Evaluating Portable Programming Models for Hypergraph Label Propagation on GPUs

Understanding community formation is a fundamental task in network science, as it reveals the structural organization of complex networks and provides insights into the functional roles and interactions of their nodes. Recently, hypergraphs have emerged as a powerful framework for modeling high-order relationships in real-world systems, capturing multi-entity relations beyond traditional pairwise connections. However, efficiently processing hypergraphs on GPUs remains challenging due to their inherent sparsity and structural irregularity, leading to poor memory locality and load imbalance. Given that the world's most powerful supercomputers are equipped with GPUs from different vendors, such as AMD, Intel, and NVIDIA, a portable performance solution is essential to exploit these systems effectively without rewriting the entire codebase for each platform. In this work, we pursue this objective by evaluating three portable programming models, OpenMP, SYCL, and Kokkos, applied to the label propagation community detection algorithm for hypergraphs, examining their programmability and performance on heterogeneous GPU architectures.