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Abstract:
Previously, FPGAs were known to be highly energy efficient, but notoriously difficult to program, and unsuitable for complex HPC applications. This is changing due to new technology developments: a high-level programming language (OpenCL), hard floating-point units, and tight integration with CPU cores. We''ll compare FPGAs and GPUs with respect to architecture, programming model, programming effort, performance, and energy efficiency, using some radio-astronomical signal-processing and imaging algorithms as examples. Can they compete with GPUs?
Previously, FPGAs were known to be highly energy efficient, but notoriously difficult to program, and unsuitable for complex HPC applications. This is changing due to new technology developments: a high-level programming language (OpenCL), hard floating-point units, and tight integration with CPU cores. We''ll compare FPGAs and GPUs with respect to architecture, programming model, programming effort, performance, and energy efficiency, using some radio-astronomical signal-processing and imaging algorithms as examples. Can they compete with GPUs?  Back
 
Topics:
Astronomy & Astrophysics, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8310
Streaming:
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