The new Hopper-based NVIDIA H100 Tensor Core GPU

The new Hopper-based NVIDIA H100 Tensor Core GPU offers exceptional computational performance and productivity for deep learning workloads. It adds innovative hardware features such as FP8 precision, Transformer Engine, and high-bandwidth HBM3 memory, which allow scientists and engineers to train and deploy models faster and more efficiently.

To use these features in-depth, the software libraries and deep learning pipelines must be specifically tailored to take advantage of these properties. This article will explore ways to optimize deep learning pipelines using H100 GPUs.

Prerequisites

• Basic Knowledge of Deep Learning: Understanding neural networks, training processes, and common deep learning frameworks like TensorFlow or PyTorch.
• Familiarity with GPU Architecture: Knowledge of GPU architectures, including the H100, particularly its Tensor Cores, memory hierarchy, and parallel processing capabilities.
• NVIDIA CUDA and NVIDIA cuDNN: Basic understanding of NVIDIA CUDA programming and NVIDIA cuDNN, as they are essential for customizing and optimizing GPU-accelerated code.
• Experience with Model Training and Inference: Familiarity with training and deploying models, including techniques like data augmentation, transfer learning, and hyperparameter tuning.
• Understanding of Quantization and Mixed Precision Training: Awareness of techniques such as model quantization, mixed-precision training (using FP16 or TF32), and their benefits for performance optimization.
• Linux and Command-Line Proficiency: Comfort with Linux operating systems and command-line tools for managing NVIDIA drivers, libraries, and software like Docker.
• Access to an H100 GPU Environment: Availability of a system equipped with an H100 GPU, either on-premises or via cloud platforms like DigitalOcean.

Understanding the Hopper Architecture and H100 GPU Enhancements

• 4th-Generation Tensor Cores: H100 Tensor Core GPUs support multiple precisions, including FP8, for high throughput without losing quality. It is particularly suitable for mixed precision training.
• Transformer Engine: The Transformer Engine accelerates transformer models. This allows dynamically shift precision between FP8-16 during training time to get the best speeds and accuracy. It is useful, particularly in large NLP models like GPT-3 and BERT.
• HBM3 Memory: With increased bandwidth, the H100’s HBM3 memory can handle larger batch sizes, thus reducing training time. Efficiency in memory consumption is necessary to take advantage of all the available bandwidth.
• Multi-Instance GPU (MIG): With up to seven MIG instances, multiple workloads can run concurrently and maintain isolation.
• NVLink 4.0 and NVSwitch: They allow faster inter-GPU communication for distributed large-model training.

Leverage Mixed Precision Training with FP8 and FP16

Mixed-precision GPU training has long been used to accelerate deep learning, and the H100 is taking it to the next level with FP8 support. The models can train on lower-precision data types, FP8 or FP16, to reduce computation times, and higher precision for some critical computations, such as gradient accumulation. Let’s consider some best practices for Mixed Precision Training:

• Automatic Mixed Precision (AMP): We can use PyTorch torch.cuda.amp or TensorFlow tf.keras.mixed_precision to automate mixed-precision training. These libraries let us automatically cast low precision where it is safe and revert to higher precision when necessary.
• Dynamic Loss Scaling: Dynamic loss scaling helps prevent underflow when using FP8 or FP16 training. This scales the loss values up on the backward passes and scales gradients back down to preserve stability.
• Using the Transformer Engine: The Hopper transformer Engine can improve transformer model training. Use the NVIDIA Transformer Engine library, which optimizes precision levels for faster computation.

For example, in an image recognition task using a deep convolutional neural network such as ResNet, mixed precision training can help to boost the model training.

Optimize Memory Management

• Gradient Checkpointing: This technique reduces memory usage by storing a subset of activations during the forward pass. The remaining activations are recomputed during the backward pass.
• Activation Offloading: Tools like DeepSpeed or ZeRO offload activations and model components into CPU memory when not in use.
• Efficient Data Loading: Preprocessing data on GPU with NVIDIA Data Loading Library (DALI) reduces CPU-GPU communication overhead.
• Memory Pooling and Fragmentation Management: Libraries like CUDA’s Unified Memory offer dynamic memory allocation, minimizing fragmentation.

Scaling with Multi-GPU and Multi-Node Training

Scaling to multiple GPUs is often necessary to quickly train large models or datasets. The H100’s NVLink 4.0 and NVSwitch allow efficient communication across multiple GPUs.

• Data Parallelism: Partition datasets across GPUs; synchronize gradients after backpropagation.
• Model Parallelism: Split large models across GPUs to handle larger computations.
• Hybrid Parallelism: Combines data and model parallelism for optimal scaling.

Optimizing Inter-GPU Communication

• Gradient Compression: Reduces communication overhead by compressing gradients (e.g., 8-bit compression).
• Overlapping Communication and Computation: Schedules communication during computation to minimize idle times. Libraries like Horovod and NCCL support this strategy.

Fine-tune Hyperparameters for Hopper-Specific Configurations

• Batch Size Tuning: Larger batch sizes improve speed and efficiency, utilizing the H100’s memory bandwidth.
• Learning Rate Scaling: Increase the learning rate proportionally to the batch size.
• Warmup Strategies: Gradually increase the learning rate during training to stabilize large batch processing.

Profiling and Monitoring for Performance Optimization

• NVIDIA Nsight Systems: Visualizes CPU-GPU data flow, identifying performance bottlenecks.
• Nsight Compute: Analyzes CUDA kernel performance to optimize execution.
• TensorBoard: Monitors loss, accuracy, GPU utilization, and memory usage during training.
• NVIDIA System Management Interface (nvidia-smi): Tracks memory usage, temperature, and power consumption.

Optimizing Inference on the NVIDIA H100 Tensor Core GPU

• Quantization: Converts models to INT8 for reduced memory usage and faster inference.
• NVIDIA TensorRT Integration: Streamlines model execution through layer fusion and kernel auto-tuning.
• Multi-Instance GPU (MIG): Partitions the GPU to run multiple models concurrently.

Practical Use Case: Accelerating Drug Discovery Using Optimized Deep Learning Pipelines

Pharmaceutical firms use deep learning models to predict drug efficacy by analyzing molecular data. The H100 enables faster and more accurate predictions.

• Mixed Precision Training: FP8 precision reduces computation time while maintaining accuracy.
• HBM3 Memory Optimization: Larger batch sizes speed up training cycles.
• Multi-GPU Scaling: NVLink 4.0 and hybrid parallelism accelerate training across GPUs.
• Profiling Tools: Nsight Systems and TensorBoard identify bottlenecks and optimize resource use.

Conclusion

This article explores the hardware and software capabilities of the NVIDIA H100. By leveraging its features—FP8 precision, Transformer Engine, and HBM3 memory—researchers can optimize deep learning workflows for faster training and improved model performance.