Financial Services Industry

Discovery in Financial Services

With the utterly immense amount of data being gathered from stock trades, portfolios, and the like, deep learning applications have found their way into the mainstream as a holy grail to helping sort and manage these data streams, while providing real results. From helping to predict market shifts like a sudden plunge in a currency's value, or looking at financial time series analysis, or even predictive analytics with massive real-world data, deep learning has enabled data scientists in financial services to aggregate and run machine learning workloads that produce real results from this data.

Cirrascale works closely with its partner NVIDIA to deploy some of the world's fastest deep learning solutions for financial services. 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 Financial Services

Financial Services 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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