Simulating modern GPU architectures with increased core counts and recent workloads can be challenging, even on powerful computing platforms. In this paper, we present a simple approach to parallelize Accel-sim with minimal code changes using OpenMP. In addition, we introduce PaSSMA, a novel technique to optimize the OpenMP for-loop scheduler performance in the different simulated workloads. Moreover, our parallelization technique is deterministic, so the simulator provides exactly the same results for single-threaded and multithreaded simulations. When we run the simulator with 16 CPU cores, we achieve an average speed-up of 6.4x and reach 10x in some workloads.

GPUs are the most popular platform for accelerating HPC workloads, such as artificial intelligence and science simulations.However, most microarchitectural research in academia relies on simulators that model GPU core architectures based on designs that are more than 15 years old, and differ significantly from modern core architectures.This work reverse engineers the architecture of modern NVIDIA GPU cores, unveiling key aspects of its design and the important role of the compiler in some of its main components.In particular, it reveals how the issue logic works, the structure of the register file and its associated cache, multiple features of the instruction and data memory pipelines.When modeling all these discovered microarchitectural details in a state-of-the-art simulation framework, we show that its accuracy is significantly improved, achieving a 20.58% reduction in mean absolute percentage error (MAPE) on average, which results in a 13.45% MAPE on average with respect to real modern hardware.In addition, we show that the software-based dependence management mechanism included in modern NVIDIA GPUs outperforms a hardware mechanism based on scoreboards in terms of performance and area.