HPC workload analysis and resource consumption characteristics are the key to driving
better operation practices, system procurement decisions, and designing effective resource …

To achieve a high degree of resource utilization, production clusters need to co-schedule
diverse workloads–including both batch analytic jobs with short-lived tasks and long-running …

As we approach the exascale era, the size and complexity of HPC systems continues to
increase, raising concerns about their manageability and sustainability. For this reason …

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 …

This paper provides a broad framework for understanding trends in Operational Data
Analytics (ODA) for High-Performance Computing (HPC) facilities. The goal of ODA is to …

The processor performance of high performance computing (HPC) systems is increasing at
a much higher rate than storage performance. This imbalance leads to I/O performance …

The power & energy demands of HPC machines have grown significantly. Modern exascale
HPC systems require tens of megawatts of combined power for computing resources and …

Traditional workload managers do not have the capacity to consider how IO contention can
increase job runtime and even cause entire resource allocations to be wasted. Whether from …

As HPC systems continue to grow in scale and complexity, efficient and manageable
operation is increasingly critical. For this reason, many centers are starting to explore the …

Node downtime and failed jobs in a computing cluster translate into wasted resources and
user dissatisfaction. Therefore understanding why nodes and jobs fail in HPC clusters is …