1. Material adapted from a tutorial by: PETSc Satish Balay Bill Gropp Lois Curfman McInnes Barry Smith www-fp.mcs.anl.gov/petsc/docs/tutorials/index.htm Mathematics and Computer Science Division Argonne National Laboratory http://www.mcs.anl.gov/petsc

2. PETSc Philosophy Writing hand-parallelized (message-passing or distributed shared memory) application codes from scratch is extremely difficult and time consuming. Scalable parallelizing compilers for real application codes are very far in the future. We can ease the development of parallel application codes by developing general-purpose, parallel numerical PDE libraries.

3. PETSc Concepts in the Tutorial How to specify the mathematics of the problem –Data objects vectors, matrices How to solve the problem –Solvers linear, nonlinear, and timestepping (ODE) solvers Parallel computing complications –Parallel data layout structured and unstructured meshes

4. Tutorial Outline Getting started –sample results –programming paradigm Data objects –vectors (e.g., field variables) –matrices (e.g., sparse Jacobians) Viewers –object information –visualization Solvers –linear –nonlinear –timestepping (and ODEs) Data layout and ghost values –structured mesh problems –unstructured mesh problems –partitioning and coloring Putting it all together –a complete example Debugging and error handling Profiling and performance tuning –-log_summary –stages and preloading –user-defined events Extensibility issues

5. Advanced –user-defined customization of algorithms and data structures Developer –advanced customizations, intended primarily for use by library developers Tutorial Approach Beginner –basic functionality, intended for use by most programmers Intermediate –selecting options, performance evaluation and tuning From the perspective of an application programmer: beginner 1 intermediate 2 advanced 3 developer 4

6. Incremental Application Improvement Beginner –Get the application “up and walking” Intermediate –Experiment with options –Determine opportunities for improvement Advanced –Extend algorithms and/or data structures as needed Developer –Consider interface and efficiency issues for integration and interoperability of multiple toolkits

7. The PETSc Programming Model Goals –Portable, runs everywhere –Performance –Scalable parallelism Approach –Distributed memory, “shared-nothing” Requires only a compiler (single node or processor) Access to data on remote machines through MPI –Can still exploit “compiler discovered” parallelism on each node (e.g., SMP) –Hide within parallel objects the details of the communication –User orchestrates communication at a higher abstract level than message passing tutorial introduction

8. Collectivity MPI communicators (MPI_Comm) specify collectivity (processors involved in a computation) All PETSc creation routines for solver and data objects are collective with respect to a communicator, e.g., VecCreate(MPI_Comm comm, int m, int M, Vec *x) Some operations are collective, while others are not, e.g., –collective: VecNorm( ) –not collective: VecGetLocalSize() If a sequence of collective routines is used, they must be called in the same order on each processor tutorial introduction

9. Computation and Communication Kernels MPI, MPI-IO, BLAS, LAPACK Profiling Interface PETSc PDE Application Codes Object-Oriented Matrices, Vectors, Indices Grid Management Linear Solvers Preconditioners + Krylov Methods Nonlinear Solvers, Unconstrained Minimization ODE Integrators Visualization Interface PDE Application Codes tutorial introduction

10. Compressed Sparse Row (AIJ) Blocked Compressed Sparse Row (BAIJ) Block Diagonal (BDIAG) DenseOther IndicesBlock IndicesStrideOther Index Sets Vectors Line SearchTrust Region Newton-based Methods Other Nonlinear Solvers Additive Schwartz Block Jacobi ILUICC LU (Sequential only) Others Preconditioners Euler Backward Euler Pseudo Time Stepping Other Time Steppers GMRESCGCGSBi-CG-STABTFQMRRichardsonChebychevOther Krylov Subspace Methods Matrices PETSc Numerical Components tutorial introduction

11. PETSc codeUser code Application Initialization Function Evaluation Jacobian Evaluation Post- Processing PCKSP PETSc Main Routine Linear Solvers (SLES) Nonlinear Solvers (SNES) Timestepping Solvers (TS) Flow of Control for PDE Solution tutorial introduction

12. True Portability Tightly coupled systems –Cray T3D/T3E –SGI/Origin –IBM SP –Convex Exemplar Loosely coupled systems, e.g., networks of workstations –Sun OS, Solaris 2.5 –IBM AIX –DEC Alpha –HP tutorial introduction –Linux –Freebsd –Windows NT/95

13. Data Objects Object creation Object assembly Setting options Viewing User-defined customizations Vectors ( Vec ) –focus: field data arising in nonlinear PDEs Matrices ( Mat ) –focus: linear operators arising in nonlinear PDEs (i.e., Jacobians) tutorial outline: data objects beginner intermediate advanced intermediate

14. Vectors Fundamental objects for storing field solutions, right-hand sides, etc. VecCreateMPI(...,Vec *) –MPI_Comm - processors that share the vector –number of elements local to this processor –total number of elements Each process locally owns a subvector of contiguously numbered global indices data objects: vectors beginner proc 3 proc 2 proc 0 proc 4 proc 1

15. Vectors: Example #include "vec.h" int main(int argc,char **argv) { Vec x,y,w; /* vectors */ Vec *z; /* array of vectors */ double norm,v,v1,v2;int n = 20,ierr; Scalar one = 1.0,two = 2.0,three = 3.0,dots[3],dot; PetscInitialize(&argc,&argv,(char*)0,help); ierr = OptionsGetInt(PETSC_NULL,"n",&n,PETSC_NULL); CHKERRA(ierr); ierr =VecCreate(PETSC_COMM_WORLD,PETSC_DECIDE,n,&x); CHKERRA(ierr); ierr = VecSetFromOptions(x);CHKERRA(ierr); ierr = VecDuplicate(x,&y);CHKERRA(ierr); ierr = VecDuplicate(x,&w);CHKERRA(ierr); ierr = VecSet(&one,x);CHKERRA(ierr); ierr = VecSet(&two,y);CHKERRA(ierr); ierr = VecSet(&one,z[0]);CHKERRA(ierr); ierr = VecSet(&two,z[1]);CHKERRA(ierr); ierr = VecSet(&three,z[2]);CHKERRA(ierr); ierr = VecDot(x,x,&dot);CHKERRA(ierr); ierr = VecMDot(3,x,z,dots);CHKERRA(ierr);

16. Vectors: Example ierr = PetscPrintf(PETSC_COMM_WORLD,"Vector length %d\n",(int)dot); CHKERRA(ierr); ierr = VecScale(&two,x);CHKERRA(ierr); ierr = VecNorm(x,NORM_2,&norm);CHKERRA(ierr); v = norm-2.0*sqrt((double)n); if (v > -1.e-10 && v < 1.e-10) v = 0.0; ierr = PetscPrintf(PETSC_COMM_WORLD,"VecScale %g\n",v); CHKERRA(ierr); ierr = VecDestroy(x);CHKERRA(ierr); ierr = VecDestroy(y);CHKERRA(ierr); ierr = VecDestroy(w);CHKERRA(ierr); ierr = VecDestroyVecs(z,3);CHKERRA(ierr); PetscFinalize(); return 0; }

17. Vector Assembly VecSetValues(Vec,…) –number of entries to insert/add –indices of entries –values to add –mode: [INSERT_VALUES,ADD_VALUES] VecAssemblyBegin(Vec) VecAssemblyEnd(Vec) data objects: vectors beginner

18. Parallel Matrix and Vector Assembly Processors may generate any entries in vectors and matrices Entries need not be generated on the processor on which they ultimately will be stored PETSc automatically moves data during the assembly process if necessary data objects: vectors and matrices beginner

19. Selected Vector Operations

20. Sparse Matrices Fundamental objects for storing linear operators (e.g., Jacobians) MatCreateMPIAIJ(…,Mat *) –MPI_Comm - processors that share the matrix –number of local rows and columns –number of global rows and columns –optional storage pre-allocation information data objects: matrices beginner

21. Parallel Matrix Distribution MatGetOwnershipRange(Mat A, int *rstart, int *rend) –rstart: first locally owned row of global matrix –rend -1: last locally owned row of global matrix Each process locally owns a submatrix of contiguously numbered global rows. proc 0 } proc 3: locally owned rowsproc 3 proc 2 proc 1 proc 4 data objects: matrices beginner

22. Matrix Assembly MatSetValues(Mat,…) –number of rows to insert/add –indices of rows and columns –number of columns to insert/add –values to add –mode: [INSERT_VALUES,ADD_VALUES] MatAssemblyBegin(Mat) MatAssemblyEnd(Mat) data objects: matrices beginner

23. Viewers Printing information about solver and data objects Visualization of field and matrix data Binary output of vector and matrix data tutorial outline: viewers beginner intermediate

24. Viewer Concepts Information about PETSc objects –runtime choices for solvers, nonzero info for matrices, etc. Data for later use in restarts or external tools –vector fields, matrix contents –various formats (ASCII, binary) Visualization –simple x-window graphics vector fields matrix sparsity structure beginner viewers

25. Viewing Vector Fields VecView(Vec x,Viewer v); Default viewers –ASCII (sequential): VIEWER_STDOUT_SELF –ASCII (parallel): VIEWER_STDOUT_WORLD –X-windows: VIEWER_DRAW_WORLD Default ASCII formats –VIEWER_FORMAT_ASCII_DEFAU LT –VIEWER_FORMAT_ASCII_MATLA B –VIEWER_FORMAT_ASCII_COMM ON –VIEWER_FORMAT_ASCII_INFO –etc. viewers beginner Solution components, using runtime option - snes_vecmonitor velocity: u velocity: v temperature: T vorticity: z

26. Viewing Matrix Data MatView(Mat A, Viewer v); Runtime options available after matrix assembly –-mat_view_info info about matrix assembly –-mat_view_draw sparsity structure –-mat_view data in ASCII –etc. viewers beginner

27. Linear Solvers Goal: Support the solution of linear systems, Ax=b, particularly for sparse, parallel problems arising within PDE-based models User provides: –Code to evaluate A, b solvers: linear beginner

28. PETSc Application Initialization Evaluation of A and b Post- Processing Solve Ax = b PCKSP Linear Solvers (SLES) PETSc codeUser code Linear PDE Solution Main Routine solvers: linear beginner

29. Linear Solvers ( SLES ) Application code interface Choosing the solver Setting algorithmic options Viewing the solver Determining and monitoring convergence Providing a different preconditioner matrix Matrix-free solvers User-defined customizations SLES: Scalable Linear Equations Solvers tutorial outline: solvers: linear beginner intermediate advanced

30. Linear Solvers in PETSc 2.0 Conjugate Gradient GMRES CG-Squared Bi-CG-stab Transpose-free QMR etc. Block Jacobi Overlapping Additive Schwarz ICC, ILU via BlockSolve95 ILU(k), LU (sequential only) etc. Krylov Methods (KSP) Preconditioners (PC) solvers: linear beginner

31. Basic Linear Solver Code (C/C++) SLES sles; /* linear solver context */ Mat A; /* matrix */ Vec x, b; /* solution, RHS vectors */ int n, its; /* problem dimension, number of iterations */ MatCreate(MPI_COMM_WORLD,n,n,&A); /* assemble matrix */ VecCreate(MPI_COMM_WORLD,n,&x); VecDuplicate(x,&b); /* assemble RHS vector */ SLESCreate(MPI_COMM_WORLD,&sles); SLESSetOperators(sles,A,A,DIFFERENT_NONZERO_PA TTERN); SLESSetFromOptions(sles); SLESSolve(sles,b,x,&its); solvers: linear beginner

32. Basic Linear Solver Code (Fortran) SLES sles Mat A Vec x, b integer n, its, ierr call MatCreate(MPI_COMM_WORLD,n,n,A,ierr) call VecCreate(MPI_COMM_WORLD,n,x,ierr) call VecDuplicate(x,b,ierr) call SLESCreate(MPI_COMM_WORLD,sles,ierr) call SLESSetOperators(sles,A,A,DIFFERENT_NONZERO_PATTE RN,ierr) call SLESSetFromOptions(sles,ierr) call SLESSolve(sles,b,x,its,ierr) C then assemble matrix and right-hand-side vector solvers: linear beginner

33. Customization Options Procedural Interface –Provides a great deal of control on a usage-by-usage basis inside a single code –Gives full flexibility inside an application Command Line Interface –Applies same rule to all queries via a database –Enables the user to have complete control at runtime, with no extra coding solvers: linear intermediate

34. Setting Solver Options within Code SLESGetKSP(SLES sles,KSP *ksp) –KSPSetType(KSP ksp,KSPType type) –KSPSetTolerances(KSP ksp,double rtol,double atol,double dtol, int maxits) –... SLESGetPC(SLES sles,PC *pc) –PCSetType(PC pc,PCType) –PCASMSetOverlap(PC pc,int overlap) –... solvers: linear intermediate

35. -ksp_type [cg,gmres,bcgs,tfqmr,…] -pc_type [lu,ilu,jacobi,sor,asm,…] -ksp_max_it -ksp_gmres_restart -pc_asm_overlap -pc_asm_type [basic,restrict,interpolate,none] etc... Setting Solver Options at Runtime solvers: linear beginner 1 intermediate 2 1 2

36. SLES: Review of Basic Usage SLESCreate( ) - Create SLES context SLESSetOperators( ) - Set linear operators SLESSetFromOptions( ) - Set runtime solver options for [SLES, KSP,PC] SLESSolve( ) - Run linear solver SLESView( ) - View solver options actually used at runtime (alternative: -sles_view ) SLESDestroy( ) - Destroy solver beginner solvers: linear

37. SLES: Review of Selected Preconditioner Options And many more options... solvers: linear: preconditioners beginner 1 intermediate 2 1 2

38. SLES: Review of Selected Krylov Method Options solvers: linear: Krylov methods beginner 1 intermediate 2 And many more options... 1 2

39. SLES: Runtime Script Example solvers: linear intermediate

40. Viewing SLES Runtime Options solvers: linear intermediate

41. SLES: Example Programs ex1.c, ex1f.F - basic uniprocessor codes ex2.c, ex2f.F - basic parallel codes ex11.c - using complex numbers ex4.c - using different linear system and preconditioner matrices ex9.c - repeatedly solving different linear systems ex15.c - setting a user-defined preconditioner And many more examples... Location: www.mcs.anl.gov/petsc/src/sles/examples/tutorials/ solvers: linear 1 2 beginner 1 intermediate 2 advanced 3 3