Abstract
Graph algorithms are widely used in several application domains. It has been established that parallelizing graph algorithms is challenging. The parallelization issues get exacerbated when graphics processing unit (GPU) is used to execute graph algorithms. In particular, three important GPU-specific aspects affect performance: memory coalescing, memory latency, and thread divergence. We attempt to tame these challenges using approximate computing. We target graph applications on GPUs that can tolerate some degradation in the quality of the output, in exchange for obtaining the result faster. We propose three techniques for boosting the performance of graph processing on the GPU by injecting approximations in a controlled manner. The first one creates a graph isomorph that brings relevant nodes nearby in memory and adds a controlled replica of nodes to improve coalescing. The second reduces memory latency by facilitating the processing of subgraphs inside shared memory by adding edges among specific nodes and processing well-connected subgraphs iteratively inside shared memory. The third technique normalizes degrees across nodes assigned to a warp to reduce thread divergence. Each technique offers notable performance benefits and provides a knob to control inaccuracy added to an execution. We demonstrate the effectiveness of the proposed techniques using a suite of large graphs with varied characteristics and five popular graph algorithms.

Bio
Somesh Singh is a Ph.D. candidate in the Dept. of CSE at the Indian Institute of Technology Madras, India. His research interests span the areas of high-performance computing, parallel computing, and graph analytics. His dissertation research focuses on designing techniques for improving the efficiency of parallel graph analytics on graphics processing unit (GPU) by trading-off computational accuracy. His PhD works have been accepted for publication at ICPP 2020, PPoPP 2019 (poster) and TMSCS 2018. He was a research intern at Intel Labs, India  and Microsoft Research, India in 2020. He was a Google Summer of Code participant with CERN-HSF in 2017 and 2018.