NVIDIA Ampere Architecture In-Depth

Today, during the 2020 NVIDIA GTC keynote address, NVIDIA founder and CEO Jensen Huang introduced the new NVIDIA A100 GPU based on the new NVIDIA Ampere GPU…

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Ronny Krashinsky
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Overview

The article provides an in-depth look at the NVIDIA Ampere architecture, focusing on the A100 GPU's features and performance enhancements for AI, HPC, and data analytics workloads. It highlights significant improvements over the previous Tesla V100 GPU, including new Tensor Core operations, memory architecture, and the introduction of Multi-Instance GPU (MIG) technology.

What You'll Learn

1

How to leverage Multi-Instance GPU (MIG) technology for better resource utilization

2

Why the A100 GPU's Tensor Cores significantly enhance AI training performance

3

How to implement fine-grained structured sparsity in deep learning models

Key Questions Answered

What are the key features of the NVIDIA A100 GPU?
The NVIDIA A100 GPU features a new Multi-Instance GPU (MIG) capability, third-generation Tensor Cores, 40 GB of HBM2 memory, and a 40 MB L2 cache. It also supports PCIe Gen 4 and includes enhanced error detection and fault isolation technologies, making it suitable for diverse workloads in AI and HPC.
How does the A100 GPU improve performance over the Tesla V100?
The A100 GPU delivers up to 20x faster performance for certain workloads compared to the Tesla V100, particularly through its new TensorFloat-32 (TF32) operations and enhanced memory bandwidth of 1555 GB/sec. It also supports new data types and improved efficiency for deep learning and HPC applications.
What is the significance of the new Sparsity feature in A100?
The Sparsity feature in the A100 GPU allows for a doubling of throughput in Tensor Core operations by exploiting fine-grained structured sparsity in deep learning networks. This capability enhances performance without sacrificing accuracy, making it a powerful tool for accelerating inference and training.

Key Statistics & Figures

Peak FP16 Tensor Core performance
312 TFLOPS
This performance is achieved when using the new Sparsity feature in the A100 GPU.
Memory bandwidth
1555 GB/sec
This is a 73% increase compared to the Tesla V100, enabling faster data access for compute-intensive applications.
Transistor count
54.2 billion
The A100 GPU is fabricated on the TSMC 7nm process, contributing to its enhanced performance and efficiency.

Technologies & Tools

Hardware
Nvidia A100
Used for accelerating AI, HPC, and data analytics workloads.
Software
Cuda
The programming model used to leverage the capabilities of NVIDIA GPUs.

Key Actionable Insights

1
Utilize the Multi-Instance GPU (MIG) feature to maximize GPU utilization in cloud environments.
MIG allows partitioning of a single A100 GPU into multiple instances, enabling better resource allocation for different workloads and improving overall efficiency in multi-tenant scenarios.
2
Implement fine-grained structured sparsity in your deep learning models to enhance performance.
By adopting the 2:4 sparsity pattern, you can significantly reduce memory usage and increase computational throughput, which is especially beneficial for large-scale AI applications.

Common Pitfalls

1
Overlooking the importance of error and fault detection in multi-GPU environments can lead to significant downtime.
Without proper error handling, a fault in one application can disrupt the performance of others. Implementing the A100's advanced error detection features is crucial for maintaining uptime.

Related Concepts

Deep Learning
High Performance Computing (hpc)
Multi-instance GPU (mig)
Tensor Core Operations