Chilankamol Sunny;Satyajit Das;Kevin J. M. Martin;Philippe Coussy
{"title":"CREPE: Concurrent Reverse-Modulo-Scheduling and Placement for CGRAs","authors":"Chilankamol Sunny;Satyajit Das;Kevin J. M. Martin;Philippe Coussy","doi":"10.1109/TPDS.2024.3402098","DOIUrl":null,"url":null,"abstract":"Coarse-Grained Reconfigurable Array (CGRA) architectures are popular as high-performance and energy-efficient computing devices. Compute-intensive loop constructs of complex applications are mapped onto CGRAs by modulo-scheduling the innermost loop dataflow graph (DFG). In the state-of-the-art approaches, mapping quality is typically determined by initiation interval (\n<italic>II</i>\n), while \n<italic>schedule length</i>\n for one iteration is neglected. However, for nested loops, \n<italic>schedule length</i>\n becomes important. In this article, we propose CREPE, a \n<bold>C</b>\noncurrent \n<bold>Re</b>\nverse-modulo-scheduling and \n<bold>P</b>\nlac\n<bold>e</b>\nment technique for CGRAs that minimizes both \n<italic>II</i>\n and \n<italic>schedule length</i>\n. CREPE performs simultaneous modulo-scheduling and placement coupled with dynamic graph transformations, generating good-quality mappings with high success rates. Furthermore, we introduce a compilation flow that maps nested loops onto the CGRA and modulo-schedules the innermost loop using CREPE. Experiments show that the proposed solution outperforms the conventional approaches in mapping success rate and total execution time with no impact on the compilation time. CREPE maps all kernels considered while state-of-the-art techniques Crimson and Epimap failed to find a mapping or mapped at very high \n<italic>II</i>\ns. On a 2×4 CGRA, CREPE reports a 100% success rate and a speed-up up to 5.9× and 1.4× over Crimson with 78.5% and Epimap with 46.4% success rates respectively.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Parallel and Distributed Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10531698/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Coarse-Grained Reconfigurable Array (CGRA) architectures are popular as high-performance and energy-efficient computing devices. Compute-intensive loop constructs of complex applications are mapped onto CGRAs by modulo-scheduling the innermost loop dataflow graph (DFG). In the state-of-the-art approaches, mapping quality is typically determined by initiation interval (
II
), while
schedule length
for one iteration is neglected. However, for nested loops,
schedule length
becomes important. In this article, we propose CREPE, a
C
oncurrent
Re
verse-modulo-scheduling and
P
lac
e
ment technique for CGRAs that minimizes both
II
and
schedule length
. CREPE performs simultaneous modulo-scheduling and placement coupled with dynamic graph transformations, generating good-quality mappings with high success rates. Furthermore, we introduce a compilation flow that maps nested loops onto the CGRA and modulo-schedules the innermost loop using CREPE. Experiments show that the proposed solution outperforms the conventional approaches in mapping success rate and total execution time with no impact on the compilation time. CREPE maps all kernels considered while state-of-the-art techniques Crimson and Epimap failed to find a mapping or mapped at very high
II
s. On a 2×4 CGRA, CREPE reports a 100% success rate and a speed-up up to 5.9× and 1.4× over Crimson with 78.5% and Epimap with 46.4% success rates respectively.
期刊介绍:
IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to:
a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing.
b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems.
c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation.
d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.