The Nvidia A100 GPUs are a series of parallel processors used by several companies and businesses.
For example, these A100 GPUs can be used for making video games, modeling new aircraft, processing design work, and many more exciting ventures. In addition, it is the only company that can offer a good solution for the breakthrough demands of AI and Deep Learning.
The new GeForce A100 GPUs (codenamed ‘Maxwell’) are made using TSMC’s 28nm manufacturing process – the same one used in the latest chips from Apple and Qualcomm. It allows Nvidia to make far more efficient chips without sacrificing their fantastic performance. The new A100 GPUs are also 50% more power-efficient than previous generations.
Let’s talk about the brand-new NVIDIA Tesla A100 GPUs. In case you haven’t heard about them yet, they’re revolutionary. A single NVIDIA Tesla A100 GPU was faster processing big data than 250 eight-core CPUs making it ten times more energy-efficient.
It contains a whopping 12GB of onboard memory, allowing fast data transfer between GPUs. Plus, each GPU features 3,072 parallel computing cores, along with 2GB of onboard memory and 8GB of GDDR5 memory for multi-display connectivity and rendering performance.
The NVIDIA Tesla A100 GPUs are the most powerful, most efficient, and most versatile GPUs for HPC. These new GPUs deliver double the performance per watt of previous generations and can be mixed and matched with any other Tesla GPU. This flexible design allows customers to scale their applications across one or many GPUs while keeping a consistent codebase.
You’ll also see that this product is of great value. The NVIDIA Tesla K80 GPU launched at $3,000 per GPU, and now with the introduction of the Tesla A100, we’re able to offer similar performance at a fraction of that price. It’s a game-changer for anyone looking to deploy accelerated computing in the cloud quickly.” – Ian Buck, vice president of accelerated computing products, NVIDIA
What Does It Offer?
The NVIDIA Tesla A100 GPU delivers the following:
- Up to 1.5x faster performance than its predecessor, the K80 GPU
- Up to 4x faster performance per watt than its predecessor, the K80 GPU
- Up to 3x faster performance per dollar than its predecessor, the K80 GPU
- High-bandwidth, low-latency communication between GPUs within an NVLink™ connected node with support for up to 8 GPUs in a system (8–way multi-GPU supported)
- Support for all major operating systems
- Innovative new system interconnects technology delivers unprecedented memory bandwidth from the CPU to the GPU, enabling the Tesla A100 to deliver up to 20X faster performance than CPU-only systems.
- Deep Learning Inference
The Inference
The inference is the most common operation performed in deep neural networks. The premise is a two-step process:
1. Forward Pass
Compute the output of the network.
2. Backward Pass
Compute gradients concerning all of the parameters.
For example, for an elementary network that takes an image as input and produces a single number as output, the forward pass computes the dot product of the information with each weight. Next, it sums those weighted products to have an intermediate result. It then adds those intermediate results across all weights to produce the final output. The backward pass then uses gradient descent to update each weight according to its contribution to building a wrong answer.
The forward pass computes a matrix-vector multiplication followed by a summation; there are efficient GPU-specific ways to do this, significantly if we can reduce the intermediate result by a constant factor by padding or binning inputs (e.g., turning [1, 2, 3] into [1, 2, 3, 4]).
The backward pass involves computing the derivative of an error function concerning each parameter; this works best when each parameter has its independent error function (and not simply its weight times an error function).
Astounding Technology
The NVIDIA Tesla A100 GPU accelerator delivers up to 4.5x the application performance of the prior-generation Tesla M2050 GPU accelerator. It is designed to significantly reduce CPU usage, enabling more applications to run in a single server node. With CUDA parallel computing technology, NVIDIA Tesla GPUs deliver up to 80 percent faster performance on various applications than competing solutions, including Intel Xeon Phi coprocessors.
Tesla accelerators provide the necessary computational horsepower for demanding tasks such as real-time 3D rendering, computational fluid dynamics (CFD), computational finance, and seismic analysis.
Tesla accelerators are easy to use. Install Linux or Windows drivers, connect the GPU, and you’re ready to go—no lengthy installs required. And with single-slot, high-density form factors, Tesla accelerators are ideal for space-constrained environments such as labs and data centers.
Tesla accelerators are ready for the future. The Tesla Accelerated Computing Platform is designed to run artificial intelligence (AI) algorithms and traditional HPC workloads. In addition, it provides a unified architecture that efficiently enables IT managers to transition from HPC to AI.
The End
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