GPU Scale-Model Simulation

The continuously increasing GPU system scale and compute capabilities, i.e., increasing number of streaming multiprocessors (SMs), caches, on-chip and off-chip memory bandwidth, pose a major challenge for performance evaluation methodologies. Architectural simulation is time-consuming and resource-intensive, and because of simulator and/or simulation host infrastructure limitations, it might not even be possible to simulate large-scale systems. Scale-model simulation is a recently proposed performance prediction methodology to predict large-scale system performance based on (much smaller) scale models. Prior work in scale-model simulation for general-purpose multicore CPUs and specialized graph analytics accelerators, unfortunately, cannot be readily applied to GPUs because different GPU applications exhibit vastly different scaling behavior with system size, thereby breaking the one-size-fits-all regression models deployed in prior work. This paper proposes a GPU scale-model simulation methodology that leverages performance measurements of two scale models alongside a miss rate curve to predict GPU target system performance. A key asset of GPU scale-model simulation is that it does not require access to a simulation model of the target system, unlike prior work in simulation acceleration. Our experimental evaluation demonstrates the accuracy of GPU scale-model simulation for both strong-scaling and weak-scaling workload scenarios. Under strong scaling, the performance of a 128-SM target system is predicted within 4% error on average, and at most 17%, using 8-SM and 16-SM scale models. Under weak scaling, the performance of a 128-SM target system is estimated with an average error of 1.7%, and at most 4.5%, while yielding a 9.3× simulation time speedup. We furthermore demonstrate how scale-model simulation predicts multi-chiplet GPU performance with an average error of 2.5% (and at most 4.3%). Alternate solutions are substantially less accurate.