Interactive Exploratory Graph-Enabled Data Analytics at High-Performance Computing Scales
Roger Pearce | 21-ERD-020
Executive Summary
We will develop a next-generation high-performance computing data analytics system to enable interactive hybrid graph and data analytics. If successful, this system will be able to analyze research problems relevant to national missions such as space security and cybersecurity at a scale much larger than the current state of the art.
Publications, Presentations, and Patents
Steil, Trevor, Tahsin Reza, Keita Iwabuchi, Benjamin W. Priest, Geoffrey Sanders, and Roger Pearce. "TriPoll: Computing Surveys of Triangles in Massive-Scale Temporal Graphs with Metadata." SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis. November 2021.
Steil, Trevor, Geoffrey Sanders, and Roger Pearce. "Towards Distributed Square Counting in Large Graphs." 2021 IEEE High Performance Extreme Computing Conference (HPEC). September 2021.
Reza, Tahsin, Hassan Halawa, Matei Ripeanu, Geoffrey Sanders, and Roger A. Pearce. “Scalable Pattern Matching in Metadata Graphs via Constraint Checking,” ACM Transactions on Parallel Computing 8, no. 1: 2:1-2:45. 2021. https://doi.org/10.1145/3434391.
Iwabuchi, Keita, Karim Youssef, Kaushik Velusamy, Maya Gokhale, and Roger Pearce. 2021. “Metall: A Persistent Memory Allocator For Data-Centric Analytics.” arXiv: 2108.07223 [cs.DC].
Youssef, Karim, Keita Iwabuchi, Wu-chun Feng, and Roger Pearce. 2021. “Privateer: Multi-versioned Memory-mapped Data Stores for High-Performance Data Science.” IEEE High Performance Extreme Computing Conference (HPEC), 2021.
