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arXiv · 2506.02344

Memory Access Vectors: Improving Sampling Fidelity for CPU Performance Simulations

Sriyash Caculo·Mahesh Madhav·Jeff Baxter
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Abstract

Accurate performance projection of large-scale benchmarks is essential for CPU architects to evaluate and optimize future processor designs. SimPoint sampling, which uses Basic Block Vectors (BBVs), is a widely adopted technique to reduce simulation time by selecting representative program phases. However, BBVs often fail to capture the behavior of applications with extensive array-indirect memory accesses, leading to inaccurate projections. In particular, the 523.xalancbmk_r benchmark exhibits complex data movement patterns that challenge traditional SimPoint methods. To address this, we propose enhancing SimPoint's BBV methodology by incorporating Memory Access Vectors (MAV), a microarchitecture independent technique that tracks functional memory access patterns. This combined approach significantly improves the projection accuracy of 523.xalancbmk_r on a 192-core system-on-chip, increasing it from 80% to 98%.

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Sriyash Caculo, Mahesh Madhav, Jeff Baxter. 2025-06-03. Memory Access Vectors: Improving Sampling Fidelity for CPU Performance Simulations. https://arxiv.org/abs/2506.02344

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