4 papers
A Smallest-Need-First Job Scheduling Framework with Adaptive Optimization of Idle Node Counts for Energy-Efficient HPC Systems
Reza Pulungan, Raka Satya Prasasta, Santana Yuda Pradata +3
Power-state management in high-performance computing (HPC) clusters must reduce idle energy without excessive wake-up delays for rigid parallel jobs. This paper presents SNF-ICON,…
SPARS: A Reinforcement Learning-Enabled Simulator for Power Management in HPC Job Scheduling
Muhammad Alfian Amrizal, Raka Satya Prasasta, Santana Yuda Pradata +3
High-performance computing (HPC) systems consume enormous amounts of energy, with idle nodes as a major source of energy waste. Powering down idle nodes can mitigate this problem,…
Improving the Efficiency of a Deep Reinforcement Learning-Based Power Management System for HPC Clusters Using Curriculum Learning
Thomas Budiarjo, Santana Yuda Pradata, Kadek Gemilang Santiyuda +3
High energy consumption remains a key challenge in high-performance computing (HPC) systems, which often feature hundreds or thousands of nodes drawing substantial power even in id…
Efficient Classical Computation of Single-Qubit Marginal Measurement Probabilities to Simulate Certain Classes of Quantum Algorithms
Santana Yuda Pradata, Muhammad 'Anin Nabail 'Azhiim, Hendry Minfui Lim +4
Classical simulations of quantum circuits are essential for verifying and benchmarking quantum algorithms, particularly for large circuits, where computational demands increase exp…