MB/sec 5262893440 bytes D2 to D1 bandwidth 1162.252MB/sec 8806624128 bytes L2 to System BW per core 645.679MB/sec 4892446080 bytes HW FP Ops / User time 893.252M/sec 6511701846 ops 9.3%peak(DP) MAIN_ / turbulence_diff_ref_turbdiff_ (x10) ------------------------------------------------------------------------ User time (approx) 4.397 secs 10551890928 cycles 100.0%Time System to D1 refill 15.757M/sec 69278266 lines System to D1 bandwidth 961.741MB/sec 4433809024 bytes D2 to D1 bandwidth 485.462MB/sec 2238073856 bytes L2 to System BW per core 982.474MB/sec 4529394160 bytes HW FP Ops / User time 326.405M/sec 1452061875 ops 3.4%peak(DP)

  20. Palu Results

  21. Results, microphysics, double precision, Palu

  22. Results, Radiation, double precision, Palu

  23. Results, Turbulence, double precision, Palu

More Related