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Abstract:
With computational rates in the teraflops, GPUs can accumulate round-off errors at an alarming rate. The errors are no different than those on other IEEE-754-compliant hardware, but GPUs are commonly used for much more intense calculations, so the concern for error is or should be significantly increased. In this talk, we'll examine the accumulation of round-off errors in the n-body application from the CUDA SDK, showing how varied the results can be, depending on the order of operations. We'll then explore a solution that tracks the accumulated errors, motivated by the methods suggested by Kahan (Kahan Summation) and Gustavson, Moreira & Enekel (from their work on stability and accuracy regarding Java portability). The result is a dramatic reduction in round-off error, typically resulting in the nearest floating-point value to the infinitely-precise answer. Furthermore, we will show the performance impact of tracking the errors, which is small, even on numerically-intense algorithms such as the n-body algorithm.
With computational rates in the teraflops, GPUs can accumulate round-off errors at an alarming rate. The errors are no different than those on other IEEE-754-compliant hardware, but GPUs are commonly used for much more intense calculations, so the concern for error is or should be significantly increased. In this talk, we'll examine the accumulation of round-off errors in the n-body application from the CUDA SDK, showing how varied the results can be, depending on the order of operations. We'll then explore a solution that tracks the accumulated errors, motivated by the methods suggested by Kahan (Kahan Summation) and Gustavson, Moreira & Enekel (from their work on stability and accuracy regarding Java portability). The result is a dramatic reduction in round-off error, typically resulting in the nearest floating-point value to the infinitely-precise answer. Furthermore, we will show the performance impact of tracking the errors, which is small, even on numerically-intense algorithms such as the n-body algorithm.  Back
 
Topics:
Numerical Algorithms & Libraries, Programming Languages, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2014
Session ID:
S4370
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Abstract:

Tips and techniques for deploying high-performance computing clusters using NVIDIA® Tesla® GPUs.

Tips and techniques for deploying high-performance computing clusters using NVIDIA® Tesla® GPUs.

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Topics:
HPC and Supercomputing
Type:
Talk
Event:
Supercomputing
Year:
2013
Session ID:
SC3128
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Abstract:

Introduction to deploying, managing, and using GPU clusters. Talk will cover a combination of "lessons learned" and "new features" that are of interest to sites deploying GPU clusters for high-performance computing.

Introduction to deploying, managing, and using GPU clusters. Talk will cover a combination of "lessons learned" and "new features" that are of interest to sites deploying GPU clusters for high-performance computing.

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Topics:
Clusters & GPU Management
Type:
Talk
Event:
GTC Silicon Valley
Year:
2013
Session ID:
S3249
Streaming:
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Abstract:

An overview of designing, deploying, and managing GPU clusters for HPC. Learn to build and operate top500-class GPU computing resources that provide users with the latest CUDA features.

An overview of designing, deploying, and managing GPU clusters for HPC. Learn to build and operate top500-class GPU computing resources that provide users with the latest CUDA features.

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Topics:
Clusters & GPU Management
Type:
Talk
Event:
GTC Silicon Valley
Year:
2012
Session ID:
S2119
Streaming:
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Topics:
Clusters & GPU Management
Type:
Webinar
Event:
GTC Webinars
Year:
2012
Session ID:
GTCE025
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Speakers:
Dale Southard
- NVIDIA
 
Topics:
HPC and AI
Type:
Talk
Event:
Supercomputing
Year:
2011
Session ID:
SC112
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Speakers:
Dale Southard
- NVIDIA
 
Topics:
Cloud Visualization
Type:
Talk
Event:
Supercomputing
Year:
2010
Session ID:
SC1007
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Speakers:
Dale Southard
Abstract:

Learn what to expect when deploying PetaFLOP or larger systems. The June 2010 list of the Top 500 computer systems featured the first GPU based cluster to exceed 1 PetaFLOP of foating point power -- a system that was built in a fraction of the time and the cost a CPU-only system of that performance would have required. An overview of how system builders and administrators should prepare for large-scale HPC deployments.

Learn what to expect when deploying PetaFLOP or larger systems. The June 2010 list of the Top 500 computer systems featured the first GPU based cluster to exceed 1 PetaFLOP of foating point power -- a system that was built in a fraction of the time and the cost a CPU-only system of that performance would have required. An overview of how system builders and administrators should prepare for large-scale HPC deployments.

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Topics:
HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2010
Session ID:
S10017
Streaming:
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