[petsc-users] About parallel performance
Qin Lu
lu_qin_2000 at yahoo.com
Mon Jun 2 09:13:21 CDT 2014
Will the speedup measured by the streams benchmark the upper limit of speedup of a parallel program? I.e., suppose there is a program with ideal linear speedup (=2 for np=2 if running in a perfect machine for parallelism), if it runs in your laptop, the maximum speedup would be 1.44 with np=2?
Thanks,
Qin
________________________________
From: Barry Smith <bsmith at mcs.anl.gov>
To: Qin Lu <lu_qin_2000 at yahoo.com>
Cc: petsc-users <petsc-users at mcs.anl.gov>
Sent: Thursday, May 29, 2014 5:46 PM
Subject: Re: [petsc-users] About parallel performance
For the parallel case a perfect machine would have twice the memory bandwidth when using 2 cores as opposed to 1 core. For yours it is almost exactly the same. The issue is not with the MPI or software. It depends on how many memory sockets there are and how they are shared by the various cores. As I said the initial memory bandwidth for one core 21,682. gigabytes per second is good so it is a very good sequential machine.
Here are the results on my laptop
Number of MPI processes 1
Process 0 Barrys-MacBook-Pro.local
Function Rate (MB/s)
Copy: 7928.7346
Scale: 8271.5103
Add: 11017.0430
Triad: 10843.9018
Number of MPI processes 2
Process 0 Barrys-MacBook-Pro.local
Process 1 Barrys-MacBook-Pro.local
Function Rate (MB/s)
Copy: 13513.0365
Scale: 13516.7086
Add: 15455.3952
Triad: 15562.0822
------------------------------------------------
np speedup
1 1.0
2 1.44
Note that the memory bandwidth is much lower than your machine but there is an increase in speedup from one to two cores because one core cannot utilize all the memory bandwidth. But even with two cores my laptop will be slower on PETSc then one core on your machine.
Here is the performance on a workstation we have that has multiple CPUs and multiple memory sockets
Number of MPI processes 1
Process 0 es
Function Rate (MB/s)
Copy: 13077.8260
Scale: 12867.1966
Add: 14637.6757
Triad: 14414.4478
Number of MPI processes 2
Process 0 es
Process 1 es
Function Rate (MB/s)
Copy: 22663.3116
Scale: 22102.5495
Add: 25768.1550
Triad: 26076.0410
Number of MPI processes 3
Process 0 es
Process 1 es
Process 2 es
Function Rate (MB/s)
Copy: 27501.7610
Scale: 26971.2183
Add: 30433.3276
Triad: 31302.9396
Number of MPI processes 4
Process 0 es
Process 1 es
Process 2 es
Process 3 es
Function Rate (MB/s)
Copy: 29302.3183
Scale: 30165.5295
Add: 34577.3458
Triad: 35195.8067
------------------------------------------------
np speedup
1 1.0
2 1.81
3 2.17
4 2.44
Note that one core has a lower memory bandwidth than your machine but as I add more cores the memory bandwidth increases by a factor of 2.4
There is nothing wrong with your machine, it is just not suitable to run sparse linear algebra on multiple cores for it.
Barry
On May 29, 2014, at 5:15 PM, Qin Lu <lu_qin_2000 at yahoo.com> wrote:
> Barry,
>
> How did you read the test results? For a machine good for parallism, should the data of np=2 be about half of the those of np=1?
>
> The machine has very new Intel chips and is very for serial run. What may cause the bad parallism? - the configurations of the machine, or I am using a MPI lib (MPICH2) that was not built correctly?
> Many thanks,
> Qin
>
> ----- Original Message -----
> From: Barry Smith <bsmith at mcs.anl.gov>
> To: Qin Lu <lu_qin_2000 at yahoo.com>; petsc-users <petsc-users at mcs.anl.gov>
> Cc:
> Sent: Thursday, May 29, 2014 4:54 PM
> Subject: Re: [petsc-users] About parallel performance
>
>
> In that PETSc version BasicVersion is actually the MPI streams benchmark so you ran the right thing. Your machine is totally worthless for sparse linear algebra parallelism. The entire memory bandwidth is used by the first core so adding the second core to the computation gives you no improvement at all in the streams benchmark.
>
> But the single core memory bandwidth is pretty good so for problems that don’t need parallelism you should get good performance.
>
> Barry
>
>
>
>
> On May 29, 2014, at 4:37 PM, Qin Lu <lu_qin_2000 at yahoo.com> wrote:
>
>> Barry,
>>
>> I have PETSc-3.4.2 and I didn't see MPIVersion there; do you mean BasicVersion? I built and ran it (if you did mean MPIVersion, I will get PETSc-3.4 later):
>>
>> =================
>> [/petsc-3.4.2-64bit/src/benchmarks/streams]$ mpiexec -n 1 ./BasicVersion
>> Number of MPI processes 1
>> Function Rate (MB/s)
>> Copy: 21682.9932
>> Scale: 21637.5509
>> Add: 21583.0395
>> Triad: 21504.6563
>> [/petsc-3.4.2-64bit/src/benchmarks/streams]$ mpiexec -n 2 ./BasicVersion
>> Number of MPI processes 2
>> Function Rate (MB/s)
>> Copy: 21369.6976
>> Scale: 21632.3203
>> Add: 22203.7107
>> Triad: 22305.1841
>> =======================
>>
>> Thanks a lot,
>> Qin
>>
>> From: Barry Smith <bsmith at mcs.anl.gov>
>> To: Qin Lu <lu_qin_2000 at yahoo.com>
>> Cc: "petsc-users at mcs.anl.gov" <petsc-users at mcs.anl.gov>
>> Sent: Thursday, May 29, 2014 4:17 PM
>> Subject: Re: [petsc-users] About parallel performance
>>
>>
>>
>> You need to run the streams benchmarks are one and two processes to see how the memory bandwidth changes. If you are using petsc-3.4 you can
>>
>> cd src/benchmarks/streams/
>>
>> make MPIVersion
>>
>> mpiexec -n 1 ./MPIVersion
>>
>> mpiexec -n 2 ./MPIVersion
>>
>> and send all the results
>>
>> Barry
>>
>>
>>
>> On May 29, 2014, at 4:06 PM, Qin Lu <lu_qin_2000 at yahoo.com> wrote:
>>
>>> For now I only care about the CPU of PETSc subroutines. I tried to add PetscLogEventBegin/End and the results are consistent with the log_summary attached in my first email.
>>>
>>> The CPU of MatSetValues and MatAssemblyBegin/End of both p1 and p2 runs are small (< 20 sec). The CPU of PCSetup/PCApply are about the same between p1 and p2 (~120 sec). The CPU of KSPSolve of p2 (143 sec) is a little faster than p1's (176 sec), but p2 spent more time in MatGetSubMatrice (43 sec). So the total CPU of PETSc subtroutines are about the same between p1 and p2 (502 sec vs. 488 sec).
>>>
>>> It seems I need a more efficient parallel preconditioner. Do you have any suggestions for that?
>>>
>>> Many thanks,
>>> Qin
>>>
>>> ----- Original Message -----
>>> From: Barry Smith <bsmith at mcs.anl.gov>
>>> To: Qin Lu <lu_qin_2000 at yahoo.com>
>>> Cc: "petsc-users at mcs.anl.gov" <petsc-users at mcs.anl.gov>
>>> Sent: Thursday, May 29, 2014 2:12 PM
>>> Subject: Re: [petsc-users] About parallel performance
>>>
>>>
>>> You need to determine where the other 80% of the time is. My guess it is in setting the values into the matrix each time. Use PetscLogEventRegister() and put a PetscLogEventBegin/End() around the code that computes all the entries in the matrix and calls MatSetValues() and MatAssemblyBegin/End().
>>>
>>> Likely the reason the linear solver does not scale better is that you have a machine with multiple cores that share the same memory bandwidth and the first core is already using well over half the memory bandwidth so the second core cannot be fully utilized since both cores have to wait for data to arrive from memory. If you are using the development version of PETSc you can run make streams NPMAX=2 from the PETSc root directory and send this to us to confirm this.
>>>
>>> Barry
>>>
>>>
>>>
>>>
>>>
>>> On May 29, 2014, at 1:23 PM, Qin Lu <lu_qin_2000 at yahoo.com> wrote:
>>>
>>>> Hello,
>>>>
>>>> I implemented PETSc parallel linear solver in a program, the implementation is basically the same as /src/ksp/ksp/examples/tutorials/ex2.c, i.e., I preallocated the MatMPIAIJ, and let PETSc partition the matrix through MatGetOwnershipRange. However, a few tests shows the parallel solver is always a little slower the serial solver (I have excluded the matrix generation CPU).
>>>>
>>>> For serial run I used PCILU as preconditioner; for parallel run, I used ASM with ILU(0) at each subblocks (-sub_pc_type ilu -sub_ksp_type preonly -ksp_type bcgs -pc_type asm). The number of unknowns are around 200,000.
>>>>
>>>> I have used -log_summary to print out the performance summary as attached (log_summary_p1 for serial run and log_summary_p2 for the run with 2 processes). It seems the KSPSolve counts only for less than 20% of Global %T.
>>>> My questions are:
>>>>
>>>> 1. what is the bottle neck of the parallel run according to the summary?
>>>> 2. Do you have any suggestions to improve the parallel performance?
>>>>
>>>> Thanks a lot for your suggestions!
>>>>
>>>> Regards,
>>>> Qin <log_summary_p1.txt><log_summary_p2.txt>
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