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

物理学家、气象学家和其他高性能计算用户希望编写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，并探索难题的解决方案。不幸的是，为了有效地利用计算资源，非计算专家需要成为一名熟练的计算机科学家。根本的问题是，非计算专家不一定知道优化他们的模型的可用选项。领域特定语言(DSL)是这种知识鸿沟的一种可能解决方案，因为领域特定语言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编译器的责任是将模型映射到高性能目标上。这项研究将产生新的DSL技术和技术，将大大降低开发这种高性能DSL的成本，并改善向用户提供的性能机会。在该项目的五年跨度中，两个特定的高性能并行平台(GPGPU和FGA)将变得更容易获得。资源感知的DSL将允许直接表达广泛的并行编程习惯用法。这种资源感知的DSL还将与现有的DSL结合使用。随着重写技术的使用，资源感知的DSL将作为一种富有表现力的公共媒介，用于在承诺特定实现技术之前、期间和之后探索设计权衡。一套定制的编译器插件将允许翻译到现有的开发环境和工具，用于GPGPU和FGA。随着编程继续从一般程序员的任务转移到领域专家手中，这项研究的更广泛影响是降低了领域专家与性能相关的知识负担，更广泛地说，通过推动最先进的DSL设计和实现来支持专家的使用。这项研究的主要智力价值在于创建通用DSL的挑战，该DSL支持对用户模型进行后自组织和资源感知的动态重构。这一新的DSL将为未来的高性能DSL编译器提供信息，并为定制DSL和领域专家合作寻找正确和快速执行的实现打开机会。

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.