NUMA balancing hampering performance of spiking network simulations
Abstract
Computing centers today mostly operate conventional CPU- and GPU-based systems, where the direct way of decreasing energy consumption is a reduction in the applications' runtime. Neuromorphic computing promises an alternative architecture with improved energy efficiency for artificial intelligence. In this endeavor, code for the simulation of large-scale spiking networks on conventional supercomputers is the reference. We show that turning off automatic NUMA balancing may reduce energy consumption by 30%. This dwarfs other attempts of increasing the energy efficiency of a computing center with respect to cost effectiveness. The memory access pattern of spiking network simulation code dynamically interacts with automatic NUMA balancing. This does not affect the correctness of simulation results and thus goes unnoticed in day-to-day neuroscience research. In performance analysis, however, time measurements fluctuate obstructing attempts to optimize simulation technology. A new time- and compute-node resolved performance display exposes the fine-grained temporal variability of distributed spiking network simulations. The analysis uncovers that automatic NUMA balancing is of disadvantage and affects the jemalloc library for thread-aware memory allocation in a transient manner. The method also allows developers to detect perturbations of the HPC system and target specific improvements to simulation technology. As a consequence, we have equipped our supercomputers with an option to turn on or off automatic NUMA balancing on a per-job basis on the user level. This gives researchers the opportunity to find the best setting for the application at hand. There are indications in the literature that the effect has been observed before, yet it does not seem common knowledge in scientific computing. It remains to be investigated how widespread the phenomenon is among scientific codes.
Authors 12
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Simulation Technologies (United States)
Affiliation as printed
Institute for Advanced Simulation (IAS- )
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D. Terhorst Aachen
Forschungszentrum Jülich · Jülich Aachen Research Alliance · FH Aachen
Affiliation as printed
Institute for Advanced Simulation (IAS- ) Jülich Research Centre Jülich , Germany
Jülich Research Centre RWTH Aachen University Jülich/ Aachen , Germany
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Sebastian Gillessen Aachen
Forschungszentrum Jülich · Jülich Aachen Research Alliance · FH Aachen
Affiliation as printed
Institute for Advanced Simulation (IAS- ) Jülich Research Centre Jülich , Germany
Jülich Research Centre RWTH Aachen University Jülich/ Aachen , Germany
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Jan Vogelsang Aachen
Forschungszentrum Jülich · Jülich Aachen Research Alliance · FH Aachen
Affiliation as printed
Jülich Research Centre RWTH Aachen University Jülich/ Aachen , Germany
Neuromorphic Software Ecosystems (PGI-15 ) Jülich Research Centre Jülich , Germany
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Norwegian University of Life Sciences
Affiliation as printed
Department of Data Science ,
Faculty of Science and Technology Norwegian University of Life Sciences Institute for Advanced Simulation (IAS- )
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Forschungszentrum Jülich · Ernst Ruska Centre · Jülich Supercomputing Centre
Affiliation as printed
Jülich Supercomputing Centre Jülich Research Centre Jülich , Germany
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Forschungszentrum Jülich · Ernst Ruska Centre · Jülich Supercomputing Centre
Affiliation as printed
Jülich Supercomputing Centre Jülich Research Centre Jülich , Germany
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Forschungszentrum Jülich · Ernst Ruska Centre · Jülich Aachen Research Alliance · Stadtwerke Jülich (Germany) · Jülich Supercomputing Centre
Affiliation as printed
Jülich Research Centre Ås , Norway / Jülich , Germany
Simulation and Data Laboratory Neuroscience Jülich Supercomputing Centre Jülich Research Centre Jülich , Germany
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Affiliation as printed
Neuromorphic Software Ecosystems (PGI-15 ) Jülich Research Centre Jülich , Germany
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