CREPE: Concurrent Reverse-Modulo-Scheduling and Placement for CGRAs

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-03-16 DOI:10.1109/TPDS.2024.3402098
Chilankamol Sunny;Satyajit Das;Kevin J. M. Martin;Philippe Coussy
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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.
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CREPE:针对 CGRA 的并行反向模式调度和布局
粗粒度可重构阵列(CGRA)架构作为高性能、高能效的计算设备广受欢迎。通过对最内层循环数据流图(DFG)进行模数调度,可将复杂应用的计算密集型循环结构映射到 CGRA 上。在最先进的方法中,映射质量通常由启动间隔 (II) 决定,而忽略了一次迭代的调度长度。然而,对于嵌套循环,计划长度变得非常重要。在本文中,我们提出了一种用于 CGRA 的并发反向模态调度和布局技术 CREPE,它能同时最小化 II 和调度长度。CREPE 结合动态图变换同时执行模化调度和布局,可生成高质量、高成功率的映射。此外,我们还引入了一种编译流程,将嵌套循环映射到 CGRA 上,并使用 CREPE 对最内层的循环进行模块化调度。实验表明,所提出的解决方案在映射成功率和总执行时间上都优于传统方法,而且对编译时间没有影响。CREPE 映射了所考虑的所有内核,而最先进的技术 Crimson 和 Epimap 却未能找到映射,或者映射的 IIs 非常高。在 2×4 CGRA 上,CREPE 的成功率为 100%,速度分别比 Crimson(78.5%)和 Epimap(46.4%)快 5.9 倍和 1.4 倍。
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
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.
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