CAREER: Filling the Gaps in Domain-Specific Functional-Based Solutions for High-Performance Execution

职业：填补特定领域的基于功能的高性能执行解决方案的空白

• 批准号: 1350901
• 负责人: Andrew Gill
• 金额: $ 52.12万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1350901&HistoricalAwards=false
• 关键词: CAREER; Filling; Gaps; Domain; Specific

Physicists, meteorologists, and other users of high-performance computing want to write large and complicated models and systems that execute correctly and quickly. These models and systems are allowing us to understand our world better, and explore solutions to hard problems. Unfortunately, to make efficient use of computing resources, a non-computing specialist needs to, for all intents and purposes, become a proficient computer scientist. The fundamental problem is that a non-computing specialist is not necessarily aware of available options for optimizing their model. Domain Specific Languages (DSLs) are one possible solution to this knowledge gap, because DSLs can encode the necessary knowledge required to map programs onto high-performance targets, at the same time to provide a customized environment in the parlance of the domain specialist. By utilizing a DSL, the non-specialist does not need to worry about performance at the outset; it is the responsibility of the DSL compiler to map the model onto the high-performance target. This research will produce new DSL techniques and technologies that will substantially lower the cost of developing such high-performance DSLs, as well as improve the performance opportunities offered to users.Over the five-year span of the project, two specific high-performance parallel platforms (GPGPUs and FPGAs) will be made more accessible. A resource-aware DSL will allow a broad range of parallel programming idioms to be directly expressed. This resource-aware DSL will also work in combination with existing DSLs. With the use of rewriting technologies, the resource-aware DSL will act as an expressive common medium for exploring design tradeoffs before, during and after the commitment to specific implementation technologies. A set of custom compiler plugins will allow translation into existing development environments and tools for GPGPUs and FPGAs. As programming continues to migrate from being the task of the general programmer into the hands of domain specialists, the broader impact of this research is lowering the performance-related knowledge burden on the domain specialist, and more generally, supporting the use of specialists by pushing the state-of-the-art in DSL design and implementation. The principal intellectual merit of this research is the challenge of creating the generic DSL that supports post-hoc and resource-aware on-the-fly refactoring on user models. This new DSL will inform future high-performance DSL compilers, and open opportunities for customized DSLs and the domain specialist working together to find implementations that execute correctly and quickly.

物理学家、气象学家和其他高性能计算的用户都希望编写出能够正确快速执行的大型复杂模型和系统。这些模型和系统使我们能够更好地了解我们的世界，并探索解决难题的方法。不幸的是，为了有效地利用计算资源，一个非计算专家需要成为一个熟练的计算机科学家。根本问题是，非计算专家不一定知道优化模型的可用选项。领域特定语言（DSL）是解决这一知识差距的一种可能的解决方案，因为DSL可以编码将程序映射到高性能目标所需的必要知识，同时提供领域专家所说的定制环境。通过使用DSL，非专业人员一开始就不需要担心性能; DSL编译器负责将模型映射到高性能目标。这项研究将产生新的DSL技术和技术，大大降低开发这种高性能DSL的成本，并提高为用户提供的性能机会。在该项目的五年跨度内，两个特定的高性能并行平台（GPGPU和FPGA）将变得更容易访问。资源感知的DSL将允许直接表达广泛的并行编程习惯用法。这种资源感知DSL也将与现有DSL结合使用。随着重写技术的使用，资源感知DSL将作为一个表达的共同媒介，探索设计权衡之前，期间和之后的承诺，以具体的实现技术。一组自定义编译器插件将允许转换为GPGPU和FPGA的现有开发环境和工具。随着编程继续从一般程序员的任务迁移到领域专家手中，本研究的更广泛影响是降低领域专家的性能相关知识负担，更普遍地说，通过推动DSL设计和实现的最新技术来支持专家的使用。这项研究的主要智力价值是创建通用DSL的挑战，支持事后和资源感知的用户模型上的动态重构。这种新的DSL将为未来的高性能DSL编译器提供信息，并为定制的DSL和领域专家提供机会，共同寻找正确快速执行的实现。