A fine-grained performance model for GPU architectures

The increasing programmability, performance, and cost/effectiveness of GPUs have led to a widespread use of such many-core architectures to accelerate general purpose applications. Nevertheless, tuning applications to efficiently exploit the GPU potentiality is a very challenging task, especially for inexperienced programmers. This is due to the difficulty of developing a SW application for the specific GPU architectural configuration, which includes managing the memory hierarchy and optimizing the execution of thousands of concurrent threads while maintaining the semantic correctness of the application. Even though several profiling tools exist, which provide programmers with a large number of metrics and measurements, it is often difficult to interpret such information for effectively tuning the application. This paper presents a performance model that allows accurately estimating the potential performance of the application under tuning on a given GPU device and, at the same time, it provides programmers with interpretable profiling hints. The paper shows the results obtained by applying the proposed model for profiling commonly used primitives and real codes.