This study proposes a novel approach combining Machine Learning (ML) techniques and
Genetic Algorithms (GA) for predicting High-Performance Computing (HPC) job run times …

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 …

High throughput computing (HTC) uses mass computing resources over long periods of time
to accomplish a batch of short fast jobs, it is widely employed by Simulation Computation …

The continual expansion of high-performance computing (HPC) brings with it an increasing
need for efficiency. Heavy investment in energy, hardware, and software infrastructure to …

Disaggregated memory is under investigation as a way to break the rigid boundaries
between node memory hierarchies in order to provide memory as a system-wide pooled …

High-performance computing systems rely upon scheduling algorithms to achieve high
utilization. These schedulers rely upon user estimates of job resource requirements, such as …

Abstract Overestimation of High Performance Computing (HPC) job resources allocation
typically happens because of the wide variety of HPC applications, environment …

(English) In a typical HPC cluster system, a node is the elemental component unit of this
architecture. Memory and compute resources are tightly coupled in each node and the rigid …

In the domain of High-Performance Computing (HPC), anomaly detection emerges as a
pivotal challenge. This research delves deeply into the architecture of Lisa and presents a …

Efficient resource utilization in High Performance Computing (HPC) systems heavily relies
on accurate job runtime prediction. This paper introduces JREP (Job Runtime Ensemble …