PEAT: Pseudo-Error Assessment for GPU Kernel Validation in DNN Training
PEAT uses pseudo-error assessment for efficient GPU kernel validation in DNN training.
Key Findings
Methodology
PEAT is a lightweight framework combining playback fault injection and frequency-based runtime fault injection to assess pseudo-errors in GPU kernels during DNN training. The Profiler collects model states, the Analyzer examines error distributions, and the Detector provides guidelines for error model associations.
Key Results
- Tested on NVIDIA V100 and AMD MI250, PEAT effectively identifies kernel errors, reducing validation time and storage needs.
- By comparing with golden implementations in PyTorch and TensorFlow, PEAT detects minor precision discrepancies.
- PEAT demonstrates broad applicability across vision and language models in pretraining and finetuning scenarios.
Significance
PEAT addresses key issues in GPU kernel validation for DNN training, reducing time and storage costs. Its innovative approach offers an efficient error detection tool for academia and industry, especially significant in large-scale model training.
Technical Contribution
PEAT introduces playback fault injection and frequency-based runtime fault injection, significantly optimizing the GPU kernel validation process. Compared to existing methods, PEAT offers new perspectives on error analysis and higher detection efficiency.
Novelty
PEAT is the first to combine playback fault injection with frequency analysis for pseudo-error assessment in GPU kernels, providing a novel method for error characterization.
Limitations
- PEAT may face performance bottlenecks when handling extremely large-scale models.
- Limited adaptability to certain specific hardware architectures.
Future Work
Future research could expand PEAT's applicability, optimize performance across different hardware platforms, and explore more complex error models.
AI Executive Summary
Deep neural networks (DNNs) are increasingly prevalent in data centers, making GPU kernel validation crucial. However, existing methods are often time-consuming and require substantial storage. To address this challenge, researchers have introduced PEAT, a lightweight pseudo-error assessment framework focused on GPU kernel validation in DNN training. PEAT employs innovative playback fault injection and frequency-based runtime fault injection techniques to effectively collect and analyze model states, identifying kernel errors.
In experiments using NVIDIA V100 and AMD MI250 GPUs, PEAT was tested across various AI models, including vision tasks and language models for pretraining and finetuning. Results show that PEAT significantly reduces validation time and storage needs while effectively detecting minor precision discrepancies.
The introduction of PEAT provides an efficient error detection tool for academia and industry, particularly significant in handling large-scale model training. Future research directions include expanding PEAT's applicability, optimizing performance across different hardware platforms, and exploring more complex error models.
Deep Analysis
Background
With the rise of deep learning, DNNs have been widely applied across various fields. GPUs, due to their powerful computing capabilities, have become the preferred hardware for DNN training. However, the GPU kernel validation process is complex and time-consuming, especially in large-scale model training. Existing methods often rely on formal verification, which struggles to handle silent errors in DNN training.
Core Problem
GPU kernel validation in DNN training faces the challenge of efficiently identifying and analyzing errors. Traditional methods struggle with minor precision discrepancies and cumulative errors, leading to a time-consuming validation process requiring substantial storage.
Innovation
PEAT introduces a novel error assessment method through playback fault injection and frequency-based runtime fault injection. It collects error data over multiple training epochs and characterizes errors by analyzing their distribution.
Methodology
- �� Profiler: Invokes operation-wise kernels in a training flow to collect model states.
- �� Analyzer: Analyzes error distributions compared to golden implementations.
- �� Detector: Provides guidelines for associating error models with signature patterns.
Experiments
Experiments were conducted on NVIDIA V100 and AMD MI250, testing PEAT's performance on vision and language models. PyTorch and TensorFlow were used as baselines to analyze the performance of different error models.
Results
PEAT demonstrated effectiveness across various AI models, significantly reducing validation time and storage needs while detecting minor precision discrepancies.
Applications
PEAT is applicable in scenarios requiring efficient GPU kernel validation, such as large-scale DNN training and new hardware architecture development.
Limitations & Outlook
PEAT may face performance bottlenecks when handling extremely large-scale models and has limited adaptability to certain specific hardware architectures.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen, and PEAT is like a smart assistant helping you check if each dish tastes right. It constantly tastes and adjusts to ensure the final meal is flawless. Similarly, in DNN training, PEAT analyzes each kernel's output to ensure no errors affect the final result.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a super complex game, and PEAT is like your game assistant, helping you check if each level's settings are correct. It keeps testing and adjusting to make sure you don't lose the game because of small errors. Isn't that cool?
Glossary
Pseudo-Error Assessment
A lightweight method for evaluating GPU kernel errors.
Used for GPU kernel validation in DNN training.
Fault Injection
Intentionally introducing errors to test system robustness.
PEAT uses playback fault injection techniques.
Golden Implementation
A reference implementation used as a benchmark for comparing new kernel outputs.
Used to validate GPU kernel correctness.
Frequency Analysis
Evaluating system stability by analyzing error occurrence frequency.
PEAT uses frequency-based runtime fault injection.
CUDA Core
The basic computing unit in NVIDIA GPUs.
Used for executing FP32 computations.
Open Questions Unanswered questions from this research
- 1 How to extend PEAT's applicability to more complex hardware architectures?
- 2 How to optimize PEAT's performance in extremely large-scale models?
Applications
Immediate Applications
Large-scale DNN Training
PEAT can improve the efficiency of large-scale DNN training by reducing validation time and storage needs.
Long-term Vision
New Hardware Architecture Development
PEAT can be used to validate GPU kernels in new hardware architectures, ensuring stability in DNN training.
Abstract
Deep neural networks (DNNs) are widely adopted in various fields, driving an emerging trend in developing software stacks associated with DNN training systems. For example, many codes have been ported across different frameworks or developed to leverage the computing power of GPUs or domain-specific accelerators. However, validating a kernel implementation in DNN training is time-consuming and generally requires massive storage. Specifically, this poses a fundamental question: how to characterize the behavior of a new implementation when it is integrated into a DNN training flow. Unfortunately, this problem is not well investigated in the literature, to the best of our knowledge. To address this shortcoming, we present PEAT - a lightweight inspection framework for \underline{P}seudo-\underline{E}rror \underline{A}ssessment associated with GPU kernel validation in DNN \underline{T}raining. Firstly, inspired by conventional fault injection (FI), PEAT's Profiler invokes an operation-wise kernel in a training flow to collect a DNN model's states (e.g., checkpoints and activations). More importantly, the Profiler introduces two simple yet effective techniques, playback FI and frequency-based runtime FI, leveraging persistent kernel calling during the training process. Secondly, PEAT's Analyzer characterizes profiled errors, revealing some signatures from the error distribution of a kernel compared to the golden one. Lastly, PEAT's Detector provides some guidelines as a sufficient condition, which enables associating several well-known error models with signature patterns. We demonstrate the applicability of our approach by presenting the results and analysis using GPUs from the two most popular vendors, NVIDIA V100 and AMD MI250, on various AI models, from vision tasks to language models, for both pretraining and finetuning scenarios.