Chapel for Parallel AND Distributed GPU Computing: A Case Study with Jaccard Similarity

Le résumé fourni par la source

This work explores the efficacy of the Chapel programming language’s GPU support for implementing irregular distributed GPU graph applications. Chapel’s partitioned global address space (PGAS) model provides a cohesive way to target distributed nodes, CPU concurrency and task parallelism, and both CPU and GPU single instruction, multiple data/thread (SIMD/SIMT) kernels. In contrast, dominant high-performance computing approaches often require interoperation of multiple programming models for inter- and intra-node operations. This work considers developer and performance impacts of using Chapel over such traditional approaches.

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