Nondeterministic Impact of CPU Multithreading on Training Deep Learning Systems

With the wide deployment of deep learning (DL) systems, research in reliable and robust DL is not an option but a priority, especially for safety-critical applications. Unfortunately, DL systems are usually nondeterministic. Due to software-level (e.g., randomness) and hardware-level (e.g., GPUs or CPUs) factors, multiple training runs can generate inconsistent models and yield different evaluation results, even with identical settings and training data on the same implementation framework and hardware platform. Existing studies focus on analyzing software-level nondeterminism factors and the nondeterminism introduced by GPUs. However, the nondeterminism impact of CPU multi-threading on training DL systems has rarely been studied. To fill this knowledge gap, we present the first work of studying the variance and robustness of DL systems impacted by CPU multithreading. Our major contributions are fourfold: 1) An experimental framework based on VirtualBox for analyzing the impact of CPU multithreading on training DL systems; 2) Six findings obtained from our experiments and examination on GitHub DL projects; 3) Five implications to DL researchers and practitioners according to our findings; 4) Released the research data (https://github.com/DeterministicDeepLearning).