Il giorno mar 16 feb 2021 alle ore 11:43 Roland Richter < [email protected]> ha scritto:
> Hei, > > after profiling my program using -log_view, I got the following output > (all matrices are dense): > > *Using 8 OpenMP threads* > *Using Petsc Development GIT revision: v3.14.3-583-g5464005aea GIT Date: > 2021-01-25 16:01:41 -0600* > > * Max Max/Min Avg Total* > *Time (sec): 5.074e+03 1.000 5.074e+03* > *Objects: 2.158e+03 1.000 2.158e+03* > *Flop: 5.236e+13 1.000 5.236e+13 5.236e+13* > *Flop/sec: 1.032e+10 1.000 1.032e+10 1.032e+10* > *MPI Messages: 0.000e+00 0.000 0.000e+00 0.000e+00* > *MPI Message Lengths: 0.000e+00 0.000 0.000e+00 0.000e+00* > *MPI Reductions: 0.000e+00 0.000* > > *Flop counting convention: 1 flop = 1 real number operation of type > (multiply/divide/add/subtract)* > * e.g., VecAXPY() for real vectors of length N > --> 2N flop* > * and VecAXPY() for complex vectors of length N > --> 8N flop* > > *Summary of Stages: ----- Time ------ ----- Flop ------ --- Messages > --- -- Message Lengths -- -- Reductions --* > * Avg %Total Avg %Total Count > %Total Avg %Total Count %Total* > * 0: Main Stage: 5.0744e+03 100.0% 5.2359e+13 100.0% 0.000e+00 > 0.0% 0.000e+00 0.0% 0.000e+00 0.0%* > > > *------------------------------------------------------------------------------------------------------------------------* > *See the 'Profiling' chapter of the users' manual for details on > interpreting output.* > *Phase summary info:* > * Count: number of times phase was executed* > * Time and Flop: Max - maximum over all processors* > * Ratio - ratio of maximum to minimum over all processors* > * Mess: number of messages sent* > * AvgLen: average message length (bytes)* > * Reduct: number of global reductions* > * Global: entire computation* > * Stage: stages of a computation. Set stages with PetscLogStagePush() > and PetscLogStagePop().* > * %T - percent time in this phase %F - percent flop in this > phase* > * %M - percent messages in this phase %L - percent message > lengths in this phase* > * %R - percent reductions in this phase* > * Total Mflop/s: 10e-6 * (sum of flop over all processors)/(max time > over all processors)* > * GPU Mflop/s: 10e-6 * (sum of flop on GPU over all processors)/(max GPU > time over all processors)* > * CpuToGpu Count: total number of CPU to GPU copies per processor* > * CpuToGpu Size (Mbytes): 10e-6 * (total size of CPU to GPU copies per > processor)* > * GpuToCpu Count: total number of GPU to CPU copies per processor* > * GpuToCpu Size (Mbytes): 10e-6 * (total size of GPU to CPU copies per > processor)* > * GPU %F: percent flops on GPU in this event* > > *------------------------------------------------------------------------------------------------------------------------* > *Event Count Time (sec) > Flop --- Global --- --- Stage ---- Total > GPU - CpuToGpu - - GpuToCpu - GPU* > * Max Ratio Max Ratio Max Ratio Mess AvgLen > Reduct %T %F %M %L %R %T %F %M %L %R Mflop/s Mflop/s Count Size > Count Size %F* > > *---------------------------------------------------------------------------------------------------------------------------------------------------------------* > > *--- Event Stage 0: Main Stage* > > *VecSet 37 1.0 1.0354e-04 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0* > *VecAssemblyBegin 31 1.0 2.9080e-06 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0* > *VecAssemblyEnd 31 1.0 2.3270e-06 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0* > *MatCopy 49928 1.0 3.7437e+02 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 7 0 0 0 0 7 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0* > *MatConvert 2080 1.0 5.8492e+00 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0* > *MatScale 56162 1.0 6.9348e+02 1.0 1.60e+12 1.0 0.0e+00 0.0e+00 > 0.0e+00 14 3 0 0 0 14 3 0 0 0 2303 0 0 0.00e+00 0 > 0.00e+00 0* > *MatAssemblyBegin 56222 1.0 1.7370e-02 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0* > *MatAssemblyEnd 56222 1.0 8.8713e-03 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0* > *MatZeroEntries 60363 1.0 3.1011e+02 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 6 0 0 0 0 6 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0* > *MatAXPY 8320 1.0 1.2254e+02 1.0 5.58e+11 1.0 0.0e+00 0.0e+00 > 0.0e+00 2 1 0 0 0 2 1 0 0 0 4557 0 0 0.00e+00 0 > 0.00e+00 0* > *MatMatMultSym 4161 1.0 7.1613e-03 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0* > *MatMatMultNum 4161 1.0 4.0706e+02 1.0 5.02e+13 1.0 0.0e+00 0.0e+00 > 0.0e+00 8 96 0 0 0 8 96 0 0 0 123331 0 0 0.00e+00 0 > 0.00e+00 0* > > *---------------------------------------------------------------------------------------------------------------------------------------------------------------* > > *Memory usage is given in bytes:* > > *Object Type Creations Destructions Memory Descendants' > Mem.* > *Reports information only for process 0.* > > *--- Event Stage 0: Main Stage* > > * Vector 37 34 1634064 0.* > * Matrix 2120 2120 52734663456 0.* > * Viewer 1 0 0 0.* > > *========================================================================================================================* > > Apparently, MatMatMultNum and MatScale take the most time (by far) during > execution. Therefore, I was wondering if it is possible to move those > operations/all matrices and vectors to a GPU or another accelerator. > According to https://www.mcs.anl.gov/petsc/features/gpus.html CUDA is > only supported for distributed vectors, but not for dense distributed > matrices. Are there any updates related to that, or other ways to speed up > the involved operations? > > You should compute the timings associated with each call, and not consider the lump sum. For example, each MatScale takes 6.9348e+02/56162 = 0.012347851 seconds on average, I doubt you can get any reasonable speedup with CUDA. What are the sizes of these matrices? > Thanks! > > Regards, > > Roland > -- Stefano
