Atharva Gondhalekar

Sparsity-aware Fine-Grained Parallelization for Set Intersection Computations in Graph Datasets

A fine-grained GPU parallelization approach that adapts to workload characteristics by assigning set intersection computations within a graph to the most suitable parallelization strategies, and predicts the optimal parallelization approach between this fine-grained and other state-of-the-art GPU kernels with 88% accuracy.