> 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 >>>> >>>
