Building the GPU-accelerated data center – insideHPC



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Data volumes have been increasing for years – and researchers expect them to continue to increase in the years to come.

Meanwhile, cutting edge computing, hyperconnectivity powered by 5G, artificial intelligence (AI) and other new technologies we’ve been hearing about for years are becoming real solutions, not research projects.

For these reasons and more, organizations and the techs that support them are forced to reinvent the data center that is still the heart of most data-dependent organizations. Modern data centers need to be ready for the future, ideally without downtime or unplanned costs.

From academia and aerospace / defense to finance, life sciences and high performance computing, standing still means quickly falling behind. You become less competitive in terms of innovation, mission execution, and even attracting and retaining talent.

Fortunately, there is a solution today that many organizations can implement to address many of these issues: building a data center with built-in graphics processing unit workload acceleration ( GPU).

Why?

It is well known that GPUs can accelerate deep learning, machine learning, and high performance computing (HPC) workloads. However, they can also improve the performance of data-intensive applications. Virtualization allows users to take advantage of the fact that GPUs rarely run near capacity. Leaving GPU hardware aside from software virtualization, GPU acceleration is essentially scaled for each task.

Additionally, many exciting new technologies are built on GPUs or explicitly require the acceleration provided by GPUs. While AI is certainly an example of this, the same highly parallel mathematical operations that make GPUs so valuable to any algorithm that can exploit embarrassing parallel approaches can also speed up even the most demanding workloads in corporate data centers and hyperscale.

GPU-based infrastructure requires fewer servers, dramatically improves performance per watt, and delivers unmatched performance. Consider, for example, the 20-fold improvement in NVIDIA’s Ampere architecture over previous generations of GPUs due to many architectural innovations and the increase in the number of transistors. The cost of GPUs has fallen in recent years, as the hardware infrastructure and software stacks that can take advantage of them (both storage and compute) have grown rapidly. As a result, you can more accurately predict future performance capabilities and, therefore, the costs of a potential workload expansion.

How? ‘Or’ What?

GPUs are ideal parallel processing engines with high speed memory with a lot of bandwidth. They are often more efficient and require less floor space than central processing units (CPUs), which have traditionally served as a performance engine in data centers. To further strengthen the case for GPU adoption, GPU vendors such as NVIDIA are pre-testing and bundling the software needed to run the workload.

Hardware infrastructure providers such as Thinkmate (Thinkmate.com) have spent the past several years ensuring that customers of all kinds have access to the compute and storage technology they need not only to keep up, but to stay ahead of their competitors in the age of data centers. of GPU. Today, the options are wider than ever.

You can get systems that generate massively parallel processing power and offer unmatched networking flexibility. Choices include two double-wide GPUs or up to 5 expansion slots in a 1U, for performance and quality optimized for the most compute-intensive applications. At the same time, thanks to experienced GPU engineers, these unique designs come with Gold-level power supplies, energy-saving motherboards and enterprise-class server management to optimize cooling even for the most demanding applications. more demanding.

By working with experienced infrastructure providers with access to the latest technology and training to inform their system designs, organizations and their data center administrators can transform or scale up existing data centers to be more agile and performant without breaking the bank. nor stop operations.

To learn more about GPU-accelerated data centers, join the upcoming Thinkmate and PNY live webinar via this registration page. We’ll dive into the future of the data center, why the GPU is crucial, the technology behind GPU acceleration, and what kinds of options exist for different industries or types of organizations.

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