DMCA
Array Optimizations for Parallel Implementations of High Productivity Languages
BibTeX
@MISC{Joyner_arrayoptimizations,
author = {Mackale Joyner and Vivek Sarkar and Rui Zhang},
title = {Array Optimizations for Parallel Implementations of High Productivity Languages},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
DARPA’s HPCS program has set a goal of bringing high pro-ductivity to high-performance computing. This has resulted in the creation of three new high-level languages, namely Chapel, Fortress and X10, that have successfully addressed one aspect of productivity: programmability. Unfortunately, the current state of the art in implementation of these high-level language concepts result in significant performance overheads. Our research addresses this issue by concentrat-ing on the second aspect of productivity: performance. This paper presents an interprocedural rank analysis al-gorithm that is capable of automatically inferring ranks of the arrays in X10, a language that allows rank-independent specification of loop and array computations using regions and points. Further, it uses the rank analysis information
Keyphrases
high productivity language parallel implementation array optimization darpa hpcs program rank-independent specification second aspect significant performance overhead array computation new high-level language current state high-performance computing high-level language concept high pro-ductivity rank analysis information interprocedural rank analysis al-gorithm
