NVIDIA A100 : Specifications, Architecture, Working, Differences & Its Applications

The first GPU, like the NVIDIA A100 Tensor Core GPU, was released on May 14, 2020, by NVIDIA. This GPU was based on the Ampere architecture, designed to be used as the basic building block in high-performance computing systems and data centers. Initially, it became a part of the 3rd-generation DGX A100 server. After that its variations like A100 PCIe 40 GB and A100 80 GB GPU were also released. The NVIDIA A100 GPU was designed to conquer key challenges & blockages in deep learning, AI, High-Performance Computing, and data analytics, mainly those faced by its predecessor, like the V100. This article elaborates on the NVIDIA A100 GPU, its working, and its applications.

What is NVIDIA A100 Tensor Core GPU?

The NVIDIA A100 Tensor Core GPU is a high-performance data center accelerator. It is based on the Ampere architecture, designed for accelerating data analytics, AI, and HPC workloads by providing significant performance gains up to 20x over its earlier generations. Its key features mainly include its third-generation Tensor Cores accelerate a variety of precision formats. MIG (Multi-Instance GPU) technology allows a single A100 to be divided into up to seven minor, isolated GPU instances.

Specifications

The NVIDIA A100 Tensor Core GPU specifications include the following.

• Its Architecture is NVIDIA Ampere
• It uses a GA100 GPU.
• Its memory is 40GB/80GB HBM2e
• Memory Bandwidth is 1,555 GB/s for 40GB, 1,935 GB/s for 80GB PCIe, and 2,039 GB/s for 80GB SXM4.
• It features CUDA Cores – 6912 and Tensor Cores – 432/
• It is supported by MIG (Multi-Instance GPU) with up to 7 instances for each GPU
• NVLink Interconnect: 600 GB/s (PCIe & SXM4) and PCIe Gen4 is 64 GB/s
• Maximum Power Consumption for PCIe is 300W
• Form Factor for PCIe is Dual-slot air-cooled/single-slot liquid-cooled
• Max power consumption for SXM4 is 400W
• Form Factor for SXM4 is SXM module
• Its performance is higher peak performance.

How does NVIDIA A100 Work?

The NVIDIA A100 GPU works with the Ampere architecture by using specialized third-generation Tensor Cores and parallel CUDA cores. Therefore, it accelerates a wide range of data analytics, HPC, and AI workloads.

Its third-generation Tensor Cores can handle both sparse and dense matrix calculations by extensively enhancing performance for AI training & inference. In addition, it can also support MIG (Multi-Instance GPU) by allowing it to be separated into up to smaller and isolated GPU instances to optimize resource portions for special tasks.

The NVIDIA A100 processes data through a series which use application calls, next data transfer, parallel execution on particular cores & result retrieval. This process uses its Ampere architecture for AI and HPC workloads.

Step-by-Step NVIDIA A100 GPU Working

The steps involved in working with the NVIDIA A100 GPU are discussed below.

• At first, the process starts on the CPU whenever a program with a framework like CUDA, PyTorch, or TensorFlow calls a GPU-accelerated function.
• The required data can be sent from the system memory of the host to the high-bandwidth HBM2/HBM2e memory of the GPU through a PCIe Gen 4/NVLink high-speed interconnect.
• The command processor of the GPU plans the kernel for execution. Therefore, the workload is broken down into a network of thread blocks, which are transmitted to the accessible SMs or streaming multiprocessors.
• Further, each streaming multiprocessor separates its allocated thread blocks into minor groups known as warps.
• After that, these are executed in parallel across the numerous cores of the A100 GPU.
• CUDA Cores in this architecture manage general-purpose parallel processing tasks.
• The specialized units in this GPU, like Tensor Cores, speed up matrix operations, which are significant for certain HPC and deep learning tasks. Thus, it supports a variety of precisions like BFLOAT16, TF32, FP16, INT4, and INT8.
• Special function units or SFUs can execute complex mathematical sine & square root-based operations.
• Once operations proceed, then data can be handled across different memory hierarchy levels for efficiency.
• Therefore, it improves compute performance by reducing trips to the major HBM2 memory.
• If several A100 GPUs are used, then the third-generation NVLink interconnect allows direct GPU-to-GPU high-speed communication by allowing them to work jointly on extremely large models or simulations through minimum latency.
• When the computation is finished, the results will be transferred back to the host system’s memory from the A100’s memory for advanced use.
• After that, the host system application can present the last output to the user or add it to succeeding steps of a larger workflow.

NVIDIA A100 Architecture

The NVIDIA A100 GPU is built on the Ampere architecture with a 7nm process that boasts 54.2 billion transistors by supporting mixed-precision calculations. It has several features like Multi-Instance GPU technology, structural sparsity acceleration, and third-generation Tensor Cores. Its latest TensorFloat-32 format is designed to boost HPC and AI performance compared to earlier generations extensively. Its key innovations, like Multi-Instance GPU, which divides a single GPU into seven instances & structural sparsity, can boost AI inference performance. Additionally, it can offer high-performance HBM2e memory utilizing a TSMC 7nm process.

Components

The NVIDIA A100’s Ampere architecture can be built with different components like Streaming Multiprocessors, where each includes CUDA cores & third-generation Tensor Cores. Therefore, it uses high-bandwidth HBM2e memory and high-speed NVLink for GPU-to-GPU communication by supporting MIG (Multi-Instance GPU) for virtualization. In addition, it can also have key components like the large L2 cache, support for the new TensorFloat-32 TF32 numerical format & hardware acceleration mainly for structural sparsity.

GA100 GPU Processor

The GA100 is an NVIDIA GPU based on the Ampere architecture, manufactured on a 7nm process. It is a large chip with 54.2 billion transistors. It uses 6 HBM2 stacks for memory with 1.6 TB/s of bandwidth on the A100 board. The core of this chip can be manufactured with a 7nm FinFET process that features over 54 billion transistors. It is a high-performance processor designed for compute-intensive tasks and for data centers like AI and HPC, with up to 512 third-generation Tensor cores and 8192 FP32 CUDA cores.

Streaming Multiprocessors or SMs

The NVIDIA A100 GPU features 108 Streaming Multiprocessors, which are the core processing units designed for executing threads. Every streaming microprocessor is responsible for managing several thread blocks & contains the required hardware for computation with Tensor Cores, CUDA cores, a register file, and shared memory. These SMs are designed to execute parallel workloads efficiently, like high-performance computing and deep learning.

CUDA Cores

This GPU contains 6,912 FP32 CUDA cores, which are the general-purpose processing units used for parallel tasks. In addition, it can also have 432 third-generation Tensor Cores, designed for high-performance computing and AI tasks. These cores can be optimized to manage extremely parallel computations to make them very effective for demanding workloads like AI inference, HPC, scientific simulations, and also deep learning. In addition, these can also work as workhorses for performing the parallel tasks that speed up these applications.

Third-Generation Tensor Cores

The third-generation tensor cores in the GPU provide significant performance boosts, which support a wider range of data types like the new TF32 precision as compared to earlier generations. These CUDA cores accelerate high-performance computing and deep learning workloads by up to 312 TFLOPS within FP32 precision for deep learning. Thus, provides up to 2x higher performance for sparse models through the Structured Sparsity feature. These specialized cores can speed up matrix operations by supporting a wide range of precisions like FP32, FP64, TF32, FP16, BFLOAT16, INT4, and INT8.

Sparsity Acceleration

Sparsity acceleration in the NVIDIA A100 is a hardware-enabled feature within its third-generation Tensor Cores. It leverages fine-grained structured sparsity within deep neural networks to double the compute throughput for improving power efficiency and AI inference.

This acceleration provides up to a 2x performance improvement for sparse matrix operations by bouncing computations on nil values. Thus, this acceleration is mostly effective for deep learning inference & training with a specific 2:4 sparsity pattern, where two out of each four elements within a matrix are set to zero. The software stack of this GPU, like cuSPARSELt libraries, automatically manages the pruning & density of models to control this hardware feature.

High-Bandwidth Memory or HBM2/HBM2e

The A100 GPU can be equipped with up to HBM2e memory in 40GB/80GB variants by providing over 2 TB/s of high memory bandwidth to manage complex models and large datasets. This memory is actually stacked on a similar package like the GPU by allowing for huge memory bandwidth & efficient data transfer. Thus, it is essential for AI and high-performance computing workloads.

Large Caches

This GPU features a large L2 cache – 40 MB, which can be shared across all SMs (Streaming Multiprocessors). In addition, every SM has a combined and configurable L1 cache as well as a shared memory unit for a total of 192 KB of capacity. It combines L1/Texture cache/shared memory system to enhance compute performance & reduce data transfer bottlenecks.
This cache performs as a shared data reservoir for all SMs, which decreases the need to access slower global HBM2 memory. It reduces latency by storing regularly used data and enhances data reuse, decreases memory bandwidth pressure & allows larger batch sizes for training & inference.

Multi-Instance GPU or MIG

MIG technology allows the GPU to be partitioned safely into different independent GPU instances, where each has its own dedicated cache, compute cores, and memory. Thus, this optimizes resource utilization within multi-tenant environments. It allows many users & workloads to run on one physical GPU in parallel with guaranteed QoS. Therefore, this enhances GPU utilization by allowing smaller workloads to run securely, independently & without affecting each other.

Third-Generation NVLink

This Third-Generation NVLink is a high-speed interconnect technology in this GPU that doubles the GPU-to-GPU direct bandwidth. Therefore, it uses 12 links for each GPU in place of the 6 in the earlier generation. It is essential for scaling applications across various GPUs within a single system. This technology is essential for AI and HPC workloads by allowing several GPUs to communicate at maximum speeds within a server.

PCI Express Gen 4 Support

This GPU can support the high-speed PCIe Gen4 interface that connects the GPU to the rest of the CPU & motherboard of the system. Its main function is to double the transfer bandwidth as compared to earlier PCIe Gen3. Thus, it improves data transmission speeds with components of other systems. Therefore, it provides a significant boost in data transfer speeds compared to the previous generation, PCIe Gen 3.

NVIDIA A100 Software Ecosystem

The NVIDIA A100 CPU can be supported with a complete software ecosystem, designed for AI, HPC, and accelerated computing. Therefore, this software stack contains core drivers, libraries, optimized application frameworks, and programming environments that typically run on a Linux operating system. The NVIDIA A100 software ecosystem includes different components, such as the following.

Operating System

The A100 GPU supports major Linux distributions like RHEL (Red Hat Enterprise Linux), CentOS, and Ubuntu. In addition, it can also be compatible with particular Windows Server editions, mainly for certain workloads.

NVIDIA Drivers

This GPU software system needs a specific data center GPU driver. So the minimum version for this GPU is R450 with the latest production branch drivers suggested for complete feature support & stability. CUDA Toolkit is the initial development environment used for GPU acceleration. This GPU needs CUDA 11.0 to control the features of the Ampere architecture.

Core Libraries

This GPU needs a set of GPU-accelerated libraries for performance, like cuDNN, cuBLAS/cuFFT, NCCL, AI & HPC Frameworks.

Inference Optimization

The NVIDIA TensorRT is used for optimizing trained AI models for low-latency inference and high-throughput deployments.

System Management

Software like the DCGM (Data Center GPU Manager) & NVIDIA Fabric Manager are used for managing, configuring, and monitoring the interconnects and GPUs.

Containerization

The software stack uses containers with the NVIDIA Container Toolkit by allowing GPU-accelerated Docker containers to run effortlessly.

Therefore, this complete software stack is frequently available as an element of the NVIDIA NGC containers or DGX OS. It ensures that developers can simply access & use the complete potential of the GPU for a broad range of demanding applications.

NVIDIA A100 GPU vs NVIDIA H100 GPU

The difference between the NVIDIA A100 GPU and the H100 GPU includes the following.

NVIDIA A100 GPU Maintenance

NVIDIA A100 GPU can be maintained by focusing on thermal management through high-efficiency cooling systems, right airflow & regular cleaning to avoid dust buildup. In addition, firmware and drivers must be updated; check temperatures with the nvidia-smi tools. Adjust power settings and workloads to balance heat output and performance. Finally, check & replace thermal paste sometimes for long-term use to ensure the best heat transfer.

Advantages

The advantages of the NVIDIA A100 Tensor Core GPU include the following.

• This GPU has significant performance, which is enhanced for data analytics, high-performance computing, and AI because of its high-bandwidth memory & third-generation tensor cores.
• It can also feature MIG technology for dividing the GPU, structural sparsity for up to 2x higher performance & Next-Generation NVLink for quick GPU-to-GPU communication. Thus, these features can make it a powerful and versatile accelerator for data centers.
• The Third-Generation Tensor Cores can provide up to 20x higher performance as compared to earlier generations by enhancing both training & inference efficiency.
• It provides huge parallel processing power, up to 2 TB/sec high memory bandwidth & large L2 cache.
• Its new TF32 or TensorFloat 32 numerical format can speed up calculations for AI without needing code changes.

Disadvantages

The disadvantages of the NVIDIA A100 Tensor Core GPU include the following.

• Its initial cost is high due to its high-performance computing and data center focus.
• This GPU needs up to 400W of significant power for the SXM4 variant, thus demanding robust & frequently specialized cooling solutions.
• The A100 architecture is less power-efficient and lacks some specialized features found in the latest Hopper-based H100.
• This GPU performance is slower than the H100 for large language model training & real-time inference tasks.
• The H100 provides significantly higher memory bandwidth & faster AI performance, particularly with transformer models.
• Its HBM2e memory has less bandwidth compared to the HBM3 memory of H100, which can generate a bottleneck for massive datasets and memory-intensive applications.
• This GPU is not designed for gaming or graphics workloads & lacks physical display connectors.
• It is not fit for consumer-grade motherboards & may have specific software or driver compatibility requirements within non-standard or certain multi-threading configurations.

Applications

The applications of the NVIDIA A100 GPU include the following.

• THE NVIDIA A100 GPU is used in demanding computational tasks like HPC, AI, data analytics, healthcare, finance, and scientific research.
• This GPU accelerates AI model training & inference by powering applications like seismic imaging, financial modeling, and genomics.
• This is extensively used in research institutions, enterprises, and cloud data centers to improve processing efficiency by enabling the latest breakthroughs.
• It accelerates the training process of complex AI models & makes predictions.
• It improves performance mainly for AI networks through optimizing them to be “sparse”.
• This GPU is used in scientific research: for data analysis and complex simulations in genomics & scientific discovery fields.
• It helps companies with seismic imaging & reservoir simulation to improve efficiency.
• It analyzes huge amounts of data for insights.
• This GPU provides ready-to-use software optimized for data analytics and AI workloads.
• It is used to optimize trading algorithms & improve risk management strategies.
• It is used in healthcare to accelerate drug discovery & personalized medicine development.
• In addition, it can support cloud service providers to deliver AI services and high-performance computing to customers.

In summary, the NVIDIA A100 is a very powerful data center GPU, built on the NVIDIA Ampere architecture. It is designed for data analytics and high-performance computing to provide significant performance gains over earlier generations. It supports a variety of precisions, a huge 80GB HBM2e memory option with 2 TB/s BW & the capacity to be divided into Multi-Instance GPU instances for supplementary workload management. Here is a question for you: What is the NVIDIA H100 GPU?