I expected TACO was better since its website says "It uses novel compiler techniques to get performance competitive with hand-optimized kernels"
--Junchao Zhang On Sat, Dec 11, 2021 at 5:56 PM Rohan Yadav <[email protected]> wrote: > Sorry, what’s surprising about this? 40 mpi ranks on a single node should > be similar performance as 40 threads. Both petsc and taco are doing a > row-based parallelism strategy so it should line up. > > Rohan Yadav > > On Dec 11, 2021, at 6:44 PM, Junchao Zhang <[email protected]> > wrote: > > > > 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 >>>>>>>>> >>>>>>>>
