Leading MLPerf Training 2.1 with Full Stack Optimizations for AI

MLPerf benchmarks, developed by MLCommons, are critical evaluation tools for organizations to measure the performance of their machine learning models’ training…

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NVIDIA
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Sukru Burc Eryilmaz
•13 min read•advanced•
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BERTJSONNumPyPythonPyTorchResNetTransformerU-Net

Overview

The article discusses NVIDIA's leadership in MLPerf Training 2.1 through full stack optimizations for AI, highlighting significant performance improvements with the new H100 Tensor Core GPU and various optimizations across popular AI workloads. It details the enhancements made in models like BERT, ResNet-50, and RetinaNet, showcasing NVIDIA's continuous innovation in AI performance.

What You'll Learn

1

How to leverage the NVIDIA Transformer Engine for optimizing BERT training

2

Why using FP8 format improves memory access times in AI models

3

How to optimize training time by overlapping CPU preprocessing with GPU operations

4

How to implement runtime fusions in Mask R-CNN for better performance

Prerequisites & Requirements

  • Understanding of AI model training and optimization techniques
  • Familiarity with NVIDIA GPUs and MLPerf benchmarks(optional)

Key Questions Answered

What performance improvements does the NVIDIA H100 GPU provide?
The NVIDIA H100 Tensor Core GPU delivers up to 6.7x higher performance compared to the first A100 Tensor Core GPU submission and up to 2.6x more performance compared to the latest A100 results in MLPerf Training 2.1.
How does the integration of the NVIDIA Transformer Engine enhance BERT training?
The NVIDIA Transformer Engine library accelerates transformer models on NVIDIA GPUs by utilizing the FP8 data format, resulting in a 37% reduction in end-to-end training time compared to not using the Transformer Engine on the same hardware.
What optimizations were made to improve the performance of ResNet-50?
For ResNet-50, optimizations included the fusion of convolution and BatchNorm operations, which led to a 4.2% speedup, and improvements in pooling operations that resulted in over a 3% speedup in MLPerf Training 2.1.
What are the benefits of using FP8 format in AI model training?
Using the FP8 format improves memory access times and computational rates, enhancing overall performance in training AI models, particularly on NVIDIA Hopper architecture GPUs.

Key Statistics & Figures

Performance improvement of H100 GPU
6.7x
Compared to the first A100 Tensor Core GPU submission
Performance improvement of A100 GPU
2.5x
Compared to its first submission due to software optimizations
End-to-end training time reduction using Transformer Engine
37%
When using FP8 format for BERT training
Speedup achieved in ResNet-50 pooling operations
over 3x
Using new graph API in cuDNN with H100 GPU

Technologies & Tools

Hardware
Nvidia Hopper
Architecture for the H100 Tensor Core GPU
Software
Nvidia Dali
Used for efficient data loading and preprocessing during evaluation
Software
Nvidia Transformer Engine
Library for accelerating transformer models on NVIDIA GPUs
Software
Cudnn
Used for runtime fusion and optimizing deep learning operations

Key Actionable Insights

1
Utilizing the NVIDIA Transformer Engine can significantly reduce training times for transformer models like BERT.
This is particularly beneficial for large-scale AI applications where training efficiency is critical. By adopting this engine, developers can leverage advanced optimizations that enhance performance.
2
Implementing FP8 format in model training can lead to substantial performance gains.
This format reduces the amount of data transferred between memory and processing units, which is crucial for optimizing resource usage in high-performance computing environments.
3
Overlapping CPU preprocessing with GPU operations can minimize idle time and improve training efficiency.
This technique is especially useful as GPU execution speeds increase, ensuring that resources are utilized effectively throughout the training process.

Common Pitfalls

1
Failing to optimize CPU-GPU synchronization can lead to performance bottlenecks.
As GPU execution speeds increase, any delays in CPU processing can cause idle GPU time, reducing overall training efficiency. It's crucial to streamline CPU tasks to keep the GPU fully utilized.

Related Concepts

Performance Optimization Techniques In AI
Deep Learning Frameworks And Libraries
Nvidia GPU Architectures And Their Features