Many-core GPU computing with NVIDIA CUDA

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Many-core GPU computing with NVIDIA CUDA

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International Conference on Supercomputing archive
Proceedings of the 22nd annual international conference on Supercomputing table of contents

Island of Kos, Greece

Pages 1-1

Year of Publication: 2008

ISBN:978-1-60558-158-3

Author

Mark Harris

NVIDIA, Santa Clara, CA, USA

Sponsors

ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 59, Downloads (12 Months): 558, Citation Count: 1

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ABSTRACT

In the past, graphics processors were special-purpose hardwired application accelerators, suitable only for conventional graphics applications. Modern GPUs are fully programmable, massively parallel floating point processors. In this talk I will describe NVIDIA's scalable, highly parallel many-core GPU architecture and how CUDA software for GPU computing delivers high throughput for data-intensive processing. I will discuss how CUDA is reinvigorating research on data-parallel algorithms, reducing time to scientific discovery, and enabling a variety of compute-intensive industrial applications of GPUs beyond computer graphics.

CITED BY

Rajat Raina , Anand Madhavan , Andrew Y. Ng, Large-scale deep unsupervised learning using graphics processors, Proceedings of the 26th Annual International Conference on Machine Learning, p.873-880, June 14-18, 2009, Montreal, Quebec, Canada

INDEX TERMS

Primary Classification:
C. Computer Systems Organization
C.1 PROCESSOR ARCHITECTURES
C.1.4 Parallel Architectures