CAREER: Reliable and Efficient Data Encoding for Extreme-Scale Simulation and Analysis

职业：用于超大规模仿真和分析的可靠且高效的数据编码

基本信息

批准号：
1751143
负责人：
Seung Woo Son
金额：
$ 50万
依托单位：
University of Massachusetts Lowell
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-15 至 2024-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1751143&HistoricalAwards=false
关键词：
CAREER Reliable Efficient Data Encoding

项目摘要

Transformative research in science and engineering to address challenges of our time, such as designing new combustion systems, depends on progressively sophisticated computational models and simulations that operate on high performance computing systems. These simulations and analyses are increasingly constrained by the massive volumes of data that they must use, generate, and analyze. To manage this enormous amount of data, this project explores innovative mechanisms to optimize the performance of these simulations by reducing data movement and maximizing the use of computing power, while minimizing errors and information loss. Such performance improvements support NSF's mission to advance emerging, data-intensive science discovery and contribute to solving the world's most pressing and complex contemporary science and engineering problems. This project implements comprehensive outreach and education to train the next-generation of professional workers and researchers in the latest computing architectures and programming methodologies, and provides rich opportunities for student engagement, research, and employment. It leverages multiple campus and national resources and implements proven, research-based interventions to attract, retain, and educate female and underrepresented minority populations in computer engineering, which furthers the US national goal of increased participation in engineering. The research goal of this project is to adapt techniques and formats for compressing video data to the investigation of novel data encoding and decoding schemes to optimize data movement and computation in data-intensive simulation and analyses. Innovative new mechanisms have the potential to efficiently reduce the volume of data generated and transferred while also enabling rapid execution of various analysis kernels using compressed data, and permitting seamless scaling of their performance on current and future extreme-scale platforms. The research objectives are to investigate data encoding/decoding of scientific datasets and harness encoded data, employ and scale encoded datasets seamlessly within current extreme-scale scientific workflows, and optimize machine learning and data mining algorithms with the goal of maximizing the use of computing power while minimizing errors. These new mechanisms are applied to an evaluation framework and validated on multiple extreme-scale data-driven scientific applications, including climate, multiphysics, and fluid dynamics. This approach is expected to transform data representation and encoding while incurring minimal disturbance to existing applications, responding to the trends in hardware architecture and dataset characteristics. It is anticipated to improve the overall performance of computational scientists' workloads by reducing defensive and productive I/O costs, respectively, up to 100x and 200x data reduction spatially and temporally, potentially resulting in up to an overall 50x I/O cost improvement. The project leverages multiple collaborations in order to establish the governing principles for system co-design and scalable system software layers for better data encoding within world-class computational infrastructures. This project strengthens the University of Massachusetts Lowell computer engineering curriculum, broadens participation in computer engineering, and creates a collaborative, interdisciplinary research program geared toward exploiting ever-evolving computing paradigms.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.

在科学和工程方面，解决我们这个时代挑战（例如设计新燃烧系统）的变革性研究取决于在高性能计算系统上运行的逐步复杂的计算模型和模拟。这些模拟和分析越来越受其使用，生成和分析的大量数据的限制。为了管理这些大量数据，该项目通过减少数据移动并最大程度地利用计算能力来探讨创新机制，以优化这些模拟的性能，同时最大程度地减少错误和信息损失。这种绩效的改进支持NSF提高新兴，数据密集型科学发现的使命，并有助于解决世界上最紧迫，最复杂的当代科学和工程问题。该项目实施全面的外展和教育，以在最新的计算体系结构和编程方法中培训专业工作者和研究人员的下一代，并为学生参与，研究和就业提供丰富的机会。它利用了多个校园和国家资源，并实施了经过验证的基于研究的干预措施，以吸引，保留和教育女性和代表性不足的计算机工程中，这进一步推动了美国国民参与工程的目标。该项目的研究目标是调整技术和格式，以将视频数据压缩到对新型数据编码和解码方案的调查中，以优化数据密集型模拟和分析中的数据运动和计算。创新的新机制有可能有效地减少生成和传输的数据量，同时还可以使用压缩数据快速执行各种分析内核，并允许在当前和将来的极端规模平台上无缝缩放其性能。研究目的是在当前极端的科学工作流程中无缝地编码和尺度编码数据集，调查科学数据集和线束编码数据的数据编码/解码，并以最大程度地利用计算能力的方式，同时最大程度地减少错误的方式，以优化机器学习和数据挖掘算法。这些新机制应用于评估框架，并在多个极端数据驱动的科学应用程序（包括气候，多物理学和流体动力学）上进行了验证。预计这种方法将转换数据表示和编码，同时对现有应用程序产生最小的干扰，从而响应硬件体系结构和数据集特性的趋势。预计，通过分别降低防御性和生产力的I/O成本，可以在空间和时间上降低最高100倍和200倍的数据，从而提高计算科学家工作量的整体性能，从而可能导致总体上50倍I/O成本提高。该项目利用多个协作来建立系统共同设计和可扩展系统软件层的管理原理，以在世界一流的计算基础架构中进行更好的数据编码。该项目加强了马萨诸塞大学洛厄尔大学计算机工程课程，扩大了计算机工程的参与，并创建了一个合作的，跨学科的研究计划，旨在利用不断发展的计算范式。该奖项反映了NSF的法定任务，并通过使用基金会的智力效果进行评估，并通过评估范围来进行评估。