By Tobias Wittwer
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Extra info for An Introduction to Parallel Programming
Parallelising SHALE with ScaLAPACK involves the following steps: • initialising the BLACS process grid, • initialising matrix descriptors and allocating memory accordingly, • distributed setup of design matrix A, • call of routines for matrix multiplication and solving the equation system, • gathering the estimated parameters for output, • exiting the BLACS process grid. 2. 4 with nmax = 50 and n = 16, 200 observations Initialising the BLACS process grid is done by call blacs_pinfo(iam,nprocs) call blacs_get( -1, 0, ictxt ) call blacs_gridinit(ictxt,’R’,nprocs,1) The variable iam contains the process id, nprocs the total number of processes.
9. Once again, computationally intensive steps are shaded. These are the build of the preconditioner and the vector-vector operations in each iteration. 2. A program run with a test data set and nmax = 100 leads to convergence in the fourth iteration. This is surprising at first, but, with noise-free data in a perfect distribution and a good preconditioner, not unexplicable. 2, indicates that more than 90% of the runtime is required for building the preconditioner N−1 bd , but with very little time required for the blockwise inversion.
For allocating memory or harddisk access. System time should stay low. 915s 23 CHAPTER 4. PERFORMANCE ANALYSIS 24 time only measures the total runtime used by the program. For the performance analysis, we want to know the runtime required by individual parts of a program. There are several programming language and operating system dependent methods for measuring time inside a program. Both MPI and OpenMP have their own, platform independent functions for time measurement. MPI_Wtime() and omp_get_wtime() return the wall time in seconds, the difference between the results of two such function calls yields the runtime elapsed between the two function calls.
An Introduction to Parallel Programming by Tobias Wittwer