AI Insight
This study demonstrates that automatic NUMA (Non-Uniform Memory Access) balancing in conventional supercomputers significantly impairs the performance of spiking neural network simulations, increasing energy consumption by approximately 30%. The researchers identified that the memory access patterns of spiking network simulation code interact poorly with automatic NUMA balancing, creating performance fluctuations that obscure optimization attempts while not affecting result correctness. They developed a new time- and compute-node resolved performance analysis method that revealed the transient effects on the jemalloc memory allocation library.
Why it matters
The findings provide an immediate, cost-effective method to reduce energy consumption in computing centers running neural network simulations by simply disabling automatic NUMA balancing on a per-job basis. This discovery has broader implications for scientific computing, as similar performance degradation may affect other computational workloads but has gone unnoticed despite some prior indications in the literature.
Understand the Science
arXiv:2607.22275v1 Announce Type: cross
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.
Source: NUMA balancing hampering performance of spiking network simulations