Computer Science – Distributed – Parallel – and Cluster Computing
Scientific paper
2011-03-24
Computer Science
Distributed, Parallel, and Cluster Computing
2nd Workshop on Model Based Engineering for Embedded Systems Design on DATE 2011
Scientific paper
Nowadays, several industrial applications are being ported to parallel architectures. These applications take advantage of the potential parallelism provided by multiple core processors. Many-core processors, especially the GPUs(Graphics Processing Unit), have led the race of floating-point performance since 2003. While the performance improvement of general- purpose microprocessors has slowed significantly, the GPUs have continued to improve relentlessly. As of 2009, the ratio between many-core GPUs and multicore CPUs for peak floating-point calculation throughput is about 10 times. However, as parallel programming requires a non-trivial distribution of tasks and data, developers find it hard to implement their applications effectively. Aiming to improve the use of many-core processors, this work presents an case-study using UML and MARTE profile to specify and generate OpenCL code for intensive signal processing applications. Benchmark results show us the viability of the use of MDE approaches to generate GPU applications.
Dekeyser Jean-Luc
Guyomarc'H Frédéric
Rodrigues Wendell
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