On Tue, Feb 7, 2023 at 6:23 AM Mark Adams <[email protected]> wrote: > I do one complete solve to get everything setup, to be safe. > > src/ts/tutorials/ex13.c does this and runs multiple solves, if you like > but one solve is probably fine. >
I think that is SNES ex13 Matt > This was designed as a benchmark and is nice because it can do any order > FE solve of Poisson (uses DM/PetscFE, slow). > src/ksp/ksp/tutorials/ex56.c is old school, hardwired for elasticity but > is simpler and the setup is faster if you are doing large problems per MPI > process. > > Mark > > On Mon, Feb 6, 2023 at 2:06 PM Barry Smith <[email protected]> wrote: > >> >> It should not crash, take a look at the test cases at the bottom of the >> file. You are likely correct if the code, unfortunately, does use >> DMCreateMatrix() it will not work out of the box for geometric multigrid. >> So it might be the wrong example for you. >> >> I don't know what you mean about clever. If you simply set the solution >> to zero at the beginning of the loop then it will just do the same solve >> multiple times. The setup should not do much of anything after the first >> solver. Thought usually solves are big enough that one need not run solves >> multiple times to get a good understanding of their performance. >> >> >> >> >> >> >> On Feb 6, 2023, at 12:44 PM, Paul Grosse-Bley < >> [email protected]> wrote: >> >> Hi Barry, >> >> src/ksp/ksp/tutorials/bench_kspsolve.c is certainly the better starting >> point, thank you! Sadly I get a segfault when executing that example with >> PCMG and more than one level, i.e. with the minimal args: >> >> $ mpiexec -c 1 ./bench_kspsolve -split_ksp -pc_type mg -pc_mg_levels 2 >> =========================================== >> Test: KSP performance - Poisson >> Input matrix: 27-pt finite difference stencil >> -n 100 >> DoFs = 1000000 >> Number of nonzeros = 26463592 >> >> Step1 - creating Vecs and Mat... >> Step2a - running PCSetUp()... >> [0]PETSC ERROR: >> ------------------------------------------------------------------------ >> [0]PETSC ERROR: Caught signal number 11 SEGV: Segmentation Violation, >> probably memory access out of range >> [0]PETSC ERROR: Try option -start_in_debugger or -on_error_attach_debugger >> [0]PETSC ERROR: or see https://petsc.org/release/faq/#valgrind and >> https://petsc.org/release/faq/ >> [0]PETSC ERROR: or try >> https://docs.nvidia.com/cuda/cuda-memcheck/index.html on NVIDIA CUDA >> systems to find memory corruption errors >> [0]PETSC ERROR: configure using --with-debugging=yes, recompile, link, >> and run >> [0]PETSC ERROR: to get more information on the crash. >> [0]PETSC ERROR: Run with -malloc_debug to check if memory corruption is >> causing the crash. >> -------------------------------------------------------------------------- >> MPI_ABORT was invoked on rank 0 in communicator MPI_COMM_WORLD >> with errorcode 59. >> >> NOTE: invoking MPI_ABORT causes Open MPI to kill all MPI processes. >> You may or may not see output from other processes, depending on >> exactly when Open MPI kills them. >> -------------------------------------------------------------------------- >> >> As the matrix is not created using DMDACreate3d I expected it to fail due >> to the missing geometric information, but I expected it to fail more >> gracefully than with a segfault. >> I will try to combine bench_kspsolve.c with ex45.c to get easy MG >> preconditioning, especially since I am interested in the 7pt stencil for >> now. >> >> Concerning my benchmarking loop from before: Is it generally discouraged >> to do this for KSPSolve due to PETSc cleverly/lazily skipping some of the >> work when doing the same solve multiple times or are the solves not >> iterated in bench_kspsolve.c (while the MatMuls are with -matmult) just to >> keep the runtime short? >> >> Thanks, >> Paul >> >> On Monday, February 06, 2023 17:04 CET, Barry Smith <[email protected]> >> wrote: >> >> >> >> >> >> Paul, >> >> I think src/ksp/ksp/tutorials/benchmark_ksp.c is the code intended to >> be used for simple benchmarking. >> >> You can use VecCudaGetArray() to access the GPU memory of the vector >> and then call a CUDA kernel to compute the right hand side vector directly >> on the GPU. >> >> Barry >> >> >> >> On Feb 6, 2023, at 10:57 AM, Paul Grosse-Bley < >> [email protected]> wrote: >> >> Hi, >> >> I want to compare different implementations of multigrid solvers for >> Nvidia GPUs using the poisson problem (starting from ksp tutorial example >> ex45.c). >> Therefore I am trying to get runtime results comparable to hpgmg-cuda >> <https://bitbucket.org/nsakharnykh/hpgmg-cuda/src/master/> >> (finite-volume), i.e. using multiple warmup and measurement solves and >> avoiding measuring setup time. >> For now I am using -log_view with added stages: >> >> PetscLogStageRegister("Solve Bench", &solve_bench_stage); >> for (int i = 0; i < BENCH_SOLVES; i++) { >> PetscCall(KSPSetComputeInitialGuess(ksp, ComputeInitialGuess, NULL)); >> // reset x >> PetscCall(KSPSetUp(ksp)); // try to avoid setup overhead during solve >> PetscCall(PetscDeviceContextSynchronize(dctx)); // make sure that >> everything is done >> >> PetscLogStagePush(solve_bench_stage); >> PetscCall(KSPSolve(ksp, NULL, NULL)); >> PetscLogStagePop(); >> } >> >> This snippet is preceded by a similar loop for warmup. >> >> When profiling this using Nsight Systems, I see that the very first solve >> is much slower which mostly correspods to H2D (host to device) copies and >> e.g. cuBLAS setup (maybe also paging overheads as mentioned in the docs >> <https://petsc.org/release/docs/manual/profiling/#accurate-profiling-and-paging-overheads>, >> but probably insignificant in this case). The following solves have some >> overhead at the start from a H2D copy of a vector (the RHS I guess, as the >> copy is preceeded by a matrix-vector product) in the first MatResidual call >> (callchain: >> KSPSolve->MatResidual->VecAYPX->VecCUDACopyTo->cudaMemcpyAsync). My >> interpretation of the profiling results (i.e. cuBLAS calls) is that that >> vector is overwritten with the residual in Daxpy and therefore has to be >> copied again for the next iteration. >> >> Is there an elegant way of avoiding that H2D copy? I have seen some >> examples on constructing matrices directly on the GPU, but nothing about >> vectors. Any further tips for benchmarking (vs profiling) PETSc solvers? At >> the moment I am using jacobi as smoother, but I would like to have a CUDA >> implementation of SOR instead. Is there a good way of achieving that, e.g. >> using PCHYPREs boomeramg with a single level and "SOR/Jacobi"-smoother as >> smoother in PCMG? Or is the overhead from constantly switching between >> PETSc and hypre too big? >> >> Thanks, >> Paul >> >> >> -- What most experimenters take for granted before they begin their experiments is infinitely more interesting than any results to which their experiments lead. -- Norbert Wiener https://www.cse.buffalo.edu/~knepley/ <http://www.cse.buffalo.edu/~knepley/>
