Simulation / Modeling / Design

CUDACasts Episode 19: CUDA 6 Guided Performance Analysis with the Visual Profiler

May 01, 2014

By Mark Ebersole

One of the main reasons for accelerating code on an NVIDIA GPU is for an increase in application performance. This is why it’s important to use the best tools available to help you get the performance you’re looking for. CUDA 6?includes?great improvements to the guided analysis tool in the NVIDIA Visual Profiler. Watch today’s CUDACast to see how to use guided analysis to locate potential optimizations for your GPU code.

You can find the code used in this video in the CUDACasts GitHub repository.

To suggest a topic for a future episode of CUDACasts, or if you have any other feedback, please leave a comment below.

Related resources

DLI course: Accelerating CUDA C++ Applications with Concurrent Streams
GTC session: Profilers, Python, and Performance: Nsight Tools for Optimizing Modern CUDA Workloads
GTC session: Introduction to CUDA Programming and Performance Optimization
GTC session: Demystify CUDA Debugging and Performance with Powerful Developer Tools
SDK: Nsight Compute
SDK: CUPTI

Discuss (0)

About the Authors

About Mark Ebersole
As CUDA Educator at NVIDIA, Mark Ebersole teaches developers and programmers about the NVIDIA CUDA parallel computing platform and programming model, and the benefits of GPU computing. With more than ten years of experience as a low-level systems programmer, Mark has spent much of his time at NVIDIA as a GPU systems diagnostics programmer in which he developed a tool to test, debug, validate, and verify GPUs from pre-emulation through bringup and into production. Before joining NVIDIA, he worked for IBM developing Linux drivers for the IBM iSeries server. Mark holds a BS degree in math and computer science from St. Cloud State University.
Follow @cudahamster on Twitter

View all posts by Mark Ebersole