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

SHF：小型：协作研究：加速数据转换：传感器的软件硬件堆栈

• 批准号: 1907863
• 负责人: Michela Becchi
• 金额: $ 25.8万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1907863&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Accelerated

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.

近年来，“大数据”和数据密集型计算的爆炸性上升。 许多在大型数据集上运行的科学和数据分析应用程序以其核心执行数据转换。 例如，许多基因组应用将DNA序列转化为蛋白质序列，并且必须对DNA测序仪产生的大量数据（PB）进行这种转换。最近的研究表明，流行的数据分析系统花费大量时间进行数据转换操作，例如数据压缩，解压缩，序列化，进行验证和误差校正。 尽管特定于应用程序的硬件加速器可能有用，但它们的狭窄适用性可以大大限制其影响。另一方面，在许多应用程序的核心上加速通用计算可能会产生更大的影响，不仅使现有的应用程序受益，而且使未来的应用受益。这项研究针对数据转换的一般加速问题。更具体地说，为了允许实用程序广度，该项目旨在提供软件硬件堆栈，以加速数据转换的核心计算抽象，即有限态态传感器。鉴于大数据计算的社会重要性，这项工作的更广泛的影响是将研究思想和技术吸收到科学基础上，以及它们对科学，工业和社会的广泛“大数据”应用的影响。此外，该项目允许学生直接体验如何将诸如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，应用和系统体系结构的元素。该研究调查了传感器计算机及其有效的实施及其有效的实施，其目的是依赖性能和能源效率效率的数据，以提供数据分析的效率和能源效果的数据分析。特别是，这项工作旨在通过将换能器程序映射到新兴的数据处理加速器中，将换能器理论降低为实际使用。为此，这项工作针对以下问题。首先，设计一个软件堆栈以将换能器映射到新型硬件加速器上。特别是，调查人员基于他们先前关于非结构化数据处理器的设计和实施的工作，这是一个新颖的硬件加速器，用于显示出高性能的数据转换器，但目前缺乏高级编程模型。实现此目标需要研究一组独立于平台的和平台特定的优化，旨在最小化代码大小，最大程度地减少内存利用率，并利用计算中固有的粗糙和细粒度的并行性。其次，根据软件堆栈设计中获得的见解，改进并扩展了基础硬件加速器。第三，扩展了传感器模型以表达流行数据分析系统中的全部数据转换。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。

项目成果

A GPU-accelerated Data Transformation Framework Rooted in Pushdown Transducers

• DOI: 10.1109/hipc56025.2022.00038
• 发表时间: 2022-12
• 期刊: 2022 IEEE 29th International Conference on High Performance Computing, Data, and Analytics (HiPC)
• 作者: Tri Nguyen;M. Becchi

Data Transformation Acceleration using Deterministic Finite-State Transducers

使用确定性有限状态传感器加速数据转换

• DOI: 10.1109/bigdata55660.2022.10020756
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Big Data (Big Data
• 作者: Nourian, Marziyeh;Nguyen, Tri;Chien, Andrew A.;Becchi, Michela
• 通讯作者: Becchi, Michela