On Sat, Dec 11, 2021 at 5:09 PM Rohan Yadav <[email protected]> wrote:
> > 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. > Interesting. TACO is supposed to give an optimized SpMV. > > 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 >>>>>>>> >>>>>>>
