[petsc-dev] Questions around benchmarking and data loading with PETSc

[Rohan Yadav]

> 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 <junchao.zhang at gmail.com>
wrote:

>
>
>
> On Sat, Dec 11, 2021 at 10:28 AM Rohan Yadav <rohany at alumni.cmu.edu>
> 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 <junchao.zhang at gmail.com>
>> wrote:
>>
>>> On Fri, Dec 10, 2021 at 8:05 PM Rohan Yadav <rohany at alumni.cmu.edu>
>>> 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
>>>>
>>>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.mcs.anl.gov/pipermail/petsc-dev/attachments/20211211/b3ab04d3/attachment-0001.html>