FlashGPU-sim: Enabling GPU Modeling for Modern Architectures and AI Workloads

2026-09-14

AI Summary

为解决现代AI工作负载和GPU架构模拟不准确的问题，FlashGPU-sim通过精确模拟硬件特性及优化操作符直接仿真，提供了一个高精度、执行驱动的开源平台。

Abstract

As AI becomes increasingly ubiquitous, modern AI systems are shaped by a tight software-hardware co-design loop. Later GPUs expose features such as asynchronous data movement, tensor core pipelines, and fine-grained synchronization that high-performance kernels aggressively exploit, while emerging application behaviors increasingly influence the next generation of hardware design. Unfortunately, the latest open-source simulators for NVIDIA GPUs focus on architectures and software stacks from roughly six years ago. Therefore, they cannot support many state-of-the-art AI kernels generated by modern compiler stacks, e.g. Triton, or accurately model the hardware features they depend on. As a result, architects lack a credible platform for analyzing bottlenecks in this flywheel or evaluating design trade-offs for future AI systems. To bridge this gap, we present FlashGPU-sim, an open-source, execution-driven, cycle-accurate GPU simulator for modern AI workloads. FlashGPU-sim faithfully models modern hardware features such as asynchronous data movement, fine-grained synchronization, tensor-core execution, and distributed shared memory. A Triton extraction front-end allows direct simulation of optimized AI operators without manual porting, while multi-threaded execution makes large-scale software-hardware co-design practical. Across 131 workload configurations on RTX 5090, H100, and B200, FlashGPU-sim achieves a cycle-level MAPE of 5.24%, while multi-threaded simulation reaches a 7.86x speedup with 16 host threads. An H100 case study further demonstrates its utility for microarchitectural design exploration.

Problem

• AI workloads
• GPU modeling
• software-hardware co-design

Innovation

• execution-driven
• cycle-accurate
• asynchronous data movement
• tensor-core execution
• multi-threaded simulation