T. Aa, P. Raghavan, S. Mahlke, B. D. Sutter, Aviral Shrivastava, Frank Hannig
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引用次数: 2
摘要
粗粒度可重构阵列(CGRA)处理器通过利用指令级并行性(ILP)加速应用程序的内部循环,在某些情况下还利用数据级和任务级并行性(DLP和TLP)。本教程的目的是深入了解CGRA体系结构及其利用并行性的编译技术。这些主题将涵盖:·多态管道数组,扩展最内层循环之外的粗粒度数组(Scott Mahlke,密歇根大学)·粗粒度数组的代码生成:灵活性和程序员生产力(Bjorn De Sutter,根特大学)·CGRAs的内存感知编译技术(Aviral Shrivastava,亚利桑那州立大学)·粗粒度可重构数组上循环程序的可重定向映射(Frank Hannig,埃尔兰根-纽伦堡大学)。
Embedded tutorial — Compilation techniques for CGRAs: Exploring all parallelization approaches
Coarse-Grained Reconfigurable Array (CGRA) processors accelerate inner loops of applications by exploiting instructionlevel parallelism (ILP) and in some cases also data-level and task-level parallelism (DLP & TLP). The aim of this tutorial is to give insight in CGRA architectures and their compilation techniques to exploit parallelism. These topics will be covered: · Polymorphic pipeline arrays, expanding coarse-grained arrays beyond innermost loops (Scott Mahlke, University of Michigan) · Code-generation for coarse-grained arrays: flexibility and programmer productivity (Bjorn De Sutter, Ghent University) · Memory-aware compilation techniques for CGRAs (Aviral Shrivastava, Arizona State University) · Retargetable Mapping of Loop Programs on Coarse-grained Reconfigurable Arrays (Frank Hannig, University of Erlangen-Nuremberg).