arXiv subjects

Melissa Lober

Publications and source records attributed to Melissa Lober.

5 recordsLinked to original sources

arXiv2026

NUMA balancing hampering performance of spiking network simulations

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.

cs.DC↗

arXiv2026

Exploiting network topology in brain-scale simulations of spiking neural networks

Simulation code for conventional supercomputers serves as a reference for neuromorphic computing systems. The present bottleneck of distributed large-scale spiking neuronal network simulations is the communication between compute nodes. Communication speed seems limited by the interconnect between the nodes and the software library orchestrating the data transfer. Profiling reveals, however, that the variability of the time required by the compute nodes between communication calls is large. The bottleneck is in fact the waiting time for the slowest node. A statistical model explains total simulation time on the basis of the distribution of computation times between communication calls. A fundamental cure is to avoid communication calls because this requires fewer synchronizations and reduces the variability of computation times across compute nodes. The organization of the mammalian brain into areas lends itself to such an optimization strategy. Connections between neurons within an area have short delays, but the delays of the long-range connections across areas are an order of magnitude longer. This suggests a structure-aware mapping of areas to compute nodes allowing for a partition into more frequent communication between nodes simulating a particular area and less frequent global communication. We demonstrate a substantial performance gain on a real-world example. This work proposes a local-global hybrid communication architecture for large-scale neuronal network simulations as a first step in mapping the structure of the brain to the structure of a supercomputer. It challenges the long-standing belief that the bottleneck of simulation is synchronization inherent in the collective calls of standard communication libraries. We provide guidelines for the energy efficient simulation of neuronal networks on conventional computing systems and raise the bar for neuromorphic systems.

cs.DC↗

arXiv2026

Continuous benchmarking: Keeping pace with an evolving ecosystem of models and technologies

Drawing on ideas from continuous integration, we present concepts of an automated benchmarking pipeline for high performance applications. Customization and collaboration have been key design goals owing to the requirements of research-software development as a continuous community effort. We have extended our previous conceptual work on systematic benchmarking workflows with the functionality of user-agnostic operations as well as continuous benchmarking. This fosters reproducibility and re-use of benchmarking results to ensure sustainable technological progress. We provide software-engineering solutions to keep pace with the rapid evolution of both large-scale models and high-performance computing systems with a view towards the scientific domains of neuroscience and artificial intelligence.

cs.DC↗

arXiv2026

Learning sequence timing and control of replay speed in networks of spiking neurons

Processing sequential inputs is a fundamental brain function, underlying tasks such as sensory perception, language, and motor control. A challenge in sequence processing is to represent not only the order of events, but also their precise timing. While existing computational models can learn sequential structure, many lack biologically plausible mechanisms to encode element-specific timing and to flexibly control the speed of sequence replay. The spiking Temporal Memory (sTM) model, a biologically inspired network model, provides a framework for key aspects of sequence processing. In the sTM model, each sequence element is represented by a small set of neurons firing synchronously, where the set of active neurons encodes the element's identity in its sequential context. In its original version, however, the sTM model learns the order but not the timing of sequence elements. Further, it remains an open question in neuroscience how the speed of sequence replay can be flexibly modulated. We propose a mechanism where the duration of sequence elements is represented by a sequential activation of element specific neuronal populations, enabling the model to encode sequences across a wide range of timescales. This provides a biologically plausible basis for learning and replaying complex temporal patterns. Additionally, we show that oscillatory background inputs can serve as a clock signal and provide a robust and flexible mechanism for controlling the speed of sequence replay. Our findings suggest that elapsed time is encoded by unique and sparse spatiotemporal patterns of neural activity, and that the speed of sequence replay during wakefulness and sleep is correlated to the characteristics of global oscillatory activity observed in EEG or LFP recordings.

q-bio.NC↗

arXiv2019

Invariant Manifolds and Rate Constants in Driven Chemical Reactions

Reaction rates of chemical reactions under nonequilibrium conditions can be determined through the construction of the normally hyperbolic invariant manifold (NHIM) [and moving dividing surface (DS)] associated with the transition state trajectory. Here, we extend our recent methods by constructing points on the NHIM accurately even for multidimensional cases. We also advance the implementation of machine learning approaches to construct smooth versions of the NHIM from a known high-accuracy set of its points. That is, we expand on our earlier use of neural nets, and introduce the use of Gaussian process regression for the determination of the NHIM. Finally, we compare and contrast all of these methods for a challenging two-dimensional model barrier case so as to illustrate their accuracy and general applicability.

physics.chem-ph↗