SHF: Small: Collaborative Research: Accelerated Data Transformation: A Software-Hardware Stack for Transducers

近年来，“大数据”和数据密集型计算爆炸式增长。许多在大型数据集上运行的科学和数据分析应用程序在其核心执行数据转换。例如，许多基因组学应用程序将DNA序列翻译成蛋白质序列，并且必须在DNA测序仪生成的大量数据（pb）上执行这种转换。最近的研究表明，流行的数据分析系统花费大量时间执行数据转换操作，如数据压缩、解压缩、序列化、反序列化和纠错。虽然特定于应用程序的硬件加速器可能很有用，但它们狭窄的适用性极大地限制了它们的影响。另一方面，加速许多应用程序核心的通用计算可以产生更广泛的影响，不仅有利于现有的应用程序，也有利于未来的应用程序。本研究针对数据转换的一般加速问题。更具体地说，为了允许更广泛的效用，该项目旨在提供一个软件-硬件堆栈，以加速数据转换核心的计算抽象，即有限状态传感器。考虑到大数据计算的社会重要性，这项工作的一个重要的更广泛的影响是将研究思想和技术引入科学基础，并由此对科学、工业和社会的广泛“大数据”应用产生影响。此外，该项目让学生亲身体验如何将有限状态传感器等抽象概念应用于实际问题，连接计算理论，算法设计和优化，应用程序和系统架构的元素。该研究调查了换能器的计算模型及其有效实现，目标是在数据分析系统中提供性能和能源效率的提高，所有这些都依赖于数据转换。特别是，这项工作旨在通过将传感器程序映射到新兴的数据处理加速器上，将传感器理论减少到实际应用。为此，本工作主要针对以下几个问题：首先，设计一个软件堆栈，将传感器映射到新型硬件加速器上。特别是，研究人员建立在他们之前设计和实现非结构化数据处理器的工作基础上，非结构化数据处理器是一种新型的硬件加速器，用于数据转换，显示出高性能，但目前缺乏高级编程模型。要实现这一目标，需要研究一组独立于平台和特定于平台的优化，这些优化旨在最小化代码大小、最小化内存利用率，并利用计算中固有的粗粒度和细粒度并行性。其次，基于在软件栈设计中获得的见解，改进和扩展底层硬件加速器。第三，扩展换能器模型，以表达流行数据分析系统中的所有数据转换。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Recent years have seen an explosive rise of "big data" and data-intensive computing. Many scientific and data analytics applications that operate on large data sets perform data transformation at their core. For example, many genomics applications translate DNA sequences into protein sequences and must perform this transformation on large volumes of data (petabytes) generated by DNA sequencers. Recent studies have shown that popular data analytics systems spend significant amount of time performing data transformation operations such as data compression, decompression, serialization, deserialization and error correction. While application-specific hardware accelerators can be useful, their narrow applicability can significantly limit their impact. On the other hand, accelerating a common computation at the core of many applications can have a broader impact, and benefit not only existing, but also future applications. This research targets the problem of general acceleration of data transformation. More specifically, to allow breadth of utility, the project aims to provide a software-hardware stack to accelerate the computational abstraction at the core of data transformation, namely, finite-state transducers. Given the societal importance of big data computing, a significant broader impact of this work is the uptake of research ideas and technology into the scientific base, and their resulting impact on a wide range of 'big data' applications for science, industry, and society. In addition, this project allows students to experience in first hand how abstract concepts such as finite-state transducers can be applied to practical problems, connecting elements of theory of computation, algorithm design and optimization, applications and systems architecture.The research investigates the transducers computational model and its efficient implementation with the goal of providing performance and energy-efficiency gains in data analytics systems all of which rely on data transformation. In particular, this work aims to reduce transducer theory to practical use by mapping transducer programs onto emerging data processing accelerators. To this end, this work targets the following issues. First, design a software stack to map transducers onto novel hardware accelerators. In particular, the investigators build on their previous work on the design and implementation of the Unstructured Data Processor, a novel hardware accelerator for data transformation shown to give high performance, but that at present lacks a high-level programming model. Accomplishing this goal requires investigating a set of platform-independent and platform-specific optimizations aimed to minimize the code size, minimize the memory utilization, and leverage the coarse- and fine-grained parallelism inherent in the computation. Second, improve and extend the underlying hardware accelerator based on the insights acquired in the design of the software stack. Third, extend the transducer model to express the full range of data transformations in popular data analytics systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.