MKL_VERBOSE=1 ./ex1 matrix nonzeros = 100, allocated nonzeros = 100 MKL_VERBOSE Intel(R) MKL 2019.0 Update 4 Product build 20190411 for Intel(R) 64 architecture Intel(R) Advanced Vector Extensions 512 (Intel(R) AVX-512) with support of Vector Neural Network Instructions enabled processors, Lnx 2.50GHz lp64 gnu_thread MKL_VERBOSE ZGEMV(N,10,10,0x7ffd9d7078f0,0x187eb20,10,0x187f7c0,1,0x7ffd9d707900,0x187ff70,1) 167.34ms CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZSYTRF(L,10,0x1894b50,10,0x1893df0,0x7ffd9d7078c0,-1,0) 77.19ms CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZSYTRF(L,10,0x1894b50,10,0x1893df0,0x1894490,10,0) 83.97ms CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZSYTRS(L,10,1,0x1894b50,10,0x1893df0,0x1880720,10,0) 44.94ms CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZAXPY(10,0x7ffd9d7078f0,0x187f7c0,1,0x1880720,1) 20.72us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZSYTRS(L,10,2,0x1894b50,10,0x1893df0,0x187d2a0,10,0) 4.22us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZGEMM(N,N,10,2,10,0x7ffd9d707790,0x187eb20,10,0x187d2a0,10,0x7ffd9d7077a0,0x1896a70,10) 1.41ms CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZAXPY(20,0x7ffd9d7078a0,0x1896a70,1,0x187b650,1) 381ns CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZSYTRF(L,10,0x1894b50,10,0x1893df0,0x7ffd9d707840,-1,0) 742ns CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZSYTRF(L,10,0x1894b50,10,0x1893df0,0x18951a0,10,0) 4.20us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZSYTRS(L,10,1,0x1894b50,10,0x1893df0,0x1880720,10,0) 2.94us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZAXPY(10,0x7ffd9d7078f0,0x187f7c0,1,0x1880720,1) 292ns CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZGEMV(N,10,10,0x7ffd9d7078f0,0x187eb20,10,0x187f7c0,1,0x7ffd9d707900,0x187ff70,1) 1.17us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZGETRF(10,10,0x1894b50,10,0x1893df0,0) 202.48ms CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZGETRS(N,10,1,0x1894b50,10,0x1893df0,0x1880720,10,0) 20.78ms CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZAXPY(10,0x7ffd9d7078f0,0x187f7c0,1,0x1880720,1) 954ns CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZGETRS(N,10,2,0x1894b50,10,0x1893df0,0x187d2a0,10,0) 30.74ms CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZGEMM(N,N,10,2,10,0x7ffd9d707790,0x187eb20,10,0x187d2a0,10,0x7ffd9d7077a0,0x18969c0,10) 3.95us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZAXPY(20,0x7ffd9d7078a0,0x18969c0,1,0x187b650,1) 995ns CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZGETRF(10,10,0x1894b50,10,0x1893df0,0) 4.09us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZGETRS(N,10,1,0x1894b50,10,0x1893df0,0x1880720,10,0) 3.92us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZAXPY(10,0x7ffd9d7078f0,0x187f7c0,1,0x1880720,1) 274ns CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZGEMV(N,15,10,0x7ffd9d7078f0,0x187ec70,15,0x187fc30,1,0x7ffd9d707900,0x1880400,1) 1.59us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZGEQRF(15,10,0x1894b40,15,0x1894550,0x7ffd9d707900,-1,0) 47.07us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZGEQRF(15,10,0x1894b40,15,0x1894550,0x1895cb0,10,0) 26.62us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZUNMQR(L,C,15,1,10,0x1894b40,15,0x1894550,0x1895b00,15,0x7ffd9d7078b0,-1,0) 35.32us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZUNMQR(L,C,15,1,10,0x1894b40,15,0x1894550,0x1895b00,15,0x1895cb0,10,0) 42.33ms CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZTRTRS(U,N,N,10,1,0x1894b40,15,0x1895b00,15,0) 16.11us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZAXPY(10,0x7ffd9d7078f0,0x187fc30,1,0x1880c70,1) 395ns CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZGEMM(N,N,15,2,10,0x7ffd9d707790,0x187ec70,15,0x187d310,10,0x7ffd9d7077a0,0x187b5b0,15) 3.22us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZUNMQR(L,C,15,2,10,0x1894b40,15,0x1894550,0x1897760,15,0x7ffd9d7078c0,-1,0) 730ns CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZUNMQR(L,C,15,2,10,0x1894b40,15,0x1894550,0x1897760,15,0x1895cb0,10,0) 4.42us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZTRTRS(U,N,N,10,2,0x1894b40,15,0x1897760,15,0) 5.96us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZAXPY(20,0x7ffd9d7078a0,0x187d310,1,0x1897610,1) 222ns CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZGEQRF(15,10,0x1894b40,15,0x18954b0,0x7ffd9d707820,-1,0) 685ns CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZGEQRF(15,10,0x1894b40,15,0x18954b0,0x1895d60,10,0) 6.11us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZUNMQR(L,C,15,1,10,0x1894b40,15,0x18954b0,0x1895bb0,15,0x7ffd9d7078b0,-1,0) 390ns CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZUNMQR(L,C,15,1,10,0x1894b40,15,0x18954b0,0x1895bb0,15,0x1895d60,10,0) 3.09us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZTRTRS(U,N,N,10,1,0x1894b40,15,0x1895bb0,15,0) 1.05us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE ZAXPY(10,0x7ffd9d7078f0,0x187fc30,1,0x1880c70,1) 257ns CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1
Yes, for petsc example, there are MKL outputs, but for my own program. All I did is to change the matrix type from MATAIJ to MATAIJMKL to get optimized performance for spmv from MKL. Should I expect to see any MKL outputs in this case? Thanks, Yongzhong From: Junchao Zhang <junchao.zh...@gmail.com> Date: Saturday, June 22, 2024 at 9:40 AM To: Yongzhong Li <yongzhong...@mail.utoronto.ca> Cc: Pierre Jolivet <pie...@joliv.et>, petsc-users@mcs.anl.gov <petsc-users@mcs.anl.gov> Subject: Re: [petsc-users] [petsc-maint] Assistance Needed with PETSc KSPSolve Performance Issue No, you don't. It is strange. Perhaps you can you run a petsc example first and see if MKL is really used $ cd src/mat/tests $ make ex1 $ MKL_VERBOSE=1 ./ex1 --Junchao Zhang On Fri, Jun 21, 2024 at 4:03 PM Yongzhong Li <yongzhong...@mail.utoronto.ca<mailto:yongzhong...@mail.utoronto.ca>> wrote: I am using export MKL_VERBOSE=1 ./xx in the bash file, do I have to use - ksp_converged_reason? Thanks, Yongzhong From: Pierre Jolivet <pie...@joliv.et<mailto:pie...@joliv.et>> Date: Friday, June 21, 2024 at 1:47 PM To: Yongzhong Li <yongzhong...@mail.utoronto.ca<mailto:yongzhong...@mail.utoronto.ca>> Cc: Junchao Zhang <junchao.zh...@gmail.com<mailto:junchao.zh...@gmail.com>>, petsc-users@mcs.anl.gov<mailto:petsc-users@mcs.anl.gov> <petsc-users@mcs.anl.gov<mailto:petsc-users@mcs.anl.gov>> Subject: Re: [petsc-users] [petsc-maint] Assistance Needed with PETSc KSPSolve Performance Issue 你通常不会收到来自 pie...@joliv.et<mailto:pie...@joliv.et> 的电子邮件。了解这一点为什么很重要<https://urldefense.us/v3/__https://aka.ms/LearnAboutSenderIdentification__;!!G_uCfscf7eWS!flsZMI97ne0yyxHhLda3hROB9qsgstuZS-jPinxGIzFCCSdn1ujdoMR8dyz-5_kVqqMM-12Lt0dTdjKrx3wXhHZmBhNydvFQeSY$ > How do you set the variable? $ MKL_VERBOSE=1 ./ex1 -ksp_converged_reason MKL_VERBOSE oneMKL 2024.0 Update 1 Product build 20240215 for Intel(R) 64 architecture Intel(R) Advanced Vector Extensions 2 (Intel(R) AVX2) enabled processors, Lnx 2.80GHz lp64 intel_thread MKL_VERBOSE DDOT(10,0x22127c0,1,0x22127c0,1) 2.02ms CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE DSCAL(10,0x7ffc9fb4ff08,0x22127c0,1) 12.67us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE DDOT(10,0x22127c0,1,0x2212840,1) 1.52us CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 MKL_VERBOSE DDOT(10,0x2212840,1,0x2212840,1) 167ns CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:1 [...] On 21 Jun 2024, at 7:37 PM, Yongzhong Li <yongzhong...@mail.utoronto.ca<mailto:yongzhong...@mail.utoronto.ca>> wrote: This Message Is From an External Sender This message came from outside your organization. Hello all, I set MKL_VERBOSE = 1, but observed no print output specific to the use of MKL. Does PETSc enable this verbose output? Best, Yongzhong From: Pierre Jolivet <pie...@joliv.et<mailto:pie...@joliv.et>> Date: Friday, June 21, 2024 at 1:36 AM To: Junchao Zhang <junchao.zh...@gmail.com<mailto:junchao.zh...@gmail.com>> Cc: Yongzhong Li <yongzhong...@mail.utoronto.ca<mailto:yongzhong...@mail.utoronto.ca>>, petsc-users@mcs.anl.gov<mailto:petsc-users@mcs.anl.gov> <petsc-users@mcs.anl.gov<mailto:petsc-users@mcs.anl.gov>> Subject: Re: [petsc-users] [petsc-maint] Assistance Needed with PETSc KSPSolve Performance Issue 你通常不会收到来自 pie...@joliv.et<mailto:pie...@joliv.et> 的电子邮件。了解这一点为什么很重要<https://urldefense.us/v3/__https://aka.ms/LearnAboutSenderIdentification__;!!G_uCfscf7eWS!eXBeeIXo9Yqgp2nypqwKYimLnGBZXnF4dXxgLM1UoOIO6n8nt3XlfgjVWLPWJh4UOa5NNpx-nrJb_H828XRQKUREfR2m69oCbxI$> On 21 Jun 2024, at 6:42 AM, Junchao Zhang <junchao.zh...@gmail.com<mailto:junchao.zh...@gmail.com>> wrote: This Message Is From an External Sender This message came from outside your organization. I remember there are some MKL env vars to print MKL routines called. The environment variable is MKL_VERBOSE Thanks, Pierre Maybe we can try it to see what MKL routines are really used and then we can understand why some petsc functions did not speed up --Junchao Zhang On Thu, Jun 20, 2024 at 10:39 PM Yongzhong Li <yongzhong...@mail.utoronto.ca<mailto:yongzhong...@mail.utoronto.ca>> wrote: This Message Is From an External Sender This message came from outside your organization. Hi Barry, sorry for my last results. I didn’t fully understand the stage profiling and logging in PETSc, now I only record KSPSolve() stage of my program. Some sample codes are as follow, // Static variable to keep track of the stage counter static int stageCounter = 1; // Generate a unique stage name std::ostringstream oss; oss << "Stage " << stageCounter << " of Code"; std::string stageName = oss.str(); // Register the stage PetscLogStage stagenum; PetscLogStageRegister(stageName.c_str(), &stagenum); PetscLogStagePush(stagenum); KSPSolve(*ksp_ptr, b, x); PetscLogStagePop(); stageCounter++; I have attached my new logging results, there are 1 main stage and 4 other stages where each one is KSPSolve() call. To provide some additional backgrounds, if you recall, I have been trying to get efficient iterative solution using multithreading. I found out by compiling PETSc with Intel MKL library instead of OpenBLAS, I am able to perform sparse matrix-vector multiplication faster, I am using MATSEQAIJMKL. This makes the shell matrix vector product in each iteration scale well with the #of threads. However, I found out the total GMERS solve time (~KSPSolve() time) is not scaling well the #of threads. >From the logging results I learned that when performing KSPSolve(), there are >some CPU overheads in PCApply() and KSPGMERSOrthog(). I ran my programs using >different number of threads and plotted the time consumption for PCApply() and >KSPGMERSOrthog() against #of thread. I found out these two operations are not >scaling with the threads at all! My results are attached as the pdf to give >you a clear view. My questions is, >From my understanding, in PCApply, MatSolve() is involved, KSPGMERSOrthog() >will have many vector operations, so why these two parts can’t scale well with >the # of threads when the intel MKL library is linked? Thank you, Yongzhong From: Barry Smith <bsm...@petsc.dev<mailto:bsm...@petsc.dev>> Date: Friday, June 14, 2024 at 11:36 AM To: Yongzhong Li <yongzhong...@mail.utoronto.ca<mailto:yongzhong...@mail.utoronto.ca>> Cc: petsc-users@mcs.anl.gov<mailto:petsc-users@mcs.anl.gov> <petsc-users@mcs.anl.gov<mailto:petsc-users@mcs.anl.gov>>, petsc-ma...@mcs.anl.gov<mailto:petsc-ma...@mcs.anl.gov> <petsc-ma...@mcs.anl.gov<mailto:petsc-ma...@mcs.anl.gov>>, Piero Triverio <piero.trive...@utoronto.ca<mailto:piero.trive...@utoronto.ca>> Subject: Re: [petsc-maint] Assistance Needed with PETSc KSPSolve Performance Issue I am a bit confused. Without the initial guess computation, there are still a bunch of events I don't understand MatTranspose 79 1.0 4.0598e+01 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 MatMatMultSym 110 1.0 1.7419e+02 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 1 0 0 0 0 1 0 0 0 0 0 MatMatMultNum 90 1.0 1.2640e+02 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 1 0 0 0 0 1 0 0 0 0 0 MatMatMatMultSym 20 1.0 1.3049e+02 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 1 0 0 0 0 1 0 0 0 0 0 MatRARtSym 25 1.0 1.2492e+02 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 1 0 0 0 0 1 0 0 0 0 0 MatMatTrnMultSym 25 1.0 8.8265e+01 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 MatMatTrnMultNum 25 1.0 2.4820e+02 1.0 6.83e+10 1.0 0.0e+00 0.0e+00 0.0e+00 1 0 0 0 0 1 0 0 0 0 275 MatTrnMatMultSym 10 1.0 7.2984e-01 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 MatTrnMatMultNum 10 1.0 9.3128e-01 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 in addition there are many more VecMAXPY then VecMDot (in GMRES they are each done the same number of times) VecMDot 5588 1.0 1.7183e+03 1.0 2.06e+13 1.0 0.0e+00 0.0e+00 0.0e+00 8 10 0 0 0 8 10 0 0 0 12016 VecMAXPY 22412 1.0 8.4898e+03 1.0 4.17e+13 1.0 0.0e+00 0.0e+00 0.0e+00 39 20 0 0 0 39 20 0 0 0 4913 Finally there are a huge number of MatMultAdd 258048 1.0 1.4178e+03 1.0 6.10e+13 1.0 0.0e+00 0.0e+00 0.0e+00 7 29 0 0 0 7 29 0 0 0 43025 Are you making calls to all these routines? Are you doing this inside your MatMult() or before you call KSPSolve? The reason I wanted you to make a simpler run without the initial guess code is that your events are far more complicated than would be produced by GMRES alone so it is not possible to understand the behavior you are seeing without fully understanding all the events happening in the code. Barry On Jun 14, 2024, at 1:19 AM, Yongzhong Li <yongzhong...@mail.utoronto.ca<mailto:yongzhong...@mail.utoronto.ca>> wrote: Thanks, I have attached the results without using any KSPGuess. At low frequency, the iteration steps are quite close to the one with KSPGuess, specifically KSPGuess Object: 1 MPI process type: fischer Model 1, size 200 However, I found at higher frequency, the # of iteration steps are significant higher than the one with KSPGuess, I have attahced both of the results for your reference. Moreover, could I ask why the one without the KSPGuess options can be used for a baseline comparsion? What are we comparing here? How does it relate to the performance issue/bottleneck I found? “I have noticed that the time taken by KSPSolve is almost two times greater than the CPU time for matrix-vector product multiplied by the number of iteration” Thank you! Yongzhong From: Barry Smith <bsm...@petsc.dev<mailto:bsm...@petsc.dev>> Date: Thursday, June 13, 2024 at 2:14 PM To: Yongzhong Li <yongzhong...@mail.utoronto.ca<mailto:yongzhong...@mail.utoronto.ca>> Cc: petsc-users@mcs.anl.gov<mailto:petsc-users@mcs.anl.gov> <petsc-users@mcs.anl.gov<mailto:petsc-users@mcs.anl.gov>>, petsc-ma...@mcs.anl.gov<mailto:petsc-ma...@mcs.anl.gov> <petsc-ma...@mcs.anl.gov<mailto:petsc-ma...@mcs.anl.gov>>, Piero Triverio <piero.trive...@utoronto.ca<mailto:piero.trive...@utoronto.ca>> Subject: Re: [petsc-maint] Assistance Needed with PETSc KSPSolve Performance Issue Can you please run the same thing without the KSPGuess option(s) for a baseline comparison? Thanks Barry On Jun 13, 2024, at 1:27 PM, Yongzhong Li <yongzhong...@mail.utoronto.ca<mailto:yongzhong...@mail.utoronto.ca>> wrote: This Message Is From an External Sender This message came from outside your organization. Hi Matt, I have rerun the program with the keys you provided. The system output when performing ksp solve and the final petsc log output were stored in a .txt file attached for your reference. Thanks! Yongzhong From: Matthew Knepley <knep...@gmail.com<mailto:knep...@gmail.com>> Date: Wednesday, June 12, 2024 at 6:46 PM To: Yongzhong Li <yongzhong...@mail.utoronto.ca<mailto:yongzhong...@mail.utoronto.ca>> Cc: petsc-users@mcs.anl.gov<mailto:petsc-users@mcs.anl.gov> <petsc-users@mcs.anl.gov<mailto:petsc-users@mcs.anl.gov>>, petsc-ma...@mcs.anl.gov<mailto:petsc-ma...@mcs.anl.gov> <petsc-ma...@mcs.anl.gov<mailto:petsc-ma...@mcs.anl.gov>>, Piero Triverio <piero.trive...@utoronto.ca<mailto:piero.trive...@utoronto.ca>> Subject: Re: [petsc-maint] Assistance Needed with PETSc KSPSolve Performance Issue 你通常不会收到来自 knep...@gmail.com<mailto:knep...@gmail.com> 的电子邮件。了解这一点为什么很重要<https://urldefense.us/v3/__https://aka.ms/LearnAboutSenderIdentification__;!!G_uCfscf7eWS!djGfJnEhNJROfsMsBJy5u_KoRKbug55xZ64oHKUFnH2cWku_Th1hwt4TDdoMd8pWYVDzJeqJslMNZwpO3y0Et94d31qk-oCEwo4$> On Wed, Jun 12, 2024 at 6:36 PM Yongzhong Li <yongzhong...@mail.utoronto.ca<mailto:yongzhong...@mail.utoronto.ca>> wrote: Dear PETSc’s developers, I hope this email finds you well. I am currently working on a project using PETSc and have encountered a performance issue with the KSPSolve function. Specifically, I have noticed that the time taken by KSPSolve is ZjQcmQRYFpfptBannerStart This Message Is From an External Sender This message came from outside your organization. ZjQcmQRYFpfptBannerEnd Dear PETSc’s developers, I hope this email finds you well. I am currently working on a project using PETSc and have encountered a performance issue with the KSPSolve function. Specifically, I have noticed that the time taken by KSPSolve is almost two times greater than the CPU time for matrix-vector product multiplied by the number of iteration steps. I use C++ chrono to record CPU time. For context, I am using a shell system matrix A. Despite my efforts to parallelize the matrix-vector product (Ax), the overall solve time remains higher than the matrix vector product per iteration indicates when multiple threads were used. Here are a few details of my setup: * Matrix Type: Shell system matrix * Preconditioner: Shell PC * Parallel Environment: Using Intel MKL as PETSc’s BLAS/LAPACK library, multithreading is enabled I have considered several potential reasons, such as preconditioner setup, additional solver operations, and the inherent overhead of using a shell system matrix. However, since KSPSolve is a high-level API, I have been unable to pinpoint the exact cause of the increased solve time. Have you observed the same issue? Could you please provide some experience on how to diagnose and address this performance discrepancy? Any insights or recommendations you could offer would be greatly appreciated. For any performance question like this, we need to see the output of your code run with -ksp_view -ksp_monitor_true_residual -ksp_converged_reason -log_view Thanks, Matt Thank you for your time and assistance. Best regards, Yongzhong ----------------------------------------------------------- Yongzhong Li PhD student | Electromagnetics Group Department of Electrical & Computer Engineering University of Toronto https://urldefense.us/v3/__http://www.modelics.org__;!!G_uCfscf7eWS!flsZMI97ne0yyxHhLda3hROB9qsgstuZS-jPinxGIzFCCSdn1ujdoMR8dyz-5_kVqqMM-12Lt0dTdjKrx3wXhHZmBhNy72AFb1k$ <https://urldefense.us/v3/__http://www.modelics.org__;!!G_uCfscf7eWS!cuLttMJEcegaqu461Bt4QLsO4fASfLM5vjRbtyNhWJQiInbjgNwkGNdkFE1ebSbFjOUatYB0-jd2yQWMWzqkDFFjwMvNl3ZKAr8$> -- What most experimenters take for granted before they begin their experiments is infinitely more interesting than any results to which their experiments lead. -- Norbert Wiener https://urldefense.us/v3/__https://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!flsZMI97ne0yyxHhLda3hROB9qsgstuZS-jPinxGIzFCCSdn1ujdoMR8dyz-5_kVqqMM-12Lt0dTdjKrx3wXhHZmBhNyYEgp7uQ$ <https://urldefense.us/v3/__http://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!djGfJnEhNJROfsMsBJy5u_KoRKbug55xZ64oHKUFnH2cWku_Th1hwt4TDdoMd8pWYVDzJeqJslMNZwpO3y0Et94d31qkNOuenGA$> <ksp_petsc_log.txt> <ksp_petsc_log.txt><ksp_petsc_log_noguess.txt>
