Abstract: Modern parallel architectures have heterogeneous processors and complex memory hierarchies, offering up to billion-way parallelism at multiple hierarchical levels. Their exploitation by HPC applications greatly boosts scientific discoveries and advances, but they are still not fully utilized, leading to proportionally high energy consumption. The task-based programming model has demonstrated promising potential in developing scientific applications on modern high-performance platforms. This work introduces a new framework for managing the concurrent execution of task-based applications, RSCHED. The framework aims to minimize the overall time spent executing a set of applications and maximize resource utilization. RSCHED is a two-level resources management framework: resource distribution and task scheduling, with sharable and reusable resources on the fly. A new model of Gradient Descent has been proposed, among other strategies for resource distribution, due to its well-known speedy convergence event in fast-growing systems. We implemented our proposal on StarPU and evaluated it on real applications. RSCHED demonstrated the potential to speed up the overall makespan of executed applications compared to consecutive execution with an average factor of 10x and the potential to increase resource utilization.

Etienne Ndamlabin and Berenger Bramas. “RSCHED: An Effective Heterogeneous Resource Management for Simultaneous Execution of Task-Based Applications”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.2 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160206

@article{Ndamlabin2025,
title = {RSCHED: An Effective Heterogeneous Resource Management for Simultaneous Execution of Task-Based Applications},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160206},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160206},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Etienne Ndamlabin and Berenger Bramas}
}