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EAGER: Towards Automated Characterization of the Data-Movement Complexity of Large Scale Analytics Applications

EAGER：实现大规模分析应用程序数据移动复杂性的自动表征

基本信息

批准号：
1645599
负责人：
Ponnuswamy Sadayappan
金额：
$ 30万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-15 至 2019-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1645599&HistoricalAwards=false
关键词：
EAGER Towards Automated Characterization Data

项目摘要

We have entered a new era where power/energy limitations have become fundamental drivers of technological trends. The cost in both time and energy for moving data from off-chip main memory to the processor is significantly higher than the cost of a double-precision floating-point computation. With future technologies, this ratio will only get worse. Therefore the characterization of the inherent data movement costs of algorithms is very important, and is particularly critical for large scale data-analytic applications. However, unlike the well-understood computational complexity of algorithms, the data movement complexity is known only for a small number of algorithms.Prior techniques for characterizing the data movement complexity of algorithms has either been restricted to subclasses of computations, or has required ad hoc manual reasoning. This project develops a scalable automated tool for analyzing the data movement complexity of arbitrary unstructured computations, expressed as computational directed acyclic graphs (CDAGs). The researchers explore several directions including out-of-core strategies, decomposition/recomposition of graphs, directional component analysis, and empirical function fitting, to address scalability challenges.

我们已经进入了一个新时代，电力/能源限制已成为技术趋势的基本驱动力。将数据从片外主存储器移动到处理器的时间和能量成本明显高于双精度浮点计算的成本。随着未来技术的发展，这一比例只会变得更糟。因此，算法的固有数据移动成本的表征是非常重要的，并且对于大规模数据分析应用是特别关键的。然而，与众所周知的算法的计算复杂性不同，数据移动复杂性仅对于少数算法是已知的。用于表征算法的数据移动复杂性的现有技术要么被限制于计算的子类，要么需要专门的人工推理。该项目开发了一个可扩展的自动化工具，用于分析任意非结构化计算的数据移动复杂性，表示为计算有向无环图（CDAG）。研究人员探索了几个方向，包括核心外策略，图的分解/重组，方向分量分析和经验函数拟合，以解决可扩展性挑战。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Automated derivation of parametric data movement lower bounds for affine programs

自动推导仿射程序的参数数据移动下限

DOI：
10.1145/3385412.3385989
发表时间：
2020
期刊：
41st ACM SIGPLAN International Conference on Programming Language Design and Implementation
影响因子：
0
作者：
Olivry, Auguste;Langou, Julien;Pouchet, Louis-Noël;Sadayappan, P.;Rastello, Fabrice
通讯作者：
Rastello, Fabrice

Characterization of Data Movement Requirements for Sparse Matrix Computations on GPUs

GPU 上稀疏矩阵计算的数据移动要求的表征

DOI：
10.1109/hipc.2017.00040
发表时间：
2017
期刊：
24th IEEE International conference on High Performance Computing (HIPC
影响因子：
0
作者：
Kurt, Sireyya Emre;Thumma, Vineeth;Hong, Changwan;Sukumaran-Rajam, Aravind;Sadayappan, P.
通讯作者：
Sadayappan, P.

ColdRoute: effective routing of cold questions in stack exchange sites

DOI：
10.1007/s10618-018-0577-7
发表时间：
2018-06
期刊：
Data Mining and Knowledge Discovery
影响因子：
4.8
作者：
Jiankai Sun-;Abhinav Vishnu;Aniket Chakrabarti;Charles Siegel;S. Parthasarathy
通讯作者：
Jiankai Sun-;Abhinav Vishnu;Aniket Chakrabarti;Charles Siegel;S. Parthasarathy

ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation

DOI：
10.1609/aaai.v33i01.3301265
发表时间：
2018-11
期刊：
影响因子：
0
作者：
Jiankai Sun-;Bortik Bandyopadhyay;Armin Bashizade;Jiongqian Liang;P. Sadayappan;S. Parthasarathy
通讯作者：
Jiankai Sun-;Bortik Bandyopadhyay;Armin Bashizade;Jiongqian Liang;P. Sadayappan;S. Parthasarathy

Symmetrization for Embedding Directed Graphs

嵌入有向图的对称化