CSR: Small: Adaptively Applying Data-Driven Execution Mode to Remove I/O Bottleneck for Data-Intensive Computing

越来越普遍的多核/多核CPU架构对于加速程序是有效的。只在保持足够的并行性时执行。对于数据密集型程序，增加的并行性可能会严重损害I/O效率：当一个顺序程序并行化时，不仅计算，而且与它们相关的I/O操作，都可能分布在多个进程之间。因为进程的执行顺序通常由运行时的调度器决定，所以进程的相对进度是不确定的，进程发出I/O请求的顺序也相应地是不确定的。正是这种I/O不确定性否定了并行执行的优势，从而极大地降低了I/O效率，从而降低了程序性能。为了解决这个问题，PI建议在操作系统内核或运行时中构建一个工具，以简化来自并行程序的不同进程的I/O请求的服务。与提高I/O性能的传统技术的主要区别在于通过I/O感知的进程调度来协调I/O请求的发出，以改善I/O密集型多线程和MPI程序的这些请求的局域性。如果成功，该研究将为以数据为中心的计算引入一种颠覆性技术，以有效缓解它们的i /O瓶颈。该项目还为学生，特别是密歇根东南部地区少数族裔学生提供了大量的研究培训机会，以帮助缓解在并行计算和存储系统方面具有专业知识的IT专业人员的短缺。

The increasingly common multi-core/many-core CPU architecturesare effective for accelerating programsâ?? execution only whensufficient parallelism is maintained. For data-intensiveprograms the increased parallelism can severely compromise I/Oefficiency: when a sequential program is parallelized, not onlycomputations, but also the I/O operations associated with them, canbe distributed among multiple processes. Because the executionorder of the processes is usually determined by the scheduler atruntime, the relative progress of processes is nondeterministicand the order in which the processes issue their I/O requests isaccordingly nondeterministic. It is this I/O nondeterminism thatcan substantially compromise I/O efficiency, and thus programperformance, by negating the advantages of parallel execution.To address this problem the PI proposes a facility built eitherin the operating system kernel or in the runtime to streamlinethe service of I/O requests from different processes of aparallel program. The major distinction from conventionaltechniques for improving I/O performance is in the coordinationof I/O request issuance, via I/O-aware process scheduling, toimprove the locality of these requests for I/O-intensivemultithreaded and MPI programs.If successful, the proposed research would introduce a disruptivetechnique for data-centric computing to effectively relieve theI/O bottleneck. This project also provides abundant researchtraining opportunities for students, especially under-representedminority students in the southeast Michigan area, to help relievethe shortage of IT professionals with expertise in parallelcomputing and storage systems.