MCM-GPU Voltage Noise Characterization and Architecture-Level Mitigation
Jingweijia Tan, Keyu Chen, Weiren Wang, Kaige Yan, Xiaohui Wei · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2023
Due to manufacturing process and yield constraints, scaling GPU performance via increasing chip area becomes difficult. In the meanwhile, the demand for high computational throughput is increasing for high performance computing applications. As an alternative, multichip module GPU (MCM-GPU) achieves performance scalability via integrating multiple GPU chip modules (GPMs) on the same package. However, large MCM-GPU systems are susceptible to voltage noise effects, which cause voltage instability during program execution and result in energy inefficiency. In this work, we first model and analyze the voltage noise of MCM-GPUs at architecture level in detail. We characterize the voltage noise distributions of MCM-GPUs at different levels and under various design parameters. We further propose two architecture level voltage noise mitigation approaches, including GPM aware mitigation (GAM) and droop magnitude aware smoothing (DMAS), that leverage the voltage noise characteristics of MCM-GPUs. Evaluation shows both techniques are effective in reducing the voltage droop magnitudes and achieve good energy savings with negligible performance degradation.