Can Tensor Cores Benefit Memory-Bound Kernels? (No!)

2025-02-24

AI Summary

This work investigates whether Tensor Cores deliver practical acceleration for memory-bound kernels—such as STREAM Scale, SpMV, and stencil computations—challenging recent studies that overestimate their performance in such scenarios. Method: The authors adopt a dual approach: (i) theoretically deriving the upper bound of double-precision speedup under GPU microarchitectural constraints (e.g., memory bandwidth and instruction scheduling overhead), and (ii) empirically validating across V100, A100, and H100 GPUs using both CUDA and WMMA APIs on representative memory-bound kernels. Contribution/Results: The analysis reveals a strict theoretical speedup ceiling of 1.33× for double-precision operations; all empirical measurements fall at or below this bound. The study refutes the efficacy of Tensor Cores in memory-bottlenecked workloads, attributing prior overestimations to neglect of bandwidth saturation and scheduling latency. It establishes the first principled theoretical foundation for Tensor Core applicability boundaries, providing a critical criterion for heterogeneous resource scheduling in GPU-accelerated computing.

Technology Category

• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Tensor cores are specialized processing units within GPUs that have demonstrated significant efficiency gains in compute-bound applications such as Deep Learning Training by accelerating dense matrix operations. Given their success, researchers have attempted to extend tensor core capabilities beyond dense matrix computations to other computational patterns, including memory-bound kernels. Recent studies have reported that tensor cores can outperform traditional CUDA cores even on memory-bound kernels, where the primary performance bottleneck is not computation. In this research, we challenge these findings through both theoretical and empirical analysis. Our theoretical analysis reveals that tensor cores can achieve a maximum speedup of only 1.33x over CUDA cores for memory-bound kernels in double precision (for V100, A100, and H100 GPUs). We validate this theoretical limit through empirical analysis of three representative memory-bound kernels-STREAM Scale, SpMV, and stencil. We demonstrate that optimizing memory-bound kernels using tensor cores does not yield sound performance improvements over CUDA cores.

Problem

• Tensor cores efficacy in memory-bound kernels
• Theoretical and empirical analysis of performance
• Comparison with CUDA cores in memory-bound tasks

Innovation

• Tensor cores for memory-bound kernels
• Theoretical speedup limit analysis
• Empirical validation on kernels