Novel Memory Technologies for Multi-Tenant Exploratory Programming

The current deep learning trend has given new popularity to the Exploratory Programming style, which often comes with high and exclusive-use demands on compute hardware and memory resources. Yet, the underlying system software is unaware of the memory semantics within applications---inhibiting optimization and use of emerging technologies. Due to over-abstraction, hardware utilization is low, resource waste (including energy) is high, and development cycles in shared scenarios are slow since users have to wait for others to give up their locks.

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Published
2024-10-24
DOI
https://doi.org/10.1145/3698783.3699379
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Novel Memory Technologies for Multi-Tenant Exploratory Programming

Felix Grzelka, Andreas Polze, Sven Köhler
Parallel Computing and Optimization Techniques
article

Novel Memory Technologies for Multi-Tenant Exploratory Programming

Felix Grzelka, Andreas Polze, Sven Köhler
article en

Abstract

The current deep learning trend has given new popularity to the Exploratory Programming style, which often comes with high and exclusive-use demands on compute hardware and memory resources. Yet, the underlying system software is unaware of the memory semantics within applications---inhibiting optimization and use of emerging technologies. Due to over-abstraction, hardware utilization is low, resource waste (including energy) is high, and development cycles in shared scenarios are slow since users have to wait for others to give up their locks.

Hasso Plattner Institute (DE), University of Potsdam (DE)
Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 81%
Parallel Computing and Optimization Techniques
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