CAREER: Compiler and Runtime Support for Irregular Applications on Many-core Processors

多核处理器（如gpu）已被用于加速各种各样的应用：分子动力学、图像处理、数据挖掘、期权定价和线性代数等。尽管这些设备被广泛采用，但它们仍然被认为相对难以使用，因为它们要求程序员既熟悉并行编程，又熟悉硬件的操作。特别是，如何在多核设备上有效地部署不规则应用程序，还远未得到理解。然而，许多已建立的和新兴的应用程序（来自社会和计算机网络、电路建模、离散事件仿真、编译器和计算科学）本质上是不规则的，基于诸如图和树之类的数据结构。本研究提出了编译器和运行时技术，以支持在多核处理器上部署图形和其他不规则应用程序，同时向程序员隐藏底层硬件和软件堆栈的复杂性和异质性。由于不规则应用程序中的并行度在很大程度上依赖于数据，因此建议的编译器技术旨在从高级平台无关的算法描述开始生成多个特定于平台的代码变体。运行时技术侧重于选择最合适的代码变体，并根据硬件和输入数据集对其进行调优。更具体地说，本研究涵盖了与不规则应用程序相关的三个重要问题：(i)在不规则应用程序中有效处理嵌套并行性（以可并行嵌套循环和递归函数的形式）；（ii）设计一个动态内存分配库，可扩展至多核设备提供的多线程程度，以及设计适合在动态数据集上运行的应用程序的图形编码方案；（iii）在多核设备上有效地处理同步。

Many-core processors (such as GPUs) have been used to accelerate a wide variety of applications: molecular dynamics, image processing, data mining, option pricing and linear algebra, among others. Despite their widespread adoption, these devices are still considered relatively difficult to use, in that they require the programmer to be familiar both with parallel programming and with the operation of the hardware. In particular, the effective deployment of irregular applications on many-core devices is still far from understood. However, many established and emerging applications (from social and computer networking, electrical circuit modeling, discrete event simulation, compilers, and computational sciences) are irregular in nature, being based on data structures such as graphs and trees. This research proposes compiler and runtime techniques to support the deployment of graph and other irregular applications on many-core processors, while hiding from the programmer the complexity and heterogeneity of the underlying hardware and software stack. Since the degree of parallelism within irregular applications is heavily data dependent, the proposed compiler techniques aim to generate multiple platform-specific code variants starting from high-level platform-agnostic algorithmic descriptions. The runtime techniques focus on the selection of the most appropriate code variant and its tuning to the hardware and the input datasets. More specifically, this research covers three important issues related to irregular applications: (i) the effective handling of nested parallelism (in the form of parallelizable nested loops and recursive functions) within irregular applications; (ii) the design of a dynamic memory allocation library that can scale to the degree of multithreading offered by many-core devices, and of graph encoding schemes suitable for applications operating on dynamic datasets; and (iii) the effective handling of synchronization on many-core devices.