Healthcare Industry

Examining Healthcare

Over the next five years, the Federal Government is projected to spend as much as $45 billion on healthcare technology systems, largely through a stimulus program known as the Health Information Technology for Economic and Clinical Health Act, or HITECH. Healthcare executives are coming under pressure to produce results and use their ever decreasing IT budgets to prepare for the rapid growth moving towards the Electronic Medical Records within their own hospital and health organizations. Many times the focus is on adding the EMR applications, hardware, and storage to an already overwhelmed data center.

Cirrascale has worked with several leading healthcare firms to solve their unique and overall demanding needs. We are an leading provider of healthcare deep learning infrastructure that help you continue to deliver the best experiences you can to your patients. We have developed data-ready configurations and work with you to tailor these architectures so your facility can move to open-architected, next-generation healthcare solutions today.

Cirrascale works closely with its partner NVIDIA to deploy some of the world's fastest deep learning solutions for healthcare. In fact, NVIDIA recently released the Tesla M40 24GB GPU accelerator which was purpose built for scale out deep learning training deployments. It dramatically reduces the time to train deep neural networks — as much as 13X faster than a CPU. Additionally, it provides 24GB of ultra-fast GDDR5 memory, which enables a single Cirrascale rackmount server to house up to an incredible 192GB of GPU memory. To learn more about these solutions, review the featured resources below.

Featured Resources for Healthcare

Healthcare Deep Learning Servers

Over the past several years, Cirrascale has been working with a variety of application developers as well as data scientists in both industry and academia. Together, our goal has been to continue making groundbreaking improvements in developing some of the most advanced hardware capable of increasing the overall speed and flexibility of deep learning training and inference by using multi-GPU compute solutions.

Visit Deep Learning Servers Page
Scaling GPU Compute Performance White Paper

GPU chip manufacturers have been providing more computational power within each GPU card. Recently, this includes packing multiple GPUs within each card, such as the NVIDIA® Tesla® K80 Dual-GPU Accelerator cards, which contain two GPUs per card. Additionally, with new iterations, they continue to increase overall memory size and bandwidth accessible to those GPUs to accelerate multiple HPC applications. Read this paper to discover the best way to scale GPU compute performance.

Download Cirrascale GPU Compute Performance White Paper
NVIDIA® GPU Application Catalog

NVIDIA identifies over two hundred applications for a wide range of industries already optimized for GPUs, including High Performance Computing. Discover if your application is supported and put those applications to work with a Cirrascale GB series blade server.

Download NVIDIA® GPU Application Catalog

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