Deep Learning Training and Inference on Nvidia H100
Understanding the Nvidia H100 GPU
NVIDIA H100 Tensor Core GPU: A Leap in Data Center GPU Technology
The NVIDIA H100 Tensor Core GPU marks a significant milestone as the ninth-generation data center GPU from NVIDIA. It’s designed to provide a substantial performance leap over its predecessor, the NVIDIA A100 Tensor Core GPU, particularly for large-scale AI and High-Performance Computing (HPC) applications. This GPU maintains a focus on improving strong scaling for AI and HPC workloads, complemented by significant architectural enhancements.
Key Features of the H100 GPU
- Streaming Multiprocessor (SM) Innovations: The H100 introduces a new streaming multiprocessor design with numerous performance and efficiency improvements. It features the fourth-generation Tensor Cores, which offer up to six times faster performance compared to the A100. These Cores provide double the Matrix Multiply-Accumulate (MMA) computational rates on equivalent data types and quadruple the rate using the new FP8 data type. Additionally, the Sparsity feature in these Cores effectively doubles the performance of standard Tensor Core operations.
- Enhanced Dynamic Programming (DPX) Instructions: The H100 GPU introduces new DPX instructions that accelerate dynamic programming algorithms, achieving up to seven times faster performance than the A100 GPU. Examples include the Smith-Waterman algorithm for genomics processing and the Floyd-Warshall algorithm for optimal routing in dynamic environments.
- Advanced IEEE FP64 and FP32 Processing Rates: The H100 achieves three times faster processing rates compared to A100, a result of faster per SM performance, additional SM counts, and higher clock speeds.
- Thread Block Cluster and Distributed Shared Memory: The H100 features a new thread block cluster that allows for programmatic control of locality on a larger scale than a single thread block on a single SM. This addition to the CUDA programming model enhances data synchronization and exchange across multiple SMs. The distributed shared memory further enables direct SM-to-SM communications.
- Asynchronous Execution Capabilities: The H100 integrates new asynchronous execution features, including the Tensor Memory Accelerator (TMA) for efficient data transfer between global and shared memory and supports asynchronous copies within a cluster.
The NVIDIA Hopper GPU Architecture
The H100 GPU is based on the cutting-edge NVIDIA Hopper architecture, which brings multiple innovations to the table:
- New Fourth-Generation Tensor Cores: These Cores are designed for faster matrix computations, crucial for a broad range of AI and HPC tasks.
- Transformer Engine: This new addition enables the H100 to deliver significantly faster AI training and inference speedups, especially on large language models, compared to the A100.
- NVLink Network Interconnect: This feature allows efficient GPU-to-GPU communication among up to 256 GPUs across multiple compute nodes, facilitating large-scale distributed workloads.
- Secure MIG Technology: It partitions the GPU into isolated instances, optimizing quality of service for smaller workloads.
Technical Specifications of the H100 GPU
The heart of the H100 GPU, the GH100, is built using the TSMC 4N process tailored for NVIDIA, featuring 80 billion transistors and a die size of 814 mm². The H100 GPU with SXM5 board form-factor comprises 8 GPU Processing Clusters (GPCs), 66 Texture Processing Clusters (TPCs), and 132 Streaming Multiprocessors (SMs). It includes 16896 FP32 CUDA Cores and 528 fourth-generation Tensor Cores. The GPU is equipped with 80 GB of HBM3 memory, providing a staggering 3 TB/sec of memory bandwidth, and includes a 50 MB L2 cache.
Deep Learning Training Performance
The Significance of AI Model Training
Training AI models is a cornerstone of the rapidly growing AI application landscape. The efficiency of this training process is crucial, as it impacts the deployment speed and the overall value generation of AI-powered applications. The NVIDIA H100, with its advanced capabilities, plays a pivotal role in enhancing this training efficiency, enabling more rapid development and deployment of AI models.
MLPerf Training v3.0: Setting New Benchmarks
MLPerf Training v3.0, a suite of tests developed by MLCommons, measures AI performance across various use cases. The inclusion of new tests, like the large language model (LLM) based on GPT-3 and an updated DLRM test, provide a more comprehensive evaluation of AI training performance. The NVIDIA H100 set new performance records in MLPerf Training v3.0, achieving the highest performance on a per-accelerator basis and delivering the fastest time to train on every benchmark at scale. This demonstrates the H100’s capability to handle a wide range of AI training tasks, from computer vision to language processing and recommender systems.
Record-Setting Performance in Diverse Workloads
NVIDIA H100 GPUs achieved unprecedented performance in MLPerf Training v3.0, setting new time-to-train records across various workloads. This includes large-scale tasks such as training the state-of-the-art LLM with 175 billion parameters and other demanding applications in natural language processing, image classification, and more. The H100 GPUs significantly reduced the time-to-train across these diverse tasks, showcasing their ability to handle the most challenging AI training workloads efficiently.
Training Large Language Models: A Case Study
The training of large language models, like the GPT-3 with 175 billion parameters, requires a robust full-stack approach, stressing every aspect of an AI supercomputer. The NVIDIA H100 GPUs demonstrated their capability in this demanding environment by achieving significant time-to-train reductions, even when scaled to hundreds or thousands of GPUs. This shows the H100’s ability to maintain high performance in both on-premises and cloud-based AI training environments.
Optimizations in AI Model Training
NVIDIA’s submissions for MLPerf Training v3.0 included various optimizations that enhanced the H100’s performance in training AI models. For instance, improvements in data preprocessing and random number generation led to significant reductions in iteration time and increased throughput. Additionally, the use of CUDA Graphs and optimizations in the cuBLAS library resulted in further enhancements in training efficiency, particularly in single-node scenarios. These optimizations not only improved the performance but also maintained the accuracy and quality of the AI models.
Inference Capabilities of Nvidia H100
The Evolving Landscape of AI Inference
AI inference, the process of running trained neural networks in production environments, is crucial in the AI world. With the rise of generative AI, the demand for high-performance inference capabilities has escalated. The Nvidia H100, powered by the Hopper architecture and its Transformer Engine, is specifically optimized for these tasks, demonstrating its prowess in MLPerf Inference 3.0 benchmarks. This benchmark is significant as it measures AI performance across a range of real-world applications, from cloud computing to edge deployments.
Unprecedented Performance in MLPerf Inference 3.0
The H100 GPUs showcased remarkable efficiency and performance in every test of AI inference in the MLPerf Inference 3.0 benchmarks. The results indicate up to a 54% performance gain from its debut, reflecting the continuous advancements and optimizations in Nvidia’s software and hardware. This level of performance is crucial for generative AI applications, such as those used for creating text, images, and 3D models, where quick and accurate responses are essential.
Optimizations for Enhanced Inference
Nvidia’s commitment to advancing AI inference extends beyond hardware. Software optimizations play a vital role in maximizing performance. These include enhancements in the NVIDIA AI Enterprise software layer, ensuring optimized performance for infrastructure investments. Furthermore, the availability of this optimized software on the MLPerf repository and continuous updates on NGC, Nvidia’s catalog for GPU-accelerated software, make these advancements accessible and beneficial for a wide range of applications.
Versatility Across Applications
The versatility of the Nvidia AI platform is evident in its ability to run all MLPerf inference workloads, catering to various scenarios in both data center and edge computing. This adaptability is crucial as real-world AI applications often employ multiple neural networks of different types, each requiring high-performance inference to deliver real-time responses. The MLPerf benchmarks, backed by leading industry players, provide a transparent and objective measure of this performance, enabling informed decisions for customers and IT decision-makers.
Applications in Real-World Scenarios
H100: A Catalyst for Mainstream AI Applications
The Nvidia H100 represents a significant leap in AI and machine learning capabilities, promising up to 9x faster AI training and 30x faster AI inference than its predecessor, the A100. This dramatic increase in performance has the potential to bring artificial intelligence applications into the mainstream across various industries. With its advanced capabilities, the H100 is much more than just a hardware accelerator; it’s a foundation for a new era of AI applications that are more accessible and powerful than ever before.
The Transformer Engine: Revolutionizing Machine Learning
One of the key factors behind the H100’s speedup is the new Transformer Engine. This engine is specifically designed to accelerate machine learning technologies, particularly those that create large and complex ML models. As these models become increasingly prevalent in the AI landscape, the H100’s Transformer Engine ensures that Nvidia stays at the forefront of these technological advancements. This specialized focus on function-specific optimizations like the Transformer Engine marks a significant shift in how AI hardware is developed, with a clear emphasis on meeting the evolving demands of the industry.
Supercomputer-Class Performance for Businesses
The advancements Nvidia has made with the H100 and the new DGX H100 servers enable businesses of various scales to achieve supercomputer-class performance using off-the-shelf parts. This democratization of high-performance computing power allows more organizations to engage in advanced computing tasks that were previously out of reach due to technological and financial constraints. The expansion of NVLink interconnect, enabling the creation of large-scale, interconnected systems, further amplifies this capability, offering unprecedented computational power in a more accessible format.
H100: Beyond a Traditional GPU
The H100, the ninth generation of Nvidia’s data center GPU, is equipped with more Tensor and CUDA cores at higher clock speeds than the A100. It also features 50MB of Level 2 cache and 80GB of HBM3 memory, providing twice the bandwidth of its predecessor. The addition of new DPX instructions accelerates dynamic programming algorithms in various fields like healthcare, robotics, quantum computing, and data science, showcasing the H100’s versatility beyond traditional GPU applications.
Revolutionizing AI Infrastructure with DGX H100
The DGX H100 represents Nvidia’s fourth-generation AI-focused server system. Packing eight H100 GPUs connected through NVLink, it provides a powerful and scalable solution for delivering AI-based services at scale. The concept of DGX POD and SuperPOD further extends this capability, linking multiple systems to deliver exascale AI performance. These systems not only represent a significant technological achievement but also provide a practical blueprint for organizations looking to leverage AI at a large scale.
Building the World’s Fastest AI Supercomputer
Nvidia plans to combine multiple SuperPODs, amounting to 4,608 H100 GPUs, to build Eos, projected to be the world’s fastest AI supercomputer. This endeavor highlights the critical role of NVLink in these systems, offering a high bandwidth chip-to-chip connectivity solution that significantly surpasses traditional PCIe capabilities. The realization of such a supercomputer underscores the transformative potential of the H100 in pushing the boundaries of AI and high-performance computing.