CUTHERMO: Understanding GPU Memory Inefficiencies with Heat Map Profiling

TL;DR

cuThermo is a lightweight GPU memory profiling tool that visualizes memory access inefficiencies at runtime using heat maps, aiding performance optimization without hardware or source code modifications.

Contribution

It introduces a novel, hardware-agnostic profiling method that identifies memory bottlenecks and provides optimization guidance for CUDA applications.

Findings

Identified five portable memory access patterns across GPU architectures.

Achieved up to 721.79% performance improvement through optimization.

Demonstrated effectiveness on six diverse applications.

Abstract

GPUs have become indispensable in high-performance computing, machine learning, and many other domains. Efficiently utilizing the memory subsystem on GPUs is critical for maximizing computing power through massive parallelism. Analyzing memory access patterns has proven to be an effective method for understanding memory bottlenecks in applications. However, comprehensive runtime and fine-grained memory profiling support is lacking on GPU architectures. In this work, we introduce cuThermo, a lightweight and practical profiling tool for GPU memory analysis. It operates on GPU binaries without requiring any modifications to hardware, operating system, or application source code. Given a CUDA application, cuThermo identifies memory inefficiencies at runtime via a heat map based on distinct visited warp counts to represent word-sector-level data sharing and provides optimization guidance in performance tuning iterations. Through our experiments on six applications, we identified five memory access patterns that are portable across different GPU architectures. By evaluating optimization on two GPUs, cuThermo achieves up to $721.79\%$ performance improvement.