SHF: Medium: Collaborative Research: An Inspector/Executor Compilation Framework for Irregular Applications

计算科学和工程提供了对物理现象和设计空间的廉价探索，并帮助指导实验和建议理论。不规则应用，如分子动力学模拟、n体模拟、有限元分析和大图形分析，构成了科学计算应用的关键和重要部分。不规则应用程序的特点是具有在编译应用程序时无法确定的间接内存访问，例如A[B[i]]，因此严重限制了并行编译器技术的大量工作的适用性。因此，不规则应用程序在推动科学前沿方面是如此重要，在为不断变化的并行架构开发高性能实现时，给计算和领域科学家带来了非常大的负担。这个项目的智力价值是为不规则应用程序开发一个编译器和运行时框架，特别适合于稀疏矩阵和图计算，这是计算科学和数据科学中关键问题的基础。更广泛的影响是为领域科学家提供了一个强大的工具，用于优化和移植对性能至关重要的不规则计算到当前和未来的多核处理器和多核加速器。ppi还将继续努力扩大和多样化，以增加STEM职业的参与，特别是女性和代表性不足的少数民族。本项目中的方法是扩展已建立的检查器/执行器范例，其中计算依赖结构（基于内存访问模式）在运行时确定，并且运行时信息传递给编译时生成的执行器。具体来说，检查器可以在运行时计算的早期检查内存访问模式，执行器利用这些信息执行数据和计算的重新排序和调度，以影响内存层次结构和并行性优化。该项目正在开发一个编译器和运行时框架，其中包含用于表达和操作检查器的新抽象；然后，这些检查器可以几乎无缝地相互集成，并与现有的编译器优化（例如，循环平铺）集成，以优化执行器。该项目还扩展了先前支持非仿射输入代码的工作，并混合了编译时和运行时优化。由此产生的系统提高了专业程序员在各种不规则应用程序上实现高性能和可移植性的生产力。

Computational science and engineering provides inexpensive exploration of physical phenomena and design spaces and helps direct experimentation and advise theory. Irregular applications such as molecular dynamics simulations, n-body simulations, finite element analysis, and big graph analytics constitute a critical and significant portion of scientific computing applications. An irregular application is characterized by having indirect memory accesses such as A[B[i]] that cannot be determined when the application is being compiled, therefore severely limiting the applicability of the large body of work on parallelizing compiler technology. Consequently, irregular applications, which are so important in pushing forward the frontiers of science, place a very large burden on computational and domain scientists in developing high-performance implementations for the ever-changing landscape of parallel architectures. The intellectual merit of this project is to develop a compiler and runtime framework for irregular applications, particularly well suited for sparse matrix and graph computations that underlie critical problems in computational science and data science. The broader impact is to provide domain scientists a powerful tool for optimizing and porting performance-critical, irregular computations to current and future multi-core processors and many-core accelerators. The PIs will also continue efforts in outreach and diversity to increase the participation in STEM careers, particularly among women and underrepresented minorities.The approach in this project is to extend the well-established inspector/executor paradigm where the computational dependence structure (based on the memory access pattern) is determined at runtime, and runtime information is passed to a compile-time generated executor. Specifically, an inspector can examine the memory access patterns early in the computation at runtime, and an executor leverages this information to perform data and computation reordering and scheduling to affect memory hierarchy and parallelism optimizations. The project is developing a compiler and runtime framework with new abstractions for expressing and manipulating inspectors; these inspectors may then be integrated nearly seamlessly with each other and with existing compiler optimizations (e.g., loop tiling) to optimize executors. The project is also extending prior work that supports non-affine input code and mixes compile-time and runtime optimization. The resulting system increases the productivity of expert programmers in achieving both high performance and portability on a wide variety of irregular applications.