CAREER: Staging Compilers for Heterogeneous Platforms

职业：异构平台的暂存编译器

基本信息

批准号：
1750399
负责人：
Louis-Noel Pouchet
金额：
$ 47.8万
依托单位：
Colorado State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-02-01 至 2024-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750399&HistoricalAwards=false
关键词：
CAREER Staging Compilers Heterogeneous Platforms

项目摘要

Power density and energy considerations have become the primary constraints driving technology directions for embedded, mainstream, as well as peta/exascale computing at the high end. Non-homogeneous CPU cores and increasingly complex System-on-Chips are on the roadmap of most manufacturers. In a word, computing platforms are now heterogeneous, after decades of mass marketing homogeneous single-core x86 processors. Optimizing compilers are a cornerstone of the software stack: they are in charge of producing high-quality machine-specific code from the input program. The current development model where either an application is manually tuned by expert engineers to the specifics of the new target platform, or simply left untuned and heavily under-utilizing the hardware resources is not sustainable. This project targets the design of a complete system to efficiently compile several key computation patterns to heterogeneous targets, from a single input source. The PI investigates how to automatically characterize the quality and performance of software transformation systems, so as to better exploit their strengths; and create new customized compilation techniques to produce optimized binaries for heterogeneous processors. In particular, the PI develops a novel system that automatically learns what types of programs an optimization tool (e.g., a vendor compiler) can optimize well, focusing on performance-critical loop-based program regions amenable to polyhedral compilation. By combining automatic benchmark generation and deep learning techniques, this system automatically builds a performance contract for the compiler: a program that meets specific syntactic and semantics restriction (the contract) is guaranteed to be well optimized by that compiler. Then, in order to best exploit such compilers, programs are automatically restructured to expose program sub-regions that meet the contract requirements. With the assistance of target-specific performance models, the best restructuring is chosen at compile-time for each hardware target. This system can then be applied at compile-time for various execution contexts (e.g., for different CPU frequencies, core counts, etc.), to deliver an adaptive binary where the best implementation is selected at run-time as a function of the execution context. The project aims to demonstrate how to best stage various compilers to exploit their strengths, in turn significantly reducing the time currently spent by developers to tune their implementation for better performance. Education material to be produced includes a lecture series for educators and students on how to write programs that compilers can optimize well, and a MOOC on polyhedral compilation, the mathematical framework to reason about programs that is central to this project.

功率密度和能量考虑因素已成为嵌入式，主流以及高端PETA/Exascale计算的主要限制。大多数制造商的路线图都在非殖民的CPU核心和日益复杂的系统上。一词中，经过数十年的大众营销均质单核X86处理器，计算平台现在是异质的。优化编译器是软件堆栈的基石：他们负责从输入程序中生成高质量的机器特定代码。当前的开发模型是，专家工程师手动调整了新目标平台的细节，或者简单地不张开且充分利用硬件资源是不可持续的。该项目以单个输入源将完整系统的设计定为完整系统的设计，以有效地将几种关键计算模式汇编为异构目标。 PI研究了如何自动表征软件转换系统的质量和性能，以更好地利用其优势；并创建新的自定义编译技术，以生成用于异质处理器的优化二进制文件。特别是，PI开发了一个新型系统，该系统会自动了解哪些类型的优化工具（例如，供应商编译器）可以很好地优化，重点是基于性能至关重要的循环的程序区域，可符合多面体汇编。通过结合自动基准生成和深度学习技术，该系统会自动为编译器建立绩效合同：符合特定句法和语义限制的程序（合同）可以通过该编译器进行了很好的优化。然后，为了最大程度地利用此类编译器，将自动重组程序以揭示满足合同要求的计划子区域。在特定于目标的性能模型的协助下，每个硬件目标的编译时间都会选择最佳的重组。然后，可以在编译时应用此系统，以适用于各种执行上下文（例如，对于不同的CPU频率，核心计数等），以提供自适应二进制文件，其中在运行时选择最佳实现作为执行上下文的函数。该项目旨在展示如何最好地登上各种编译器来利用其优势，从而大大减少开发人员目前花费的时间来调整其实施以提高绩效。要生产的教育材料包括一个讲座系列，旨在为教育者和学生讲授如何编写编译器可以优化的节目，以及关于多面体编译的MOOC，这是理论该项目核心的计划的数学框架。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

PALMED: Throughput Characterization for Superscalar Architectures

PALMED：超标量架构的吞吐量表征

DOI：
10.1109/cgo53902.2022.9741289
发表时间：
2022
期刊：
2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO
影响因子：
0
作者：
Derumigny, Nicolas;Bastian, Theophile;Gruber, Fabian;Iooss, Guillaume;Guillon, Christophe;Pouchet, Louis-Noel;Rastello, Fabrice
通讯作者：
Rastello, Fabrice

Data-flow/dependence profiling for structured transformations

结构化转换的数据流/依赖性分析

DOI：
10.1145/3293883.3295737
发表时间：
2019
期刊：
Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming
影响因子：
0
作者：
Gruber, Fabian;Selva, Manuel;Sampaio, Diogo;Guillon, Christophe;Moynault, Antoine;Pouchet, Louis-Noël;Rastello, Fabrice
通讯作者：
Rastello, Fabrice

Building a Polyhedral Representation from an Instrumented Execution: Making Dynamic Analyses of Nonaffine Programs Scalable

从仪表化执行构建多面体表示：使非仿射程序的动态分析可扩展

DOI：
10.1145/3363785
发表时间：
2020
期刊：
ACM Transactions on Architecture and Code Optimization
影响因子：
1.6
作者：
Selva, Manuel;Gruber, Fabian;Sampaio, Diogo;Guillon, Christophe;Pouchet, Louis-Noël;Rastello, Fabrice
通讯作者：
Rastello, Fabrice

Self-Supervised Learning to Prove Equivalence Between Programs via Semantics-Preserving Rewrite Rules

自监督学习通过保留语义的重写规则证明程序之间的等效性