SYgraph: Efficient Data Layout for Heterogeneous Parallel Graph Analytics

Graph analytics serves as an essential tool for modeling and exploring complex relationships in a variety of domains, including social networks, bioinformatics, and scientific computing. These applications often involve analyzing massive and intricate datasets, making it critical to optimize graph algorithms for modern hardware. However, achieving optimal performance on massively parallel architectures is a challenging task due to the memory-bound nature of graph computations and their inherently irregular workloads. Existing graph processing frameworks, such as Gunrock, Tigr, or SEP-Graph, have made strides in optimizing these workloads, but are predominantly designed for NVIDIA GPUs using CUDA. This design choice restricts their applicability in environments equipped with other high-performance hardware, such as AMD and Intel GPUs, which now power some of the world’s fastest supercomputers.