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SHF: Medium: Scalable Holistic Autotuning for Software Analytics

SHF：中：用于软件分析的可扩展整体自动调整

基本信息

批准号：
1703487
负责人：
Timothy Menzies
金额：
$ 89.83万
依托单位：
North Carolina State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1703487&HistoricalAwards=false
关键词：
SHF Medium Scalable Holistic Autotuning

项目摘要

Software analytics distills large quantities of low-value data down to smaller sets of higher value data that shed important insights for software quality enhancement. It is essential for software effort estimation, risk management, defect prediction, project resource management and many other tasks. Software analytics is a complex, time-consuming process. Recent research has tried to alleviate the issue through intelligent optimizers that make better use of existing computational resources. The space of possible options for optimization is very large, and spans over multiple layers: all possible settings for algorithms, compilers, and execution time options. To complicate matters, there are many competing goals that could be used to guide that tuning; e.g. reducing CPU usage while increasing the predictive power of the learned model. Existing research has mainly focused on limited optimizers that explore just a few options at mostly one level while trying to improve on just one or two goals, leaving the large potential of optimizations untapped.This research proposes to advance the state of the art to holistic scalable intelligent optimization for software analytics (SHASA). SHASA tunes all levels of options for multiple optimization objectives at the same time. It achieves this ambitious goal through the development of a set of novel techniques that efficiently handle the tremendous tuning space. These techniques take advantage of the synergies between all those options and goals by exploiting relevancy filtering (to quickly dispose of unhelpful options), locality of inference (that enables faster updates to outdated tunings) and redundancy reduction (that reduces the search space for better tunings). This research will produce algorithms and tools that are demonstrably more useful and efficient for software analytics research. Those techniques are generalizable beyond software analytics for use in computational science and engineering at large. An important broader impact is minimizing CPU and memory usage, ultimately reducing energy consumption in data centers, as data analytics computations grown significantly in scale and become computationally more demanding.

软件分析将大量低价值数据提炼成较小的较高价值数据集，这些数据为软件质量改进提供了重要的见解。它对于软件工作量估计、风险管理、缺陷预测、项目资源管理和许多其他任务都是必不可少的。软件分析是一个非常复杂、耗时的过程。最近的研究试图通过更好地利用现有计算资源的智能优化器来缓解这个问题。用于优化的可能选项的空间非常大，并且跨越多个层：算法、编译器和执行时选项的所有可能设置。让事情变得复杂的是，有许多相互竞争的目标可以用来指导这种调整；例如，减少CPU使用量的同时增加学习模型的预测能力。现有的研究主要集中在有限的优化器上，这些优化器只在一个层面上探索少数几个选项，同时试图只在一个或两个目标上进行改进，而没有挖掘优化的巨大潜力。本研究建议将最新技术提升到软件分析的整体可伸缩智能优化(SHASA)。沙萨同时为多个优化目标调整所有级别的选项。它通过开发一套有效处理巨大调谐空间的新技术来实现这一雄心勃勃的目标。这些技术通过利用相关性过滤(以快速处理无用的选项)、推理的局部性(使过时的调整能够更快地更新)和冗余减少(减少搜索空间以获得更好的调整)来利用所有这些选项和目标之间的协同作用。这项研究将产生对软件分析研究明显更有用和更有效的算法和工具。这些技术不仅适用于软件分析，还适用于计算科学和工程。随着数据分析计算规模的显著增长和对计算的要求越来越高，一个重要的更广泛的影响是最大限度地减少CPU和内存的使用，最终降低数据中心的能源消耗。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

HARP: Holistic Analysis for Refactoring Python-Based Analytics Programs

DOI：
10.1145/3377811.3380434
发表时间：
2020-06
期刊：
2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE)
影响因子：
0
作者：
Weijie Zhou;Yue Zhao;Guoqiang Zhang;Xipeng Shen
通讯作者：
Weijie Zhou;Yue Zhao;Guoqiang Zhang;Xipeng Shen

Exploring Flexible Communications for Streamlining DNN Ensemble Training Pipelines

DOI：
10.1109/sc.2018.00067
发表时间：
2018-11
期刊：
SC18: International Conference for High Performance Computing, Networking, Storage and Analysis
影响因子：
0
作者：
Randall Pittman;Hui Guan;Xipeng Shen;Seung-Hwan Lim;R. Patton
通讯作者：
Randall Pittman;Hui Guan;Xipeng Shen;Seung-Hwan Lim;R. Patton

In-Place Zero-Space Memory Protection for CNN

CNN 的就地零空间内存保护