2017/10/24 by Jason Sewall, Sewall, Jason, S. J. Pennycook +1
Computer Science · #Advanced Data Storage Technologies #Distributed #Embedded Systems Design Techniques #FOS: Computer and information sciences #Parallel #Parallel Computing and Optimization Techniques #Performance (cs.PF) #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1710.08774
openalex publication_date 2017/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a technique for automatically transforming kernel-based computations in disparate, nested loops into a fused, vectorized form that can reduce intermediate storage needs and lead to improved performance on contemporary hardware. We introduce representations for the abstract relationships and data dependencies of kernels in loop nests and algorithms for manipulating them into more efficient form; we similarly introduce techniques for determining data access patterns for stencil-like array accesses and show how this can be used to elide storage and improve vectorization. We discuss our prototype implementation of these ideas---named HFAV---and its use of a declarative, inference-based front-end to drive transformations, and we present results for some prominent codes in HPC.