Ryft ‘s Open Platform Aims to Supercharge Analytics Performance by Eliminating I/O Bottlenecks

White Papers > Data Storage > Ryft ‘s Open Platform Aims to Supercharge Analytics Performance by Eliminating I/O Bottlenecks

The new Ryft ONE open platform from Washington, D.C., area–based Ryft Systems Inc. aims to supercharge performance on even the most challenging analytics workloads with a balanced architecture that tightly couples compute, I/O, and storage in a scalable U device designed for easy integration into existing cluster and other server system environments. The Ryft ONE platform exploits the company's decade-long experience creating analytics platforms used primarily by military and other government customers, including active-duty personnel in war zones. The platform is also designed to simultaneously accelerate both batch data and streaming data — a new requirement more and more user organizations face today.

Especially during the past six years, the markets for data-intensive high-performance computing (HPC) and advanced commercial data analytics have been converging to form a fast-growing fusion market that IDC calls high-performance data analysis (HPDA). Simply put, this is the market for big data needing HPC resources. IDC forecasts that by 2018, the HPDA server market will reach $2.6 billion (23.5% CAGR) and the HPDA external storage market will add $1.6 billion (26.5% CAGR). Primary HPDA market drivers are the organic growth of data-intensive simulation and the need to process data analytics workloads that are too complex and time critical for enterprise IT technology to handle alone. IDC works with a growing number of government and commercial organizations that save millions of dollars per year by using HPC resources for advanced analytics.

Even the expensive hyperscale clusters that dominate today's HPC and enterprise IT markets cannot handle the most challenging HPDA workloads efficiently. These workloads need ultrafast communication among processing elements and between processing elements and memory. The loosely coupled, physically distributed compute-and-memory nodes of today's high-end clusters turn daunting data analytics problems into I/O bottlenecks that can slow solution times to a crawl.

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