SPX: Collaborative Research: Moving Towards Secure and Massive Parallel Computing

现代计算系统已经从单核、单处理器设备转移到更现代的多核并行处理器，这些处理器在联网系统中运行，并可在仓库规模的云中使用，这些云在行业和政府中得到普及。这个新的并行、互联、大数据世界需要从算法到系统和计算机体系结构的多个层面的基础研究。该项目旨在通过构建和开发新的通用框架来研究这一重要研究挑战中的广泛算法和系统问题，在现实生活中经常涉及隐私和安全的大规模并行计算的新通用框架。调查人员？长期目标包括两个方向。作为这项工作的第一个主旨，研究人员的目标是在实用的MapReduce框架中为大规模并行计算设计基本和高效的算法，特别是通过减少该框架中的轮数。作为这项工作的第二个主旨，调查人员的目标是用更好的数据结构和抽象来增强当前的并行环境和体系结构，以开发基本算法的简化和快速实现，以便每个人都可以在实践中使用它们。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Modern computing systems have moved beyond single-core, single-processor devices to more modern multi-core parallel processors operating in networked systems and available in warehouse-scale clouds popularized by industries and the government. This new parallel, interconnected, big-data world requires fundamental research on multiple levels from algorithms to systems and computer architecture. This project seeks to take initial steps in the study of the expansive set of algorithms and systems issues in this important research challenge by building and developing new general frameworks for massive parallel computation, often involving privacy and security, in real-life scenarios. The investigators? long-term goals include two directions. As the first thrust of this effort, the investigators aim to design fundamental and efficient algorithms for massive parallel computations in the practical MapReduce framework, in particular by reducing the number of rounds in this framework. As the second thrust of this effort, the investigators aim to augment current parallel environments and architectures with better data structures and abstractions to develop simplified and fast implementations of fundamental algorithms such that everyone can use them in practice.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.