In many domains, the previous decade was characterized by increasing data volumes and
growing complexity of data analyses, creating new demands for batch processing on …

Computational quantum mechanics based molecular and materials design campaigns
consume increasingly more high-performance computer resources, making improved job …

With the growing complexity of high-performance computing (HPC) systems, achieving high
performance can be difficult because of I/O bottlenecks. We analyze multiple years' worth of …

In modern high-performance computing (HPC) systems, users are usually requested to
estimate the job runtime for system scheduling when they submit a job. In general, an …

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 …

Large high-performance computers (HPC) are expensive tools responsible for supporting
thousands of scientific applications. However, it is not easy to determine the best set of …

Deep reinforcement learning applied to computing systems has shown potential for
improving system performance, as well as faster discovery of better allocation strategies. In …

Determining resource allocations (memory and time) for submitted jobs in High Performance
Computing (HPC) systems is a challenging process even for computer scientists. HPC users …

The emergence of new classes of HPC applications and usage models, such as real-time
HPC and cloud HPC, coupled with the increasingly heterogeneous nature of HPC …

As the amount of available data continues to grow in fields as diverse as bioinformatics,
physics, and remote sensing, the importance of scientific workflows in the design and im …