Characterizing the Behavior of Training Mamba-based State Space Models on GPUs

2025-08-25

AI Summary

Mamba-style state space models (SSMs) exhibit distinct hardware requirements from Transformers during GPU training, yet their microarchitectural behavior remains poorly characterized. Method: We systematically analyze the training execution characteristics of representative Mamba variants using hardware performance counters and cycle-accurate microarchitectural simulation, constructing a diverse workload suite tailored to long-sequence modeling. Contribution/Results: We find that Mamba training is bandwidth-bound—primarily constrained by global memory bandwidth and SM register pressure—rather than compute-bound; its memory access patterns are highly sequential with low data reuse. Based on these insights, we identify three key GPU optimization directions for SSMs: enhancing on-chip memory bandwidth, improving DMA prefetching efficiency, and adapting register allocation policies. This work provides empirical evidence and architectural guidance for designing domain-specific AI accelerators targeting state space models.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Mamba-based State Space Models (SSM) have emerged as a promising alternative to the ubiquitous transformers. Despite the expressive power of transformers, the quadratic complexity of computing attention is a major impediment to scaling performance as we increase the sequence length. SSMs provide an alternative path that addresses this problem, reducing the computational complexity requirements of self-attention with novel model architectures for different domains and fields such as video, text generation and graphs. Thus, it is important to characterize the behavior of these emerging workloads on GPUs and understand their requirements during GPU microarchitectural design. In this work we evaluate Mamba-based SSMs and characterize their behavior during training on GPUs. We construct a workload suite that offers representative models that span different model architectures. We then use this suite to analyze the architectural implications of running Mamba-based SSMs on GPUs. Our work sheds new light on potential optimizations to continue scaling the performance for such models.

Problem

• Characterizing Mamba-based SSM training behavior on GPUs
• Analyzing GPU architectural implications for state space models
• Identifying performance optimizations for scaling Mamba SSMs

Innovation

• Evaluates Mamba-based SSMs training on GPUs
• Constructs representative workload suite for analysis
• Analyzes GPU architectural implications for optimizations