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GPU Parallelization Strategy for Irregular Grids

GPU Parallelization Strategy for Irregular Grids. September 8, 2011 Presented by Jim Rosinski Mark Govett , Tom Henderson, Jacques Middlecoff. Regular vs. Irregular grid. Lat/Lon Model. Icosahedral Model. Near constant resolution over the globe Efficient high resolution simulations.

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GPU Parallelization Strategy for Irregular Grids

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  1. GPU Parallelization Strategy for Irregular Grids September 8, 2011 Presented by Jim Rosinski Mark Govett, Tom Henderson, Jacques Middlecoff

  2. Regular vs. Irregular grid Lat/Lon Model Icosahedral Model • Near constant resolution over the globe • Efficient high resolution simulations (slide courtesy Dr. Jin Lee)

  3. Fortran Loop Structures in NIM • Dynamics: (lev,column) • Lots of data independence in both dimensions => thread over levels, block over columns. • Exceptions: MPI messages, vertical summations • TBD Physics: (column,lev,[chunk]) • True for WRF, CAM • Dependence in “lev” dimension Multi-core Workshop

  4. GPU-izing Physics • Problem 1: Can’t thread or block over “k” because most physics has “k” dependence • In NIM this leaves only 1 dimension for parallelism, and efficient CUDA needs 2 • Problem 2: Data transpose needed Multi-core Workshop

  5. Solution to GPU-izing Physics • Transpose and “chunking”: !ACC$REGION(<chunksize>,<nchunks>,<dynvars,physvars:none>) BEGIN do n=1,nvars_dyn2phy ! Loop over dynvars needed in phy do k=1,nz ! Vertical !ACC$DO PARALLEL (1) do c=1,nchunks ! Chunksize*nchunks >= nip !ACC$DO VECTOR (1) do i=1,chunksize ! 128 is a good number to choose for chunksize ipn = min (ipe, ips + (c-1)*chunksize + (i-1)) physvars(i,c,k,n) = dynvars(k,ipn,n) end do end do end do end do !ACC$REGION END Multi-core Workshop

  6. Transpose Performance in NIM Multi-core Workshop

  7. CPU code runs fast • Used PAPI to count flops (Intel compiler) • Requires –O1 (no vectorization) to be accurate! • 2nd run with –O3 (vectorization) to get wallclock 27% of peak on Westmere 2.8 GHz Multi-core Workshop

  8. NIM scaling Multi-core Workshop

  9. Current Software Status • Full dynamics runs on CPU or GPU • ~5X speedup socket to socket on GPU • GPU solution judged reasonable comparing output field diffs vs. applying rounding-level perturbation • Dummy physics can run on CPU or GPU • Single-source • GPU directives ignored in CPU mode • NO constructs that look like: #ifdef GPU <do this> #else <do that> #endif Multi-core Workshop

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