Graphics Computing Units (GPUs) have evolved in the past decades to become a key element of many computing systems, due to their affordable raw computing power. However, they require intricate knowledge of their behavior to achieve the best performance their performance can offer. GPU developers usually rely on tools to analyze the performance of their code, but existing tools fall short as they are unable to provide detailed data from the device.

In this presentation, we explore tracing methods for GPUs compute kernels. We first discuss implementation methods for efficient tracing that alleviate tracing challenges. We then present possible tracing schemes for GPUs and study their performance on a GPU-accelerated computing benchmark. Lastly, we discuss the instrumentation challenges of SIMT code and how to best place tracepoints.