Abstract
Computational accelerators such as GPUs, FPGAs and many-core accelerators can dramatically improve the performance of computing systems and catalyze highly demanding applications. Many scientific and commercial applications are beginning to integrate computational accelerators in their code. However, programming accelerators for high performance remains a challenge, resulting from the restricted architectural features of accelerators compared to general purpose CPUs. Moreover, software must conjointly use conventional CPUs with accelerators to support legacy code and benefit from general purpose operating system services. The objective of this topic is to provide a forum for exchanging new ideas and findings in the domain of accelerator-based computing. © 2013 Springer-Verlag.
Cite
CITATION STYLE
Maruyama, N., Kobbelt, L., Balaji, P., Puzovic, N., Thibault, S., & Zhou, K. (2013). Topic 15: GPU and accelerator computing (Introduction). In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8097 LNCS, p. 800). https://doi.org/10.1007/978-3-642-40047-6_79
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
