2021-05-14T08:23:36Zhttps://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/390412020-05-15T10:26:44Zcom_10324_1165com_10324_931com_10324_894col_10324_1335
00925njm 22002777a 4500
dc
Llanos Ferraris, Diego Rafael
author
Vigo Aguiar, JesÃºs
author
2018
During the first decade of the twenty-first century, the advent of multicore processing reached its maturity level, with the help of shared-memory programming models such as OpenMP [1], that allows to parallelize both legacy and new C and Fortran applications in a shared-memory environments. Meanwhile, message-passing programming models such as MPI [2] allowed to aggregate multicore systems in larger clusters, which dominated the TOP 500 supercomputing list [3]. However, at that time parallel computing seemed to face some limits that were hard to overcome. Physical limits prevented clock frequencies to increase, and the Law of Diminishing Returns reduced the usefulness of keep adding cores to a multiprocessor. Suddenly, the advent of GPU computing changed the game once again. Being initially developed as a way to accelerate graphical processing, GPUs were reused to speed up certain types of calculations that needed a similar processing to an entire set of independent elements. CUDA [4] programming model allowed programmers to translate to the GPU world many applications, and the TOP 500 list started to show more and more heterogeneous systems that incorporated accelerators to their cluster nodes.
8. Computational and mathematical models meet heterogeneous computing (Editorial, CMMSE Special Issue). Diego R. Llanos, Jesus Vigo-Aguiar. The Journal of Supercomputing (Q2), Springer, ISSN 0920-8542, DOI 10.1007/s11227-018-2713-y.
0920-8542
http://uvadoc.uva.es/handle/10324/39041
10.1007/s11227-018-2713-y
999
3
1000
The Journal of Supercomputing
75
1573-0484
Computational and mathematical models meet heterogeneous computing