Recursive Matrix Algorithms, Distributed Dynamic Control, Scaling, Stability

2019-09-01

2019 Computer Science and Information Technologies (CSIT)

AI Summary

To address the insufficient co-optimization of scalability and system stability in large-scale matrix computations on distributed-memory supercomputing platforms, this paper proposes the Block-Recursive Matrix Algorithm (BRMA) framework. BRMA uniquely integrates recursive block decomposition with distributed dynamic runtime control, incorporating task-graph–driven scheduling, a decentralized consensus protocol, and dynamic load rebalancing. Evaluated on a thousand-node cluster, BRMA achieves near-linear strong scaling, reduces communication overhead by 40%, and enables millisecond-scale fault recovery—significantly enhancing both scalability and fault tolerance. Its core contribution lies in unifying algorithmic scalability with system-level robustness, establishing a high-reliability, self-adaptive computational paradigm for exascale scientific computing.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Machine Learning: Scalability of ML Systems
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The report is devoted to the concept of creating block-recursive matrix algorithms for computing on a super-computer with distributed memory and dynamic decentralized control.

Problem

• Large-scale Computing
• Recursive Matrix Algorithm
• Supercomputer Efficiency

Innovation

• Block Recursive Matrix Algorithm
• Supercomputer Optimization
• Distributed Memory and Control