Table of Contents
Advances in Software Engineering
Volume 2015, Article ID 940628, 14 pages
Research Article

LTTng CLUST: A System-Wide Unified CPU and GPU Tracing Tool for OpenCL Applications

Department of Computer and Software Engineering, Polytechnique Montreal, P.O. Box 6079, Station Downtown, Montreal, QC, Canada H3C 3A7

Received 14 April 2015; Accepted 1 July 2015

Academic Editor: Moreno Marzolla

Copyright © 2015 David Couturier and Michel R. Dagenais. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As computation schemes evolve and many new tools become available to programmers to enhance the performance of their applications, many programmers started to look towards highly parallel platforms such as Graphical Processing Unit (GPU). Offloading computations that can take advantage of the architecture of the GPU is a technique that has proven fruitful in recent years. This technology enhances the speed and responsiveness of applications. Also, as a side effect, it reduces the power requirements for those applications and therefore extends portable devices battery life and helps computing clusters to run more power efficiently. Many performance analysis tools such as LTTng, strace and SystemTap already allow Central Processing Unit (CPU) tracing and help programmers to use CPU resources more efficiently. On the GPU side, different tools such as Nvidia’s Nsight, AMD’s CodeXL, and third party TAU and VampirTrace allow tracing Application Programming Interface (API) calls and OpenCL kernel execution. These tools are useful but are completely separate, and none of them allow a unified CPU-GPU tracing experience. We propose an extension to the existing scalable and highly efficient LTTng tracing platform to allow unified tracing of GPU along with CPU’s full tracing capabilities.