OPENMP


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AcronymDefinition
OPENMPOpen Multi Processing
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The parts cover elementary C programming, parallel computing using OpenMP, distributed programming and MPI, GPU programming and CUDA, GPU programming and OpenCL, and applications.
Panel 2 for(int iz= HALFLENGTH; iz<nz- HALFLENGTH; iz++) { for(int iy= HALFLENGTH; iy<ny- HALFLENGTH; iy++) { for(int ix= HALFLENGTH; ix<nx- HALFLENGTH; ix++) { int offset = iz*dimnXnY + iy*nx + ix; float value = 0.0; value += ptr_prev[offset]*coeff[0]; Panel 3 float *prev_base = (float*)_mm_malloc(nsize*sizeof(float)+ 32*sizeof (float), 64); float *next_base = (float*)_mm_malloc(nsize*sizeof(float)+ 32*sizeof (float), 64); float *vel_base = (float*)_mm_malloc(nsize*sizeof(float)+ 32*sizeof (float), 64); Panel 4 p.prev = &prev_base[16-HALF_LENGTH]; p.next = &next_base[16-HALF_LENGTH]; p.vel = &vel_base[16-HALF_LENGTH]; Following this advice, we add the #pragma omp simd OpenMP directive at line 42 (as indicated in the Advisor survey analysis).
[8] parallelized both algorithms using OpenMP to reduce the computation time, and achieved a quite good efficiency.
CAPE, which stands for Checkpointing-Aided Parallel Execution, is a checkpoint-based approach to automatically translate and execute OpenMP programs on distributed-memory architectures.
Simulation is parallelized using OpenMP and tested on a single node multi GPU system, consisting of three nVIDIA M2070 devices or three nVIDIA GTX560 devices.
The k-d tree nearest neighbor search algorithm (Bentley 1975) implemented in PyKDTree is highly efficient due to its use of Cython and OpenMP, and it is faster than the Scipy and libann (www.cs.umd.edu/~mount /ANN/) packages.
All the computations were carried out on a workstation with four Xeon E5-4620 CPU and 256 GB of RAM with OpenMP technique, and the digits were stored in double precision.
Time consumption evaluation, excluding interface and video showing time, is conducted using the OpenMP wall clock.
Pas, Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation), The MIT Press, 2007.
Gonzalez-Escribano, "An OpenMP extension that supports thread-level speculation," IEEE Transactions on Parallel and Distributed Systems, vol.
For writing the parallelization code, we used alternatively Intel Cilk Plus and OpenMP frameworks and Intel vectorization C++ language extensions as available using the Intel Compiler v17.0.