SPX: Collaborative Research: SANDY: Sparsification-Based Approach for Analyzing Network Dynamics

这个为期三年的项目名为基于稀疏化的网络动态分析方法（SANDY），其目标是开发一套可扩展的并行算法，用于更新动态网络，以解决不同的问题，这些问题可以在各种高性能计算平台上执行。动态网络分析将使研究人员能够研究不同学科中复杂系统的演化，如生物信息学、社会科学和流行病学。预计SANDY项目将在开发并行动态网络算法方面开创一个新的研究方向，这将有利于多个分析目标（例如，基序发现和网络对齐）和应用领域（例如，流行病学，卫生保健）。研究成果将整合到三家合作机构提供的网络分析、并行算法和生物信息学课程中。pi将与高中合作开展网络理论讲座，并鼓励女性和少数民族学生从事与it相关的职业。为了开发高效和可扩展的并行算法，pi建议使用一种称为图稀疏化的优雅技术，该技术以类似简化的方式表达图算法。在图稀疏化框架的指导下，并行化的正式步骤提供了一个模板，用于为动态网络创建可证明正确的并行算法。所提出的算法将满足可移植性和性能优化的双重需求。该框架将进一步提供一种机制，结合高级（例如，静态和动态图分区）和低级（例如，数据流算法）调优策略，通过考虑可伸缩性、时间、内存和能效等因素，确保各种并行架构的高性能和可伸缩性。

The goal of this three-year project, Sparsification-based Approach for Analyzing Network Dynamics (SANDY), is to develop a suite of scalable parallel algorithms for updating dynamic networks for different problems that can be executed on a wide range of HPC platforms. Dynamic network analysis will enable researchers to study the evolution of complex systems in diverse disciplines, such as bioinformatics, social sciences, and epidemiology. The SANDY project is expected to initiate a new direction of research in developing parallel dynamic network algorithms that will benefit multiple analysis objectives (e.g., motif finding and network alignment) and application domains (e.g., epidemiology, health care). Research findings will be integrated into courses on network analysis, parallel algorithms, and bioinformatics offered at the three collaborating institutions. The PIs will collaborate with high schools to deliver talks on network theory, and encourage women and minority students to pursue IT-related careers. To develop efficient and scalable parallel algorithms, the PIs propose to use an elegant technique, called graph sparsification, that expresses graph algorithms in a reduction-like fashion. The formal steps to parallelization, as guided by the graph sparsification framework, provide a template for creating provably correct parallel algorithms for dynamic networks. The proposed algorithms will address the dual needs of portability and performance optimization. The framework will further provide a mechanism for combining high level (e.g., static and dynamic graph partitioning) and low level (e.g., dataflow algorithms) tuning strategies to ensure high performance and scalability for various parallel architectures by considering such factors as scalability, time, memory, and energy efficiency.