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”和数据密集型计算呈爆炸式增长。 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序列转换为蛋白质序列，并且必须对DNA测序仪生成的大量数据（PB）执行此转换。最近的研究表明，流行的数据分析系统花费大量的时间执行数据转换操作，例如数据压缩、解压缩、序列化、重复化和纠错。 虽然特定于应用程序的硬件加速器可能很有用，但其狭窄的适用性可能会大大限制其影响。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，而且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，为了允许广泛的实用性，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纳入科学基础，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，该项目还允许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，算法设计和优化的元素，应用程序和系统架构。该研究调查了传感器计算模型及其有效的实现，其目标是提供性能和能量-数据分析系统的效率提高，所有这些都依赖于数据转换。特别是，这项工作的目的是减少传感器理论的实际应用映射到新兴的数据处理加速器的传感器程序。为此，这项工作针对以下问题。首先，设计一个软件堆栈，将传感器映射到新的硬件加速器上。特别是，研究人员建立在他们以前的工作的设计和实现的非结构化数据处理器，一种新的硬件加速器的数据转换显示，提供高性能，但目前缺乏一个高层次的编程模型。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，旨在最大限度地减少代码大小，最大限度地减少内存利用率，并利用计算中固有的粗粒度和细粒度并行性。第二，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，扩展传感器模型，以表达流行数据分析系统中的全方位数据转换。该奖项反映了NSF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。

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