Learning Reduced Order Dynamics via Geometric Representations
Imran Nasim, Melanie Weber
SCML 2024
Gang scheduling - the scheduling of a number of related threads to execute simultaneously on distinct processors -appears to meet the requirements of interactive, multiuser, general-purpose parallel systems. Distributed hierarchical control (DHC) has been proposed as an efficient mechanism for coping with the dynamic processor partitioning necessary to support gang scheduling on massively parallel machines. In this paper, we compare and evaluate different algorithms that can be used within the DHC framework. Regrettably, gang scheduling can leave processors idle if the sizes of the gangs do not match the number of available processors. We show that in DHC this effect can be reduced by reclaiming the leftover processors when the gang size is smaller than the allocated block of processors, and by adjusting the scheduling time quantum to control the adverse effect of badly matched gangs. Consequently, the on-line mapping and scheduling algorithms developed for DHC are optimal in the sense that asymptotically they achieve performance commensurate with off-line algorithms. © 1996 Academic Press, Inc.
Imran Nasim, Melanie Weber
SCML 2024
Tim Erdmann, Stefan Zecevic, et al.
ACS Spring 2024
Arthur Nádas
IEEE Transactions on Neural Networks
Rangachari Anand, Kishan Mehrotra, et al.
IEEE Transactions on Neural Networks