XPS: FULL: FP: Collaborative Research: Taming parallelism: optimally exploiting high-throughput parallel architectures

标题：XPS：Full：FP：协作研究：驯服并行性：以最佳方式利用高吞吐量并行体系结构在过去十年中，计算机制造商一直专注于生产“多核”芯片，即在单个芯片上封装多个强大的计算核心。研究人员投入了大量精力来开发编写能够在这些内核上高效运行的程序的方法。其基本思想是允许程序员使用高级编程模型编写程序，并依靠底层编译器和运行时系统在多核平台上高效地调度这些程序。然而，由于功率和散热方面的考虑，新兴的“面向吞吐量的”计算系统越来越依赖简单得多的计算核心来提供并行计算性能。这些核心比传统的多核效率高得多，可以提供更高的性能。许多领域的从业者--生物信息学、数据分析、机器学习等--都在部署这些系统，以利用它们的力量。不幸的是，现有的高级编程模型是针对多核芯片的，并且不能产生能够在这些新系统上有效运行的代码。因此，实践者被迫重写他们的应用程序，并进行艰苦的低级优化和调度。该项目将开发一些方案，使为多核系统编写的应用程序能够在面向吞吐量的处理器上高效运行。智能的优点是新颖的程序优化，它将面向多核的程序转换为有效地映射到面向吞吐量的处理器的形式，确保这些面向吞吐量的处理器不浪费计算资源的调度机制，以及确保有效使用这些机制的调度策略。该项目更广泛的意义和重要性在于，程序员将能够为传统的多核系统以及面向吞吐量的系统编写可移植、高性能和高能效的程序。此外，将使用高级编程模型对以吞吐量为导向的机器进行编程，从而大大减少许多科学和工程学科从业者的编程工作量。最后，推广工作通过为不同的学生群体提供培训和指导来加强项目。像Cilk这样的语言提供了对“动态多线程”的支持，这允许程序员识别他们程序中的所有并行性，同时依赖复杂的运行时系统在运行时将这种并行性映射到可用的并行执行硬件。然而，Cilk风格的执行不适合在SIMD单元、GPU和Xeon Phi中发现的基于向量的并行；向量并行需要找到在不同数据单元上执行的相同计算。该项目研究了一系列转换，将Cilk风格的程序转换为公开可向量化并行性的程序，从而允许动态多线程程序映射到新兴的面向吞吐量的体系结构。启用转换涉及通过识别在计算中的不同点执行的类似任务，将任务并行应用程序转换为数据并行应用程序。该项目开发了一系列调度机制和被证明有效的调度策略，以确保在面向吞吐量的体系结构上并行动态多线程应用是有效的。通过这种方式，该项目使可移植的应用程序能够在多核和基于向量的架构上高效运行。

Title: XPS: FULL: FP: Collaborative Research: Taming parallelism: Optimally exploiting high-throughput parallel architecturesOver the past decade, computer manufacturers have focused on producing "multicore" chips, that package multiple, powerful computing cores on a single chip. Researchers have invested significant effort in developing methods for writing programs that can run efficiently on these cores. The basic idea is to allow programmers to write programs using a high-level programming model and to rely on an underlying compiler and runtime system to efficiently schedule these programs on multicore platforms. However, due to power and heat dissipation concerns, emerging "throughput-oriented" computing systems increasingly rely on far simpler computing cores to deliver parallel computing performance. These cores are much more efficient than traditional multicores, and can deliver much higher performance. Practitioners across numerous fields -- bioinformatics, data analytics, machine learning, etc. -- are deploying these systems to harness their power. Unfortunately, existing high level programming models are targeted to multicore chips, and do not produce code that can run effectively on these new systems. As a result, practitioners are forced to rewrite their applications, with painstaking low-level optimization and scheduling. This project will develop schemes to adapt applications written for multicore systems to run efficiently on throughput-oriented processors. The intellectual merits are novel program optimizations that will transform multicore-oriented programs into forms that map efficiently to throughput-oriented processors, scheduling mechanisms that ensure that these throughput-oriented processors do not waste computational resources, and scheduling policies that ensure that the mechanisms are used effectively. The project's broader significance and importance are that programmers will be able to write portable, high-performant and energy-efficient programs for both traditional multicore systems as well as throughput-oriented systems. Moreover, high-level programming models will be used to program the throughput-oriented machines, thus leading to significant reduction of programming effort for practitioners in many science and engineering disciplines. Finally, outreach efforts enhance the project by providing training and mentoring to a diverse group of students.Languages like Cilk provide support for "dynamic multithreading", which allows programmers to identify all of the parallelism in their program, while relying on sophisticated runtime systems to map that parallelism to available parallel execution hardware at runtime. However, Cilk-style execution is inappropriate for the vector-based parallelism found in SIMD units, GPUs and the Xeon Phi; vector parallelism requires finding identical computations performed on different data units. This project investigates a series of transformations that will morph Cilk-style programs into programs that expose vectorizable parallelism, allowing dynamic multithreading programs to be mapped to emerging throughput-oriented architectures. The enabling transformation involves transforming task parallel applications into data-parallel applications by identifying similar tasks being performed at different points in the computation. This project develops a series of scheduling mechanisms and provably efficient scheduling policies that ensure that parallelizing dynamic multithreading applications on throughput-oriented architectures are effective. In this manner, this project enables portable applications that run efficiently both on multicores and on vector-based architectures.