[petsc-users] Smaller assemble time with increasing processors
Runfeng Jin
jsfaraway at gmail.com
Mon Jul 3 09:11:42 CDT 2023
Hi,
> We use a hash table to store the nonzeros on the fly, and then convert to
> packed storage on assembly.
>
Maybe can you tell me which file implements this function?
Runfeng
Runfeng Jin <jsfaraway at gmail.com> 于2023年7月3日周一 22:05写道：
> Thank you for all your help!
>
> Runfeng
>
> Matthew Knepley <knepley at gmail.com> 于2023年7月3日周一 22:03写道：
>
>> On Mon, Jul 3, 2023 at 9:56 AM Runfeng Jin <jsfaraway at gmail.com> wrote:
>>
>>> Hi, impressive performance!
>>> I use the newest version of petsc(release branch), and it almost
>>> deletes all assembly and stash time in large processors (assembly time
>>> 64-4s/128-2s/256-0.2s, stash time all below 2s). For the zero programming
>>> cost, it really incredible.
>>> The order code has a regular arrangement of the number of
>>> nonzero-elements across rows, so I can have a good rough preallocation. And
>>> from the data, dedicatedly arrange data and roughly acquiring the max
>>> number of non-zero elements in rows can have a better performance than the
>>> new version without preallocation. However, in reality, I will use the
>>> newer version without preallocation for:1)less effort in programming and
>>> also nearly the same good performance 2) good memory usage(I see no
>>> unneeded memory after assembly) 3) dedicated preallocation is usually not
>>> very easy and cause extra time cost.
>>> Maybe it will be better that leave some space for the user to do a
>>> slight direction for the preallocation and thus acquire better performance.
>>> But have no idea how to direct it.
>>> And I am very curious about how petsc achieves this. How can it not
>>> know anything but achieve so good performance, and no wasted memory? May
>>> you have an explanation about this?
>>>
>>
>> We use a hash table to store the nonzeros on the fly, and then convert to
>> packed storage on assembly.
>>
>> Thanks,
>>
>> Matt
>>
>>
>>> assemble time:
>>> version\processors 4 8 16 32
>>> 64 128 256
>>> old 14677s 4694s 1124s 572s
>>> 38s 8s 2s
>>> new 50s 28s 15s
>>> 7.8s 4s 2s 0.4s
>>> older 27s 24s 19s
>>> 12s 14s - -
>>> stash time(max among all processors):
>>> version\processors 4 8 16 32
>>> 64 128 256
>>> old 3145s 2554s 673s 329s
>>> 201s 142s 138s
>>> new 2s 1s ~0s
>>> ~0s ~0s ~0s ~0s
>>> older 10s 73s 18s
>>> 5s 1s - -
>>> old: my poor preallocation
>>> new: newest version of petsc that do not preallocation
>>> older: the best preallocation version of my code.
>>>
>>>
>>> Runfeng
>>>
>>> Barry Smith <bsmith at petsc.dev> 于2023年7月3日周一 12:19写道：
>>>
>>>>
>>>> The main branch of PETSc now supports filling sparse matrices
>>>> without providing any preallocation information.
>>>>
>>>> You can give it a try. Use your current fastest code but just remove
>>>> ALL the preallocation calls. I would be interested in what kind of
>>>> performance you get compared to your best current performance.
>>>>
>>>> Barry
>>>>
>>>>
>>>>
>>>> On Jul 2, 2023, at 11:24 PM, Runfeng Jin <jsfaraway at gmail.com> wrote:
>>>>
>>>> Hi! Good advice!
>>>> I set value with MatSetValues() API, which sets one part of a row
>>>> at a time(I use a kind of tiling technology so I cannot get all values of a
>>>> row at a time).
>>>> I tested the number of malloc in these three cases. The number of
>>>> mallocs is decreasing with the increase of processors, and all these are
>>>> very large(the matrix is 283234*283234, as can see in the following). This
>>>> should be due to the unqualified preallocation. I use a rough
>>>> preallocation, that every processor counts the number of nonzero elements
>>>> for the first 10 rows, and uses the largest one to preallocate memory for
>>>> all local rows. It seems that not work well.
>>>>
>>>> number_of_processors number_of_max_mallocs_among_all_processors
>>>> 64 20000
>>>> 128 17000
>>>> 256 11000
>>>>
>>>> I change my way to preallocate. I evenly take 100 rows in every
>>>> local matrix and take the largest one to preallocate memory for all local
>>>> rows. Now the assemble time is reduced to a very small time.
>>>>
>>>> number_of_processors number_of_max_mallocs_among_all_processors
>>>> 64 3000
>>>> 128 700
>>>> 256 500
>>>>
>>>> Event Count Time (sec) Flop
>>>> --- Global ---
>>>> --- Stage ---- Total
>>>> Max Ratio Max Ratio Max Ratio
>>>> Mess AvgLen Reduct %T %F %M %L %R %T %F %M %L %R
>>>> Mflop/s
>>>> 64 1 1.0 3.8999e+01 1.0 0.00e+00 0.0
>>>> 7.1e+03 2.9e+05 1.1e+01 15 0 1 8 3 15 0 1 8
>>>> 3 0
>>>>
>>>> 128 1 1.0 8.5714e+00 1.0 0.00e+00 0.0
>>>> 2.6e+04 8.1e+04 1.1e+01 5 0 1 4 3 5 0 1 4
>>>> 3 0
>>>> 256 1 1.0 2.5512e+00 1.0 0.00e+00 0.0
>>>> 1.0e+05 2.3e+04 1.1e+01 2 0 1 3 3 2 0 1 3
>>>> 3 0
>>>>
>>>> So the reason "why assemble time is smaller with the increasing number
>>>> of processors " may be because more processors divide the malloc job so
>>>> that total time is reduced?
>>>> If so, I still have some questions:
>>>> 1. If preallocation is not accurate, will the performance of the
>>>> assembly be affected? I mean, when processors receive the elements that
>>>> should be stored in their local by MPI, then will the new mallocs happen
>>>> at this time point?
>>>> 2. I can not give an accurate preallocation for the large cost, so
>>>> is there any better way to preallocate for my situation?
>>>>
>>>>
>>>>
>>>> Barry Smith <bsmith at petsc.dev> 于2023年7月2日周日 00:16写道：
>>>>
>>>>>
>>>>> I see no reason not to trust the times below, they seem reasonable.
>>>>> You get more than 2 times speed from 64 to 128 and then about 1.38 from 128
>>>>> to 256.
>>>>>
>>>>> The total amount of data moved (number of messages moved times
>>>>> average length) goes from 7.0e+03 * 2.8e+05 1.9600e+09 to 2.1060e+09
>>>>> to 2.3000e+09. A pretty moderate amount of data increase, but note that
>>>>> each time you double the number of ranks, you also increase substantially
>>>>> the network's hardware to move data, so one would hope for a good speed up.
>>>>>
>>>>> Also, the load balance is very good, near 1. Often with assembly,
>>>>> we see very out-of-balance, and it is difficult to get good speedup when
>>>>> the balance is really off.
>>>>>
>>>>> It looks like over 90% of the entire run time is coming from
>>>>> setting and assembling the values? Also the setting values time dominates
>>>>> assembly time more with more ranks. Are you setting a single value at a
>>>>> time or a collection of them? How big are the vectors?
>>>>>
>>>>> Run all three cases with -info :vec to see some information about
>>>>> how many mallocs where move to hold the stashed vector entries.
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On Jun 30, 2023, at 10:25 PM, Runfeng Jin <jsfaraway at gmail.com> wrote:
>>>>>
>>>>>
>>>>>
>>>>> Hi,
>>>>> Thanks for your reply. I try to use PetscLogEvent(), and the
>>>>> result shows same conclusion.
>>>>> What I have done is :
>>>>> ----------------
>>>>> PetscLogEvent Mat_assemble_event, Mat_setvalue_event,
>>>>> Mat_setAsse_event;
>>>>> PetscClassId classid;
>>>>> PetscLogDouble user_event_flops;
>>>>> PetscClassIdRegister("Test assemble and set value", &classid);
>>>>> PetscLogEventRegister("Test only assemble", classid,
>>>>> &Mat_assemble_event);
>>>>> PetscLogEventRegister("Test only set values", classid,
>>>>> &Mat_setvalue_event);
>>>>> PetscLogEventRegister("Test both assemble and set values",
>>>>> classid, &Mat_setAsse_event);
>>>>> PetscLogEventBegin(Mat_setAsse_event, 0, 0, 0, 0);
>>>>> PetscLogEventBegin(Mat_setvalue_event, 0, 0, 0, 0);
>>>>> ...compute elements and use MatSetValues. No call for assembly
>>>>> PetscLogEventEnd(Mat_setvalue_event, 0, 0, 0, 0);
>>>>>
>>>>> PetscLogEventBegin(Mat_assemble_event, 0, 0, 0, 0);
>>>>> MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY);
>>>>> MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY);
>>>>> PetscLogEventEnd(Mat_assemble_event, 0, 0, 0, 0);
>>>>> PetscLogEventEnd(Mat_setAsse_event, 0, 0, 0, 0);
>>>>> ----------------
>>>>>
>>>>> And the output as follows. By the way, dose petsc recorde all time
>>>>> between PetscLogEventBegin and PetscLogEventEnd? or just test the time of
>>>>> petsc API?
>>>>>
>>>>>
>>>>> It is all of the time.
>>>>>
>>>>> ----------------
>>>>> Event Count Time (sec) Flop
>>>>> --- Global --- --- Stage ---- Total
>>>>> Max Ratio *Max* Ratio Max Ratio Mess
>>>>> AvgLen Reduct %T %F %M %L %R %T %F %M %L %R Mflop/s
>>>>> 64new 1 1.0 *2.3775e+02* 1.0 0.00e+00 0.0 6.2e+03
>>>>> 2.3e+04 9.0e+00 52 0 1 1 2 52 0 1 1 2 0
>>>>> 128new 1 1.0* 6.9945e+01* 1.0 0.00e+00 0.0 2.5e+04
>>>>> 1.1e+04 9.0e+00 30 0 1 1 2 30 0 1 1 2 0
>>>>> 256new 1 1.0 *1.7445e+01* 1.0 0.00e+00 0.0 9.9e+04
>>>>> 5.2e+03 9.0e+00 10 0 1 1 2 10 0 1 1 2 0
>>>>>
>>>>> 64:
>>>>> only assemble 1 1.0 *2.6596e+02 *1.0 0.00e+00 0.0 7.0e+03
>>>>> 2.8e+05 1.1e+01 55 0 1 8 3 55 0 1 8 3 0
>>>>> only setvalues 1 1.0 *1.9987e+02* 1.0 0.00e+00 0.0 0.0e+00
>>>>> 0.0e+00 0.0e+00 41 0 0 0 0 41 0 0 0 0 0
>>>>> Test both 1 1.0 4.*6580e+02* 1.0 0.00e+00 0.0 7.0e+03
>>>>> 2.8e+05 1.5e+01 96 0 1 8 4 96 0 1 8 4 0
>>>>>
>>>>> 128:
>>>>> only assemble 1 1.0 *6.9718e+01* 1.0 0.00e+00 0.0 2.6e+04
>>>>> 8.1e+04 1.1e+01 30 0 1 4 3 30 0 1 4 3 0
>>>>> only setvalues 1 1.0 *1.4438e+02* 1.1 0.00e+00 0.0 0.0e+00
>>>>> 0.0e+00 0.0e+00 60 0 0 0 0 60 0 0 0 0 0
>>>>> Test both 1 1.0 *2.1417e+02* 1.0 0.00e+00 0.0 2.6e+04
>>>>> 8.1e+04 1.5e+01 91 0 1 4 4 91 0 1 4 4 0
>>>>>
>>>>> 256:
>>>>> only assemble 1 1.0 *1.7482e+01* 1.0 0.00e+00 0.0 1.0e+05
>>>>> 2.3e+04 1.1e+01 10 0 1 3 3 10 0 1 3 3 0
>>>>> only setvalues 1 1.0 *1.3717e+02* 1.1 0.00e+00 0.0 0.0e+00
>>>>> 0.0e+00 0.0e+00 78 0 0 0 0 78 0 0 0 0 0
>>>>> Test both 1 1.0 *1.5475e+02* 1.0 0.00e+00 0.0 1.0e+05
>>>>> 2.3e+04 1.5e+01 91 0 1 3 4 91 0 1 3 4 0
>>>>>
>>>>>
>>>>>
>>>>> Runfeng
>>>>>
>>>>> Barry Smith <bsmith at petsc.dev> 于2023年6月30日周五 23:35写道：
>>>>>
>>>>>>
>>>>>> You cannot look just at the VecAssemblyEnd() time, that will very
>>>>>> likely give the wrong impression of the total time it takes to put the
>>>>>> values in.
>>>>>>
>>>>>> You need to register a new Event and put a PetscLogEvent() just
>>>>>> before you start generating the vector entries and calling VecSetValues()
>>>>>> and put the PetscLogEventEnd() just after the VecAssemblyEnd() this is the
>>>>>> only way to get an accurate accounting of the time.
>>>>>>
>>>>>> Barry
>>>>>>
>>>>>>
>>>>>> > On Jun 30, 2023, at 11:21 AM, Runfeng Jin <jsfaraway at gmail.com>
>>>>>> wrote:
>>>>>> >
>>>>>> > Hello!
>>>>>> >
>>>>>> > When I use PETSc build a sbaij matrix, I find a strange thing. When
>>>>>> I increase the number of processors, the assemble time become smaller. All
>>>>>> these are totally same matrix. The assemble time mainly arouse from message
>>>>>> passing, which because I use dynamic workload that it is random for which
>>>>>> elements are computed by which processor.
>>>>>> > But from instinct, if use more processors, then more possible that
>>>>>> the processor computes elements storing in other processors. But from the
>>>>>> output of log_view, It seems when use more processors, the processors
>>>>>> compute more elements storing in its local(infer from that, with more
>>>>>> processors, less total amount of passed messages).
>>>>>> >
>>>>>> > What could cause this happened? Thank you!
>>>>>> >
>>>>>> >
>>>>>> > Following is the output of log_view for 64\128\256 processors.
>>>>>> Every row is time profiler of VecAssemblyEnd.
>>>>>> >
>>>>>> >
>>>>>> ------------------------------------------------------------------------------------------------------------------------
>>>>>> > processors Count Time (sec)
>>>>>> Flop
>>>>>> --- Global --- ---
>>>>>> Stage ---- Total
>>>>>> > Max Ratio Max
>>>>>> Ratio Max Ratio Mess AvgLen
>>>>>> Reduct %T %F %M %L %R %T %F %M %L %R Mflop/s
>>>>>> > 64 1 1.0 2.3775e+02
>>>>>> 1.0 0.00e+00 0.0 6.2e+03 2.3e+04 9.0e+00
>>>>>> 52 0 1 1 2 52 0 1 1 2
>>>>>> 0
>>>>>> > 128 1 1.0 6.9945e+01
>>>>>> 1.0 0.00e+00 0.0 2.5e+04 1.1e+04 9.0e+00
>>>>>> 30 0 1 1 2 30 0 1 1 2
>>>>>> 0
>>>>>> > 256 1 1.0 1.7445e+01
>>>>>> 1.0 0.00e+00 0.0 9.9e+04 5.2e+03 9.0e+00
>>>>>> 10 0 1 1 2 10 0 1 1 2
>>>>>> 0
>>>>>> >
>>>>>> > Runfeng Jin
>>>>>>
>>>>>>
>>>>>
>>>>
>>
>> --
>> What most experimenters take for granted before they begin their
>> experiments is infinitely more interesting than any results to which their
>> experiments lead.
>> -- Norbert Wiener
>>
>> https://www.cse.buffalo.edu/~knepley/
>> <http://www.cse.buffalo.edu/~knepley/>
>>
>
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