DrGPUM: Guiding Memory Optimization for GPU-Accelerated Applications
Mao Lin, Keren Zhou, Pengfei Su · 2023
GPUs are widely used in today’s computing platforms to accelerate applications in various domains. However, scarce GPU memory resources are often the dominant limiting factor in strengthening the applicability of GPU computing. In this paper, we propose DrGPUM, the first profiler that systematically investigates patterns of memory inefficiencies in GPU-accelerated applications. The strength of DrGPUM, when compared to a large class of existing GPU profilers, is its ability to (1) correlate problematic memory usage with data objects and GPU APIs, (2) identify and categorize object-level and intra-object memory inefficiencies, and (3) provide rich insights to guide memory optimization.