Monitoring and analyzing I/O behaviors is critical to the efficient utilization of parallel storage
systems. Unfortunately, with increasing I/O requirements and resource contention, I/O …

Many massive data processing applications nowadays often need long, continuous, and
uninterrupted data accesses. Distributed file systems are used as the back-end storage to …

We propose an unsupervised machine-learning checkpoint-restart (CR) algorithm for
particle-in-cell (PIC) algorithms using Gaussian mixtures (GM). The algorithm compresses …

Failed workloads that consumed significant computational resources in time and space
affect the efficiency of HPC data centers significantly and thus limit the amount of scientific …

Production HPC clusters endure failures reducing computational capability and resource
availability. Despite the presence of various failure prediction schemes for large-scale …

The past decade has seen a rise in the use of liquid cooling due to its energy efficiency.
While many previous works have helped make progress toward improving data center …

Understanding the reliability characteristics of supercomputers has been a key focus of the
HPC and dependability communities. However, there is no current study that analyzes both …

Checkpoint/Restart (C/R) is widely used to provide fault tolerance on High-Performance
Computing (HPC) systems. However, Parallel File System (PFS) overhead and failure …

HPC datacenters offer a backbone to the modern digital society. Increasingly, they run
Machine Learning (ML) jobs next to generic, compute-intensive workloads, supporting …

Reliable job execution is important in High Performance Computing clusters. Understanding
the failure distribution and failure pattern of jobs helps HPC cluster managers design better …