Collaborative Research: CIF: Small: Maximizing Coding Gain in Coded Computing

协作研究：CIF：小型：最大化编码计算中的编码增益

• 批准号: 2327510
• 负责人: Alexander Sprintson
• 金额: $ 22.5万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327510&HistoricalAwards=false
• 关键词: Collaborative; Research; CIF; Small; Maximizing

Artificial intelligence and machine learning algorithms rely on parallel, distributed computing systems to efficiently carry out intricate, data-heavy tasks. A significant challenge in designing large-scale distributed computing systems is addressing the unpredictable variations in service times across multiple servers. Computing redundancy, such as task replication, is a promising powerful tool to curtail the overall variability in service time. This project focuses on the intelligent management of redundancy in distributed computing that will affect the execution efficiency of data-intensive algorithms in large-scale systems. The project will quantify redundancy benefits, pivotal to developing and ultimately deploying efficient redundancy schemes for executing artificial intelligence and machine learning workloads. The educational goal of the project includes stimulating students' interest in applied probability and mathematical modeling and developing hands-on labs on cloud computing infrastructure. The project will contribute to the Research Experiences for Undergraduate and High School students and will recruit and mentor women and members of underrepresented groups.This project considers distributed computing systems that use replication and erasure coding to reduce job execution times. The project aims to maximize the gain of using computing redundancy (coding gain) in practical scenarios. It complements recent work on redundancy in distributed systems, focusing primarily on designing redundancy schemes using erasure codes. The project will use statistical analysis and queueing and coding theories to make the following contributions: (i) characterization of the crucial effects of using redundancy in distributed computing, including analysis of the benefits and costs of redundancy; (ii) new mathematical models that capture the performance of distributed computing systems with stragglers; (iii) new analysis tools for computing coding gain in coded computing systems; (iv) development of redundancy management algorithms; (v) characterization of the diversity vs. parallelism trade-off; and (vi) addressing other critical issues in coded computing that do not exist in the better-understood replication solutions.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.

人工智能和机器学习算法依赖于平行的分布式计算系统来有效执行复杂的数据繁重的任务。设计大规模分布式计算系统的一个重大挑战是解决多个服务器的服务时间的不可预测变化。计算冗余（例如任务复制）是一种有希望的强大工具，可降低服务时间的整体变异性。该项目着重于分布式计算中冗余的智能管理，这将影响大规模系统中数据密集型算法的执行效率。该项目将量化冗余好处，这对于开发并最终部署有效的冗余方案以执行人工智能和机器学习工作负载。该项目的教育目标包括刺激学生对应用概率和数学建模的兴趣，并在云计算基础架构上开发动手实验室。该项目将为本科和高中生的研究经验做出贡献，并将招募和指导妇女以及代表性不足的群体的成员。该项目认为分配了使用复制和删除编码来减少工作执行时间的分配计算系统。该项目旨在最大程度地利用在实际情况下使用计算冗余（编码增益）的增益。它补充了分布式系统中冗余的最新工作，主要关注使用擦除代码设计冗余方案。该项目将使用统计分析，排队和编码理论来做出以下贡献：（i）表征在分布式计算中使用冗余的关键效应，包括分析冗余的收益和成本； （ii）新的数学模型，以捕获带有散乱者的分布式计算系统的性能； （iii）用于计算编码系统中计算编码增益的新分析工具； （iv）开发冗余管理算法； （v）多样性与平行性权衡的表征； （vi）解决编码计算中不存在的其他关键问题，这些问题不存在于更好地理解的复制解决方案中。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来支持。