NPS: A Framework for Accurate Program Sampling Using Graph Neural Network

TL;DR

NPS introduces a graph neural network-based framework for program sampling that significantly improves accuracy and robustness over traditional methods like SimPoint, facilitating faster and more precise workload characterization.

Contribution

The paper presents Neural Program Sampling (NPS), a novel GNN-based framework that learns execution embeddings for program sampling, outperforming existing approaches in accuracy and robustness.

Findings

NPS outperforms SimPoint by up to 63% in accuracy.

NPS reduces average error by 38%.

NPS demonstrates higher accuracy and generality than previous GNN approaches.

Abstract

With the end of Moore's Law, there is a growing demand for rapid architectural innovations in modern processors, such as RISC-V custom extensions, to continue performance scaling. Program sampling is a crucial step in microprocessor design, as it selects representative simulation points for workload simulation. While SimPoint has been the de-facto approach for decades, its limited expressiveness with Basic Block Vector (BBV) requires time-consuming human tuning, often taking months, which impedes fast innovation and agile hardware development. This paper introduces Neural Program Sampling (NPS), a novel framework that learns execution embeddings using dynamic snapshots of a Graph Neural Network. NPS deploys AssemblyNet for embedding generation, leveraging an application's code structures and runtime states. AssemblyNet serves as NPS's graph model and neural architecture, capturing a program's behavior in aspects such as data computation, code path, and data flow. AssemblyNet is trained with a data prefetch task that predicts consecutive memory addresses. In the experiments, NPS outperforms SimPoint by up to 63%, reducing the average error by 38%. Additionally, NPS demonstrates strong robustness with increased accuracy, reducing the expensive accuracy tuning overhead. Furthermore, NPS shows higher accuracy and generality than the state-of-the-art GNN approach in code behavior learning, enabling the generation of high-quality execution embeddings.