GPU architectural simulation is orders of magnitude slower than native execution, necessitating workload sampling for practical speedups. Existing methods rely on hand-crafted features with limited expressiveness, yielding either aggressive sampling with high errors or conservative sampling with constrained speedups. To address these issues, we propose GCL-Sampler, a sampling framework that leverages Relational Graph Convolutional Networks with contrastive learning to automatically discover high-dimensional kernel similarities from trace graphs. By encoding instruction sequences and data dependencies into graph embeddings, GCL-Sampler captures rich structural and semantic properties of program execution, enabling both high fidelity and substantial speedup. Evaluations on extensive benchmarks show that GCL-Sampler achieves 258.94x average speedup against full workload with 0.37% error, outperforming state-of-the-art methods, PKA (129.23x, 20.90%), Sieve (94.90x, 4.10%) and STEM+ROOT (56.57x, 0.38%).

翻译：GPU架构模拟的速度比原生执行慢数个数量级，因此需要通过工作负载采样来实现实际的加速。现有方法依赖于表达能力有限的手工特征，导致要么是误差高的激进采样，要么是加速受限的保守采样。为了解决这些问题，我们提出了GCL-Sampler，这是一个采样框架，它利用关系图卷积网络和对比学习，从跟踪图中自动发现高维内核相似性。通过将指令序列和数据依赖关系编码为图嵌入，GCL-Sampler能够捕捉程序执行中丰富的结构和语义特性，从而实现高保真度和显著的加速。在大量基准测试上的评估表明，GCL-Sampler相比完整工作负载实现了258.94倍的平均加速，误差仅为0.37%，其性能优于最先进的方法，包括PKA（129.23倍，20.90%）、Sieve（94.90倍，4.10%）和STEM+ROOT（56.57倍，0.38%）。