Kernel concurrency opportunities based on GPU benchmarks characterization

Graphical Processing Units (GPUs) became an important platform to general purpose computing, thanks to their high performance and low cost when compared to CPUs. Modern GPU architectures are constantly evolving with growing resources. In order to take advantage of all the resources available and increase the GPU efficiency, new generation GPUs include support for concurrent kernel execution. Different kernels can be executed at the same time and share the GPU resources. Thus, benchmark suites developed to evaluate GPU performance and scalability should take this aspect into account that could be quite different from traditional CPU benchmarks. Nowadays, SHOC, Parboil, and Rodinia are the main benchmark suites for evaluating GPUs. This work analyzes these benchmark suites in a novel way. We propose to categorize the kernels of each application of these benchmarks by multiple criteria, built on their behavior in terms of computation type (integer or float), usage of memory hierarchy, efficiency and hardware occupancy. Based on the characterization results, we analyze kernel concurrency opportunities. The focus is on disclosing the resource requirements of the kernels of these benchmarks and to explain their behavior when executed concurrently.