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GTC ON-DEMAND

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Abstract:
This talk consists of two parts. In the first part, we explain how we use Tensor Cores to obtain extreme signal-processing performance. Tensor Cores are special-purpose matrix-multiplication units found in the latest GPUs, and are designed to speed up deep learning. However, their use is not limited to deep learning: we show how a single Tesla V100 GPU can achieve speeds of up to 75 TFLOPS on signal-processing algorithms like correlations and beam forming. In the second part of this talk, we explain how we solve the largest computational challenge in the imaging pipeline of modern radio telescopes. We explain how we implemented and optimized the novel Image-Domain Gridding algorithm on GPUs and compare performance and energy efficiencies with other devices. We show that our solution is an ideal candidate for the world's largest radio telescope (the Square Kilometre Array) as it meets the challenging performance and power consumption constraints.
This talk consists of two parts. In the first part, we explain how we use Tensor Cores to obtain extreme signal-processing performance. Tensor Cores are special-purpose matrix-multiplication units found in the latest GPUs, and are designed to speed up deep learning. However, their use is not limited to deep learning: we show how a single Tesla V100 GPU can achieve speeds of up to 75 TFLOPS on signal-processing algorithms like correlations and beam forming. In the second part of this talk, we explain how we solve the largest computational challenge in the imaging pipeline of modern radio telescopes. We explain how we implemented and optimized the novel Image-Domain Gridding algorithm on GPUs and compare performance and energy efficiencies with other devices. We show that our solution is an ideal candidate for the world's largest radio telescope (the Square Kilometre Array) as it meets the challenging performance and power consumption constraints.  Back
 
Topics:
Performance Optimization, Astronomy & Astrophysics, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9306
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Abstract:
We'll discuss how FPGAs are changing as a result of new technology such as the Open CL high-level programming language, hard floating-point units, and tight integration with CPU cores. Traditionally energy-efficient FPGAs were considered notoriously difficult to program and unsuitable for complex HPC applications. We'll compare the latest FPGAs to GPUs, examining the architecture, programming models, programming effort, performance, and energy efficiency by considering some real applications.
We'll discuss how FPGAs are changing as a result of new technology such as the Open CL high-level programming language, hard floating-point units, and tight integration with CPU cores. Traditionally energy-efficient FPGAs were considered notoriously difficult to program and unsuitable for complex HPC applications. We'll compare the latest FPGAs to GPUs, examining the architecture, programming models, programming effort, performance, and energy efficiency by considering some real applications.  Back
 
Topics:
HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9338
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Abstract:

This talk will present research on accelerator-based computing for radio telescopes. Showing GPU implementations of a dozen of (signal-processing) algorithms used by radio telescopes, e.g., filtering, correlating, beam forming, dedispersion, and peak detection. Glued together, these computational kernels form several processing pipelines. Each pipeline implements an observation mode, as used by the LOFAR radio telescope. Implemented pipelines create sky images, to search for pulsars, to observe known pulsars, and to detect ultra-high-energy particles - first on a Blue Gene/P, and ported these to GPUs. This talk will briefly explain these algorithms and processing pipelines, show performance results, multi-GPU scaling results, and impact on energy efficiency. The research is relevant to current radio telescopes like LOFAR, and the future SKA telescope, that needs exascale computing power.

This talk will present research on accelerator-based computing for radio telescopes. Showing GPU implementations of a dozen of (signal-processing) algorithms used by radio telescopes, e.g., filtering, correlating, beam forming, dedispersion, and peak detection. Glued together, these computational kernels form several processing pipelines. Each pipeline implements an observation mode, as used by the LOFAR radio telescope. Implemented pipelines create sky images, to search for pulsars, to observe known pulsars, and to detect ultra-high-energy particles - first on a Blue Gene/P, and ported these to GPUs. This talk will briefly explain these algorithms and processing pipelines, show performance results, multi-GPU scaling results, and impact on energy efficiency. The research is relevant to current radio telescopes like LOFAR, and the future SKA telescope, that needs exascale computing power.

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Topics:
Astronomy & Astrophysics, Signal and Audio Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2013
Session ID:
S2124
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Abstract:

In this talk, we will present GPU implementations of four highly compute-intensive algorithms used by radio telescopes.

In this talk, we will present GPU implementations of four highly compute-intensive algorithms used by radio telescopes.

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Topics:
Astronomy & Astrophysics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2012
Session ID:
S2124
Streaming:
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