Journal Article

Artificial neural networks based predictions towards the auto-tuning and optimization of parallel IO bandwidth in HPC system

Conbhuí, Pádraig Ó, Tipu, Abdul Jabbar Saeed, Howley, Enda

2024

Overview

Super-computing or HPC clusters are built to provide services to execute computationally complex applications. Generally, these HPC applications involve large scale IO (input/output) processing over the networked parallel file system disks. They are commonly developed on top of the C/C++ based MPI standard library. The HPC clusters MPI–IO performance significantly depends on the particular parameter value configurations, not generally considered when writing the algorithms or programs. Therefore, this leads to poor IO and overall program performance degradation. The IO is mostly left to individual practitioners to be optimised at code level. This usually leads to unexpected consequences due to IO bandwidth degradation which becomes inevitable as the file data scales in size to petabytes. To overcome the poor IO performance, this research paper presents an approach for auto-tuning of the configuration parameters by forecasting the MPI–IO bandwidth via artificial neural networks (ANNs), a machine learning (ML) technique. These parameters are related to MPI–IO library and lustre (parallel) file system. In addition to this, we have identified a number of common configurations out of numerous possibilities, selected in the auto-tuning process of READ/WRITE operations. These configurations caused an overall READ bandwidth improvement of 65.7% with almost 83% test cases improved. In addition, the overall WRITE bandwidth improved by 83% with number of test cases improved by almost 93%. This paper demonstrates that by using auto-tuning parameters via ANNs predictions, this can significantly impact overall IO bandwidth performance.

• Publisher: Springer US,Springer Nature B.V
• Subject: Accuracy / Algorithms / Artificial neural networks / Bandwidths / Clusters / Computer Communication Networks / Computer Science / Configurations / Libraries / Machine learning / Neural networks / Operating Systems / Optimization / Parameters / Performance degradation / Processor Architectures / Software / Tuning