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A Light-weight API for Portable Multicore Programming
| Citations: | 7 - 1 self |
BibTeX
@MISC{Baker_alight-weight,
author = {Christopher G. Baker and H. Carter Edwards and Michael A. Heroux and Alan B. Williams},
title = {A Light-weight API for Portable Multicore Programming},
year = {}
}
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Abstract
Abstract—Multicore nodes have become ubiquitous in just a few years. At the same time, writing portable parallel software for multicore nodes is extremely challenging. Widely available programming models such as OpenMP and Pthreads are not useful for devices such as graphics cards, and more flexible programming models such as RapidMind are only available commercially. OpenCL represents the first truly portable standard, but its availability is limited. In the presence of such transition, we have developed a minimal application programming interface (API) for multicore nodes that allows us to write portable parallel linear algebra software that can use any of the aforementioned programming models and any future standard models. We utilize C++ template meta-programming to enable users to write parallel kernels that can be executed on a variety of node types, including Cell, GPUs and multicore CPUs. The support for a parallel node is provided by implementing a Node object, according to the requirements specified by the API. This ability to provide custom support for particular node types gives developers a level of control not allowed by the current slate of proprietary parallel programming APIs. We demonstrate implementations of the API for a simple vector dot-product on sequential CPU, multicore CPU and GPU nodes. I.
Keyphrases
light-weight api portable multicore programming multicore node minimal application proprietary parallel abstract multicore node node type portable parallel linear algebra software particular node type flexible programming model future standard model node object current slate portable standard gpu node template meta-programming custom support sequential cpu aforementioned programming model graphic card portable parallel software simple vector dot-product multicore cpu parallel node parallel kernel
