> Did you mean with 1 rank or 40 mpi ranks, petsc's performance is close to 1 thread or 40 threads of TACO?
The 1 rank time is the same as taco 1 thread, and the 40 rank time is the same as taco 40 threads. Rohan On Sat, Dec 11, 2021 at 6:07 PM Junchao Zhang <[email protected]> wrote: > > > On Sat, Dec 11, 2021, 4:22 PM Rohan Yadav <[email protected]> wrote: > >> Thanks all for the help, the main problem was the lack of optimization >> flags in the default build provided by my system. A manual installation >> with optimization flags delivers performance equal to the single node >> benchmark I discussed before. >> > Did you mean with 1 rank or 40 mpi ranks, petsc's performance is close to > 1 thread or 40 threads of TACO? > >> >> Rohan >> >> On Sat, Dec 11, 2021 at 4:04 PM Rohan Yadav <[email protected]> >> wrote: >> >>> > The matrix market file in text format is not good for load. One >>> should convert it to petsc binary format (only once), and use the new >>> binary file afterwards. >>> >>> Yes, I understand this. The point I'm trying to make is that using PETSc >>> to even perform the initial conversion from matrix market to the binary >>> format was prohibitively slow using `MatSetValues`. >>> >>> > I meant 10 lines of code without any function call, which can be >>> thought of as a textbook implementation of SpMV. As a baseline, one can >>> apply optimizations to it. PETSc does not do sophisticated sparse matrix >>> optimization itself, instead it relies on third-party libraries. I >>> remember we had OSKI from Berkeley for CPU, and on GPU we use cuSparse, >>> hipSparse, MKLSparse or Kokkos-Kernels. If TACO is good, then petsc can add >>> an interface to it too. >>> >>> Yes, this is what I expected. Given that PETSc uses high-performance >>> kernels for for the sparse matrix operation itself, I was surprised to see >>> that the single-thread performance of PETSc to be closer to a baseline like >>> TACO. This performance will likely improve when I compile PETSc with >>> optimization flags. >>> >>> Rohan >>> >>> On Sat, Dec 11, 2021 at 1:04 PM Junchao Zhang <[email protected]> >>> wrote: >>> >>>> >>>> >>>> >>>> On Sat, Dec 11, 2021 at 10:28 AM Rohan Yadav <[email protected]> >>>> wrote: >>>> >>>>> Hi Junchao, >>>>> >>>>> Thanks for the response! >>>>> >>>>> > You can use https://petsc.org/main/src/mat/tests/ex72.c.html to >>>>> convert a Matrix Market file into a petsc binary file. And then in >>>>> your test, load the binary matrix, following this example >>>>> https://petsc.org/main/src/mat/tutorials/ex1.c.html >>>>> >>>>> I tried an example like this, but the performance was too slow (it >>>>> would process ~2000-3000 calls to `SetValue` a second), which is not >>>>> reasonable for loading matrices with millions of non-zeros. >>>>> >>>> The matrix market file in text format is not good for load. One should >>>> convert it to petsc binary format (only once), and use the new binary file >>>> afterwards. >>>> >>>> >>>>> >>>>> > I don't know what "No Races" means, but it seems you'd better also >>>>> verify the result of SpMV. >>>>> >>>>> This is a correct implementation of SpMV. The no-races is fine as it >>>>> parallelizes over the rows of the matrix, and thus does not need >>>>> synchronization between writes to the output. >>>>> >>>>> > You can think petsc's default CSR spmv is the baseline, which is >>>>> done in ~10 lines of code. >>>>> >>>>> I'm sorry, but I don't think that is a reasonable statement w.r.t to >>>>> the lines of code making it a good baseline. The TACO compiler also can be >>>>> used in 10 lines of code to compute an SpMV, or any other state-of-the-art >>>>> library could wrap an SpMV implementation behind a single function call. >>>>> I'm wondering if this performance I'm seeing using PETSc is expected, or >>>>> if >>>>> I've misconfigured or am misusing the system in some way. >>>>> >>>> I meant 10 lines of code without any function call, which can be >>>> thought of as a textbook implementation of SpMV. As a baseline, one can >>>> apply optimizations to it. PETSc does not do sophisticated sparse matrix >>>> optimization itself, instead it relies on third-party libraries. I >>>> remember we had OSKI from Berkeley for CPU, and on GPU we use cuSparse, >>>> hipSparse, MKLSparse or Kokkos-Kernels. If TACO is good, then petsc can add >>>> an interface to it too. >>>> >>>> >>>>> Rohan >>>>> >>>>> >>>>> On Fri, Dec 10, 2021 at 11:39 PM Junchao Zhang < >>>>> [email protected]> wrote: >>>>> >>>>>> On Fri, Dec 10, 2021 at 8:05 PM Rohan Yadav <[email protected]> >>>>>> wrote: >>>>>> >>>>>>> Hi, I’m Rohan, a student working on compilation techniques for >>>>>>> distributed tensor computations. I’m looking at using PETSc as a >>>>>>> baseline >>>>>>> for experiments I’m running, and want to understand if I’m using PETSc >>>>>>> as >>>>>>> it was intended to achieve high performance, and if the performance I’m >>>>>>> seeing is expected. Currently, I’m just looking at SpMV operations. >>>>>>> >>>>>>> >>>>>>> My experiments are run on the Lassen Supercomputer ( >>>>>>> https://hpc.llnl.gov/hardware/platforms/lassen). The system has 40 >>>>>>> CPUs, 4 V100s and an Infiniband interconnect. A visualization of the >>>>>>> architecture is here: >>>>>>> https://hpc.llnl.gov/sites/default/files/power9-AC922systemDiagram2_1.png >>>>>>> . >>>>>>> >>>>>>> >>>>>>> As of now, I’m trying to understand the single-node performance of >>>>>>> PETSc, as the scaling performance onto multiple nodes appears to be as I >>>>>>> expect. I’m using the arabic-2005 sparse matrix from the SuiteSparse >>>>>>> matrix >>>>>>> collection, detailed here: https://sparse.tamu.edu/LAW/arabic-2005. >>>>>>> As a trusted baseline, I am comparing against SpMV code generated by the >>>>>>> TACO compiler ( >>>>>>> http://tensor-compiler.org/codegen.html?expr=y(i)%20=%20A(i,j)%20*%20x(j)&format=y:d:0;A:ds:0,1;x:d:0&sched=split:i:i0:i1:32;reorder:i0:i1:j;parallelize:i0:CPU%20Thread:No%20Races) >>>>>>> . >>>>>>> >>>>>> I don't know what "No Races" means, but it seems you'd better also >>>>>> verify the result of SpMV. >>>>>> >>>>>>> >>>>>>> My experiments find that PETSc is roughly 4 times slower on a single >>>>>>> thread and node than the kernel generated by TACO: >>>>>>> >>>>>>> >>>>>>> PETSc: 1 Thread: 5694.72 ms, 1 Node 40 threads: 262.6 ms. >>>>>>> >>>>>>> TACO: 1 Thread: 1341 ms, 1 Node 40 threads: 86 ms. >>>>>>> >>>>>> You can think petsc's default CSR spmv is the baseline, which is >>>>>> done in ~10 lines of code. >>>>>> >>>>>>> >>>>>>> My code using PETSc is here: >>>>>>> https://github.com/rohany/taco/blob/9e0e30b16bfba5319b15b2d1392f35376952f838/petsc/benchmark.cpp#L38 >>>>>>> . >>>>>>> >>>>>>> >>>>>>> Runs from 1 thread and 1 node with -log_view are attached to the >>>>>>> email. The command lines for each were as follows: >>>>>>> >>>>>>> >>>>>>> 1 node 1 thread: `jsrun -n 1 -c 1 -r 1 -b rs ./bin/benchmark -n 20 >>>>>>> -warmup 10 -matrix $TENSOR_DIR/arabic-2005.petsc -log_view` >>>>>>> >>>>>>> 1 node 40 threads: `jsrun -n 40 -c 1 -r 40 -b rs ./bin/benchmark -n >>>>>>> 20 -warmup 10 -matrix $TENSOR_DIR/arabic-2005.petsc -log_view` >>>>>>> >>>>>>> >>>>>>> >>>>>>> In addition to these benchmarking concerns, I wanted to share my >>>>>>> experiences trying to load data from Matrix Market files into PETSc, >>>>>>> which >>>>>>> ended up 1being much more difficult than I anticipated. Essentially, >>>>>>> trying >>>>>>> to iterate through the Matrix Market files and using `write` to insert >>>>>>> entries into a `Mat` was extremely slow. In order to get reasonable >>>>>>> performance, I had to use an external utility to basically construct a >>>>>>> CSR >>>>>>> matrix, and then pass the arrays from the CSR Matrix into >>>>>>> `MatCreateSeqAIJWithArrays`. I couldn’t find any more guidance on PETSc >>>>>>> forums or Google, so I wanted to know if this was the right way to go. >>>>>>> >>>>>>> >>>>>>> Thanks, >>>>>>> >>>>>>> >>>>>>> Rohan Yadav >>>>>>> >>>>>>
