numa-balancing-spiking-network-hpc

name	numa-balancing-spiking-network-hpc
description	NUMA balancing performance optimization for spiking network simulations on HPC systems. Identifies that automatic NUMA balancing can reduce energy efficiency by 30% in spiking network simulations and provides methodology for per-job NUMA balancing control to optimize performance and energy consumption.
metadata	{"arxiv_id":"2607.22275","published":"2026-07-24","authors":"Melissa Lober, Alp Inangu, Gorka Peraza Coppola, Dennis Terhorst, Sebastian Gillessen, Jan Vogelsang, Hans Ekkehard Plesser, Brian Wylie, Benedikt Steinbusch, Guido Trensch, Susanne Kunkel, Markus Diesmann","tags":["spiking-neural-networks","hpc","numa","performance-optimization","energy-efficiency","distributed-computing"]}
license	Complete terms in LICENSE.txt

NUMA Balancing Performance Optimization for Spiking Network Simulations

Overview

This skill addresses a critical performance issue in spiking network simulations on conventional HPC systems: automatic NUMA (Non-Uniform Memory Access) balancing can significantly hamper performance and energy efficiency. The research shows that turning off automatic NUMA balancing may reduce energy consumption by up to 30%, which dwarfs other energy efficiency optimization attempts in terms of cost effectiveness.

Key Findings

Problem Identification

• Memory access pattern interaction: Spiking network simulation code dynamically interacts with automatic NUMA balancing
• Performance fluctuations: Time measurements fluctuate, obstructing optimization efforts
• Hidden impact: Does not affect correctness of results, so goes unnoticed in day-to-day neuroscience research
• Library interference: Affects jemalloc library for thread-aware memory allocation in a transient manner

Solution Methodology

• Per-job NUMA control: Equip supercomputers with option to turn on/off automatic NUMA balancing on a per-job basis
• Performance monitoring: Use time- and compute-node resolved performance display to expose fine-grained temporal variability
• System perturbation detection: Method allows developers to detect HPC system perturbations and target specific improvements

Practical Implementation

For Researchers

1. Evaluate NUMA settings: Test both NUMA balancing ON and OFF for your specific spiking network simulation
2. Measure performance: Use fine-grained temporal performance monitoring to identify optimal settings
3. Document findings: Record NUMA configuration that provides best performance/energy trade-off for your application

For System Administrators

1. Enable per-job control: Implement user-level options to control NUMA balancing per job submission
2. Monitor system performance: Use the proposed performance display methodology to detect system perturbations
3. Educate users: Make researchers aware of this phenomenon as it may not be common knowledge in scientific computing

Performance Analysis Workflow

1. Baseline measurement: Run simulation with default NUMA settings
2. NUMA OFF measurement: Run identical simulation with automatic NUMA balancing disabled
3. Compare metrics: Analyze runtime, energy consumption, and memory access patterns
4. Fine-grained analysis: Use time- and node-resolved performance displays to identify temporal variability
5. Optimize configuration: Select NUMA setting that provides optimal performance for the specific application

Technical Details

NUMA Balancing Impact

• Energy reduction: Up to 30% energy consumption reduction when NUMA balancing is disabled
• Runtime improvement: Corresponding decrease in application runtime
• Memory allocation: Transient effects on jemalloc thread-aware memory allocation
• Correctness preservation: Simulation results remain correct regardless of NUMA setting

Applicability

• HPC systems: Conventional CPU- and GPU-based supercomputing centers
• Spiking network simulations: Large-scale neural network simulation codes
• Scientific computing: Potentially applicable to other scientific codes with similar memory access patterns
• Neuromorphic reference: Relevant as conventional systems serve as reference for neuromorphic computing energy efficiency claims

Activation Keywords

• numa balancing spiking networks
• hpc performance optimization neuroscience
• energy efficiency spiking simulations
• memory access pattern neuroscience
• jemalloc numa interference
• distributed spiking network performance
• supercomputer numa configuration

References

• arXiv:2607.22275 - NUMA balancing hampering performance of spiking network simulations
• DOI: https://doi.org/10.48550/arXiv.2607.22275

Pitfalls and Considerations

Common Misconceptions

• Assumption of universal benefit: Automatic NUMA balancing is not universally beneficial for all workloads
• Overlooked impact: Performance impact may go unnoticed due to preserved result correctness
• Limited awareness: Phenomenon may not be common knowledge in scientific computing communities

Implementation Challenges

• System-specific behavior: NUMA behavior may vary across different HPC architectures
• Workload dependency: Optimal NUMA settings depend on specific simulation characteristics
• Monitoring overhead: Fine-grained performance monitoring may introduce additional overhead

Broader Implications

• Energy efficiency claims: Neuromorphic computing energy efficiency comparisons should account for optimized conventional system performance
• Scientific reproducibility: NUMA settings should be documented in computational neuroscience publications
• HPC best practices: This finding suggests need for workload-specific NUMA optimization strategies in scientific computing