CS 591x – Cluster Computing and Programming Parallel Computers Parallel Libraries

Recall that so far we have been – Breaking up (decomposing) our “large” problems into smaller pieces… Distributing the pieces of the problem to multiple processors Explicitly moving data among processes through message passing

Parallel Libraries Note that – Large scientific and engineering problems often represent data in matrices and vectors Large scientific and engineering problems make heavy use of linear algebra, linear systems, non-linear systems

Parallel Libraries MPI is designed to support the development of libraries Consequently, there are a number of libraries, based on MPI, used to develop parallel software Some libraries take care of much, or all of the parallelization That means….

Parallel Libraries … You don’t have to… … but you still can… … if you want … sometimes…

Parallel Libraries ScaLAPACK Scalable Linear Algebra PACKage PETSc Portable, Extensible Toolkit for Scientific Computation

ScaLaPACK Built on LAPACK – Linear Algebra Package Powerful Widely used in scientific and engineering computing not scalable to distributed memory parallel computers LAPACK is built on BLAS – the Basic Linear Algebra Subprogram library

ScaLAPACK uses PBLAS – Parallel BLAS performs local matrix and vector operations in parallel application uses BLAS uses BLACS – Basic Linear Algebra Communications Subprograms library handles interprocess communications for ScaLAPACK uses MPI (other implementations also)

ScaLAPACK Maps matrices and vectors to a process grid called a BLACSgrid similar to an MPI Cartesian topology matrices and vectors decomposed into rectangular blocks – block cyclically distributed to BLACSgrid

ScaLAPACK – sample based on Pacheco pg MPI_Init(&argc, &argv); MPI_Comm_size(MPI_COMM_WORLD, &p); MPI_Comm_rank(MPI_COMM_WORLD,&myrank); Get_input(p, myrank, &n, &n_proc_rows,&nproc_cols, &row_block_size, &col_block_size); m=n; Cblacs_get(0,0,&blacs_grid); /* build blacs grid */ /* R process grid will use row major order */ Cblacs_gridinit(&blacs_grid,”R”,nproc_rows, nproc_cols); Cblacs_pcoord(blacs_grid,my_rank,&my_proc_row,&my_proc_col);

ScaLAPACK – sample cont. local_mat_rows=get_dim(m,row_block_size,my_proc_row,nproc_rows); local_mat_cols=get_dim(n,col_block_size,my_proc_col,nproc_cols); Allocate(my_rank,”A”,&A_local,local_mat_rows*local_mat_cols,1); b_local_size=get_dim(m,row_block_size,my_proc_row,nproc_rows); Allocate(my_rank,”b”,b_local,b_local_size,1); exact_local_size=get_dim(m,col_block_size,my_proc_row,nproc_rows); Allocate(myrank,”Exact”,&exact_local,exact_local_size,1);

ScaLAPACK – sample cont. Build_descript(my_rank,”A”,A_descript,m,n,row_block_size,col_block_siz e,blacs_grid,local_mat_rows); Build_descript(my_rank,”B”,b_descript,m,1,row_block_size,1,blacs_grid, b_local_size); Build_descript(my_rank,”Exact”,exact_descript,n,1,col_block_size,1,blac s_grid,exact_local_size);

scaLAPACK – sample cont. Initialize(p,my_rank,A_local,local_mat_rows,local_mat_cols,exact_loca l,exact_local_size); Mat_vect_mult(m,n,A_local,A_descript, exact_local, exact_descript, b_local, b_descript); Allocate(my_rank,”pivot_list”,&pivot_list,local_mat_rows + row_block_size,0); MPI_Barrier(MPI_COMM_WORLD);

scaLAPACK – sample cont. /* psgesv solves Ax=b returns solution in b */ solve(my_rank,n,A_local,A_descript,pivot_list, b_local, b_descript); … Cblacs_exit(1); MPI_Finalize(); … }

scaLAPACK – sample cont. void Mat_vect_mult(int m, int n, float* A_local, int A_descript, float* x_local, int* x_descript, float y_local, int* y_descript) ( char transpose = ‘N’; … psgemv(&transpose, &m, &n, &alpha, A_local, &first_row_A, &first_col_A, A_descript, x_local, &first_row_x, &first_col_x, x_descript, &beta, y_local, &first_row_y, &first_col_y, y_descript, y_increment); }

Crossing Languages – Some Issues Calling routines from another language calling Fortran subroutine in C Using n dimensional arrays remember row major vs column major Passing arguments in routine/function calls Fortran passes by address, C passes by value

PETSc Portable, Extensible Toolkit for Scientific Computation Large, powerful Solves Partial differential equations Linear systems Non-linear systems Solves matrices – Dense Sparse

PETSc PETSc routines return error codes PETSc error checking routines to help troubleshoot problems CHKERRRA(errorcode)

PETSc Built on top of MPI Developed primarily for C/C++ unlike scaLAPACK has Fortran interface Dense and sparce matrices same interface

PETSc Includes many non-blocking operations i.e. any process can update any cell matrix as non-blocking operation --- other work can be going on while this update operation is carried out Many options available from command line PETSc includes many solvers Solvers can be selected from command line can change solvers without recompiling PETSC_DECIDES

PETSc from --

PETSc from --

PETSc – sample routines PetscOptionsGetInt(PETSC_NULL, “-n”, &n, &flg); VecSetType(Vec x, Vec_type vec_type); VecCreate(MPI_Comm comm, Vec *x); VecSetSizes(Vec x, int m, int M); VecDuplicate(Vec old, Vec new); MatCreate(MPI_Comm comm, int m, int n, int M, int N, Mat* A); MatSetValues(Mat A, int m, int* im, int n, int* in, PetscScalar *values, INSERT_VALUES);

PETSc – sample routines MatAssemblyBegin(Mat A, MAT_FINAL_ASSEMBLY); MatAssemblyEnd(Mat A, MAT_FINAL_ASSEMBLY); KSPCreate(MPI_Comm comm, KSP *ksp); KSPSolve(KSP ksp, Vec b, Vec x); PetscInitialize(&argc, &argv); PetscFinalize();

BLAS (Basic Linear Algebra Subprograms LAPACK Linear Algebra PACKage ScaLaPACK ome.html ome.html

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