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GTC ON-DEMAND

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Abstract:
In this session we explore how to analyze and optimize the performance of kernels running on the GPU. Working with a real-world example, we will walk through an analysis-driven process leading to a series of kernel-level optimizations, using NVIDIA's profiling tools as an example. Attendees will learn about the fundamental performance limiters-instruction throughput, memory throughput, and latency and we will present strategies to identify and tackle each type of limiter.
In this session we explore how to analyze and optimize the performance of kernels running on the GPU. Working with a real-world example, we will walk through an analysis-driven process leading to a series of kernel-level optimizations, using NVIDIA's profiling tools as an example. Attendees will learn about the fundamental performance limiters-instruction throughput, memory throughput, and latency and we will present strategies to identify and tackle each type of limiter.  Back
 
Topics:
Performance Optimization, Tools & Libraries, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8630
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Abstract:

In this session we explore how to analyze and optimize the performance of kernels running on the GPU. Working with a real-world example, we will walk through an analysis-driven process leading to a series of kernel-level optimizations, using NVIDIA's profiling tools as an example. Attendees will learn about the fundamental performance limiters-instruction throughput, memory throughput, and latency and we will present strategies to identify and tackle each type of limiter. This session is accompanied by Session S7445, which considers performance optimization at application level.

In this session we explore how to analyze and optimize the performance of kernels running on the GPU. Working with a real-world example, we will walk through an analysis-driven process leading to a series of kernel-level optimizations, using NVIDIA's profiling tools as an example. Attendees will learn about the fundamental performance limiters-instruction throughput, memory throughput, and latency and we will present strategies to identify and tackle each type of limiter. This session is accompanied by Session S7445, which considers performance optimization at application level.

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Topics:
Performance Optimization, Algorithms & Numerical Techniques, Tools & Libraries, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7444
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Abstract:

We'll present a real CUDA application and use NVIDIA Nsight Eclipse Edition on Linux to optimize the performance of the code. Attendees will learn a method to analyze their codes and how to use the tools to apply those ideas.

We'll present a real CUDA application and use NVIDIA Nsight Eclipse Edition on Linux to optimize the performance of the code. Attendees will learn a method to analyze their codes and how to use the tools to apply those ideas.

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Topics:
Performance Optimization, Tools & Libraries
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6111
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Abstract:

We'll present a real CUDA application and use NVIDIA Nsight Visual Studio Edition on Windows to optimize the performance of the code. Attendees will learn a method to analyze their codes and how to use the tools to apply those ideas.

We'll present a real CUDA application and use NVIDIA Nsight Visual Studio Edition on Windows to optimize the performance of the code. Attendees will learn a method to analyze their codes and how to use the tools to apply those ideas.

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Topics:
Performance Optimization, Tools & Libraries
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6112
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Abstract:
Dynamic parallelism enables a CUDA kernel to create and synchronize new nested work by launching child kernels from the GPU. Such a nested parallelism programming model maps directly to many real-world programming patterns like adaptive grids or tree-traversal based computations. We'll systematically analyze the performance characteristics of dynamic parallelism by means of real-world application case studies and suggest programming guidelines to get the best performance out of the dynamic parallelism feature. (This talk will be held in collaboration with Thejaswi Rao.)
Dynamic parallelism enables a CUDA kernel to create and synchronize new nested work by launching child kernels from the GPU. Such a nested parallelism programming model maps directly to many real-world programming patterns like adaptive grids or tree-traversal based computations. We'll systematically analyze the performance characteristics of dynamic parallelism by means of real-world application case studies and suggest programming guidelines to get the best performance out of the dynamic parallelism feature. (This talk will be held in collaboration with Thejaswi Rao.)  Back
 
Topics:
Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6807
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Abstract:
In this session, we will study a real CUDA application and use NVIDIA® Nsight Eclipse Edition on Linux to optimize the performance of the code. The attendees will learn a method to analyze their codes and how to use the tools to apply those ideas.
In this session, we will study a real CUDA application and use NVIDIA® Nsight Eclipse Edition on Linux to optimize the performance of the code. The attendees will learn a method to analyze their codes and how to use the tools to apply those ideas.  Back
 
Topics:
Performance Optimization, Tools & Libraries
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5173
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Abstract:
In this session, we will study a real CUDA application and use Nsight(TM) Visual Studio Edition on Windows to optimize the performance of the code. The attendees will learn a method to analyze their codes and how to use the tools to apply those ideas.
In this session, we will study a real CUDA application and use Nsight(TM) Visual Studio Edition on Windows to optimize the performance of the code. The attendees will learn a method to analyze their codes and how to use the tools to apply those ideas.  Back
 
Topics:
Performance Optimization, Tools & Libraries
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5174
Streaming:
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