CAREER: Closing the Memory Gap for Unstructured Applications

许多重要的非数字应用程序都是数据密集型应用程序；因此，随着未来处理器和内存速度之间的差距拉大，它们的性能将会下降。与科学工作负载不同，非数字应用程序执行的非结构化计算表现出较低的控制流和地址流可预测性，以及较低的指令和线程级并行性。这些属性限制了现有技术在非数字应用程序上提高内存性能的有效性。这一职业奖通过综合研究和教育议程解决了非数字应用程序的内存缺口问题。在研究中，将开发几种新的延迟容忍技术来针对非结构化计算。首先，将研究通过运行时配置文件信息引导预取插装的技术，以解决不可预测的程序行为。其次，将开发积极调度串行化内存操作的指针预取技术，以暴露指针密集型计算中的内存并行性。最后，我们将研究“代码空间”预取调度技术，以支持沿着非结构化计算中常见的多条执行路径进行预取。在教育方面，通过研究开发的主题和工具将被纳入强调“边做边学”理念的课程中。此外，还将设立本科生研究项目和研究生研讨会。总而言之，这些教育活动将培养出在实验系统方面拥有强大背景的学生。

Many important non-numeric applications are data intensive; hence, they will experience performance degradations as the gap between future processor and memory speeds widen. Unlike scientific workloads, non-numeric applications perform unstructured computations that exhibit low control-flow and address stream predictability, and low instruction and thread-level parallelism. These attributes limit the effectiveness of existing techniques for improving memory performance on non-numeric applications.This CAREER award addresses the memory gap problem for non-numeric applications through an integrated research and education agenda. In research, several novel latency tolerance techniques will be developed to target unstructured computations. First, techniques for guiding prefetch instrumentation through runtime profile information will be studies to address unpredictable program behavior. Second, pointer prefetching techniques that aggressively schedule serialized memory operations will be developed to expose memory parallelism in pointer-intensive computations. Finally, "code-space" prefetch scheduling techniques will be investigated to enable prefetching along the multiple paths of execution commonly found in unstructured computations. In education, the topics and tools developed through research will be integrated into a curriculum that emphasized a "learn-by-doing" philosophy. In addition, undergraduate research programs and graduate student workshops will be created. Together, these education activities will produce students with a strong background in experimental systems.