[petsc-users] Smaller assemble time with increasing processors
Runfeng Jin
jsfaraway at gmail.com
Mon Jul 3 08:55:44 CDT 2023
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?
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
>>>
>>>
>>
>
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