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