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

[Rohan Yadav]

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

>
>
> On Sat, Dec 11, 2021, 4:22 PM Rohan Yadav <rohany at alumni.cmu.edu> 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 <rohany at alumni.cmu.edu>
>> 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 <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
>>>>>>>
>>>>>>
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