Pannotia: Understanding Irregular GPGPU Graph Applications

GPUs have become popular recently to accelerate general-purpose data-parallel applications. However, most existing work has focused on GPU-friendly applications with regular data structures and access patterns. While a few prior studies have shown that some irregular workloads can also achieve speedups on GPUs, this domain has not been investigated thoroughly. Graph applications are one such set of irregular workloads, used in many commercial and scientific domains. In particular, graph mining - as well as web and social network analysis-are promising applications that GPUs could accelerate. However, implementing and optimizing these graph algorithms on SIMD architectures is challenging because their data-dependent behavior results in significant branch and memory divergence. To address these concerns and facilitate research in this area, this paper presents and characterizes a suite of GPGPU graph applications, Pannotia, which is implemented in OpenCL and contains problems from diverse and important graph application domains. We perform a first-step characterization and analysis of these benchmarks and study their behavior on real hardware. We also use clustering analysis to illustrate the similarities and differences of the applications in the suite. Finally, we make architectural and scheduling suggestions that will improve their execution efficiency on GPUs.