Name
Abstract | ||
|---|---|---|
Today, the multi-core processor has occupied more and more market shares, and the programming personnel also must face the collision brought by the revolution of multi-core processor. Semiconductor scaling limits and associated power and thermal challenges limit performance growth for single-core microprocessors. This reason leads many microprocessor vendors to turn instead to multi-core chip organizations. So programmer or compiler explicitly parallelize the software is the key for enhance the performance on multi-core chip. At the same time, parallel processing is not only the opportunity but also a challenge. The programmer or compiler explicitly parallelize the software is the key for enhance the performance on multi-core chip. In this paper, what we want to know is there any effective way that can reduce our time on rewrite or can automatically parallel the program for multi-processing purpose and do speedup the processing. We discussed some tools that can automatically generate OpenMP directives from serial C/C++ codes, and compare them with each other include normal C/C++ code, and run on general computer and embedded system. Also we compared some tools that are specifically designed to extract the most of data parallelism from C and FORTRAN kernels and translate them into NVIDIA CUDA or OpenCL to know how mush fast after use them. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ISPA.2011.60 | ISPA |
Keywords | Field | DocType |
parallel processing,application program interfaces,openmp parallelization,multi-core systems,multi-core processor,multi-core,nvidia cuda,serial c,cuda,parallel,fortran kernel,multiprocessing systems,openmp directive,performance comparison,c++ language,multi-core chip,auto-parallelization,chip organization,openmp,multicore system,thermal challenges limit performance,normal c,opencl,c/c++ codes,program compilers,auto parallelization,multicore processing,central processing unit,multi core | Programmer,CUDA,Computer science,Parallel computing,Code generation,Compiler,Data parallelism,Multi-core processor,Automatic parallelization,Speedup | Conference |
ISBN | Citations | PageRank |
978-0-7695-4428-1 | 3 | 0.44 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao-Tung Yang | 1 | 1196 | 139.50 |
| Tzu-Chieh Chang | 2 | 8 | 1.66 |
| Hsien-Yi Wang | 3 | 27 | 3.22 |
| William C. Chu | 4 | 449 | 90.83 |
| Chih-Hung Chang | 5 | 233 | 44.07 |