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- Author: Starpu Doc Team
- Publisher: Samurai Media Limited
- ISBN-10: 9888407147
- ISBN-13: 9789888407149
- Format: 21.6 x 28 x 2.7 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
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Description
The use of specialized hardware such as accelerators or coprocessors offers an interesting approach to overcome the physical limits encountered by processor architects. As a result, many machines are now equipped with one or several accelerators (e.g. a GPU), in addition to the usual processor(s). While a lot of efforts have been devoted to offload computation onto such accelerators, very little attention as been paid to portability concerns on the one hand, and to the possibility of having heterogeneous accelerators and processors to interact on the other hand.
StarPU is a runtime system that offers support for heterogeneous multicore architectures, it not only offers a unified view of the computational resources (i.e. CPUs and accelerators at the same time), but it also takes care of efficiently mapping and executing tasks onto an heterogeneous machine while transparently handling low-level issues such as data transfers in a portable fashion.
EXTRA 10 % discount with code: EXTRA
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- Author: Starpu Doc Team
- Publisher: Samurai Media Limited
- ISBN-10: 9888407147
- ISBN-13: 9789888407149
- Format: 21.6 x 28 x 2.7 cm, softcover
- Language: English English
The use of specialized hardware such as accelerators or coprocessors offers an interesting approach to overcome the physical limits encountered by processor architects. As a result, many machines are now equipped with one or several accelerators (e.g. a GPU), in addition to the usual processor(s). While a lot of efforts have been devoted to offload computation onto such accelerators, very little attention as been paid to portability concerns on the one hand, and to the possibility of having heterogeneous accelerators and processors to interact on the other hand.
StarPU is a runtime system that offers support for heterogeneous multicore architectures, it not only offers a unified view of the computational resources (i.e. CPUs and accelerators at the same time), but it also takes care of efficiently mapping and executing tasks onto an heterogeneous machine while transparently handling low-level issues such as data transfers in a portable fashion.
Reviews