2026-06-02T00:34:42Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/139082026-02-03T11:58:30Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Lázaro-Muñoz, Antonio J.
author
González-Linares, José María
author
Gómez-Luna, Juan
author
Guil-Mata, Nicolás
2017-06-16T10:54:03Z
2017-06-16T10:54:03Z
2017
http://hdl.handle.net/10630/13908
http://orcid.org/0000-0003-3431-6516
Current heterogeneous platforms with CPUs and accelerators have the ability to launch several independent tasks simultaneously, in order to exploit concurrency among them. These tasks typically consist of data transfer commands and kernel computation commands. In this paper we develop a runtime approach to optimize the concurrency between data transfers and kernel computation commands in a multithreaded scenario where each CPU thread offloads tasks to the accelerator. It deploys a heuristic based on a temporal execution model for concurrent tasks. It is able to establish a near-optimal task execution order that significantly reduces the total execution time, including data transfers. Our approach has been evaluated employing five different benchmarks composed of dominant kernel and dominant transfer real tasks. In these experiments our heuristic achieves speedups up to 1.5x in AMD R9 and NVIDIA K20c accelerators and 1.3x in an Intel Xeon Phi (KNC) device.
eng
by-nc-nd
Computación heterogénea
Efficient OpenCL-based concurrent tasks offloading on accelerators
conference output
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URL
https://riuma.uma.es/bitstreams/35fa7ecf-c585-41ab-9615-43f1c416a492/download
File
MD5
5535ffc2f44f8ca73511069e5dcffb9f
800347
application/pdf
ICCS2017.pdf