MEGsim: A Novel Methodology for Efficient Simulation of Graphics Workloads in GPUs

Jorge Ortiz, D. Corbalán-Navarro,Juan L. Aragón,Antonio González

2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)(2022)

引用 0|浏览23
暂无评分
摘要
An important drawback of cycle-accurate microarchitectural simulators is that they are several orders of magnitude slower than the system they model. This becomes an important issue when simulations have to be repeated multiple times sweeping over the desired design space. In the specific context of graphics workloads, performing cycle-accurate simulations are even more demanding due to the high number of triangles that have to be shaded, lighted and textured to compose a single frame. As a result, simulating a few minutes of a video game sequence is extremely time-consuming.In this paper, we make the observation that collecting information about the vertices and primitives that are processed, along with the times that shader programs are invoked, allows us to characterize the activity performed on a given frame. Based on that, we propose a novel methodology for the efficient simulation of graphics workloads called MEGsim, an approach that is capable of accurately characterizing entire video sequences by using a small subset of selected frames which substantially drops the simulation time. For a set of popular Android games, we show that MEGsim achieves an average simulation speedup of 126×, achieving remarkably accurate results for the estimated final statistics, e.g., with average relative errors of just 0.84% for the total number of cycles, 0.99% for the number of DRAM accesses, 1.2% for the number of L2 cache accesses, and 0.86% for the number of L1 (tile cache) accesses.
更多
查看译文
关键词
Simulation,GPUs,Graphics pipeline,Statistical simulation,Sampling,Clustering
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要