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

众核处理器（如GPU）已被用于加速各种各样的应用程序：分子动力学，图像处理，数据挖掘，期权定价和线性代数等。尽管它们被广泛采用，但这些设备仍然被认为相对难以使用，因为它们需要程序员熟悉并行编程和硬件操作。特别是，在众核设备上有效部署非常规应用程序仍然远未被理解。然而，许多已建立和新兴的应用程序（来自社交和计算机网络，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，基于图形和树等数据结构。本研究提出了编译器和运行时技术，以支持部署的图形和其他不规则的应用程序的众核处理器，同时隐藏的程序员的复杂性和异构性的底层硬件和软件栈。由于不规则的应用程序的并行度在很大程度上依赖于数据，所提出的编译器技术的目的是从高层次的平台无关的算法描述开始生成多个平台特定的代码变体。运行时技术的重点是选择最合适的代码变体，并将其调整到硬件和输入数据集。更具体地说，这项研究涵盖了三个重要问题有关的不规则应用程序：（i）有效处理嵌套并行（以可并行嵌套循环和递归函数的形式）在不规则应用程序中;（ii）设计动态存储器分配库，其可以扩展到由众核设备提供的多线程的程度，和图形编码方案，适用于动态数据集上运行的应用程序;（iii）有效处理众核设备上的同步。

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.