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GTC ON-DEMAND

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Abstract:
Debugging and analyzing NVIDIA GPU-Based HPC applications requires a tool that supports the demands of today's complex CUDA applications. Debuggers must deal with the extensive use of C++ templates, STL, many shared libraries, and debugging optimized code. They need to seamlessly support debugging both host and GPU code, Python, and C/C++ mixed-language applications. They must also scale to the complexities of today's multi-GPU cluster supercomputers such as Summit and Sierra. We'll discuss the advanced technologies provided by the TotalView for an HPC debugger and explain how they're used to analyze and debug complex CUDA applications to make code easily understood and to quickly solve difficult problems. We'll also show TotalView's new user interface. Learn how to easily debug multi-GPU environments and OpenACC, and see a unified debugging view for Python applications that leverage C++ Python extensions such as TensorFlow.
Debugging and analyzing NVIDIA GPU-Based HPC applications requires a tool that supports the demands of today's complex CUDA applications. Debuggers must deal with the extensive use of C++ templates, STL, many shared libraries, and debugging optimized code. They need to seamlessly support debugging both host and GPU code, Python, and C/C++ mixed-language applications. They must also scale to the complexities of today's multi-GPU cluster supercomputers such as Summit and Sierra. We'll discuss the advanced technologies provided by the TotalView for an HPC debugger and explain how they're used to analyze and debug complex CUDA applications to make code easily understood and to quickly solve difficult problems. We'll also show TotalView's new user interface. Learn how to easily debug multi-GPU environments and OpenACC, and see a unified debugging view for Python applications that leverage C++ Python extensions such as TensorFlow.  Back
 
Topics:
Tools & Libraries, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9378
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