HPC and AI environments, by their nature, tend to be large and quite complex. Whether it’s pushing the boundaries in life and physical sciences or fueling the convergence of engineering innovation and artificial intelligence, it takes many computers operating together to simulate, analyze, or visualize the problems at hand. The massive quantity of data and the access performance needed to keep all those computers busy can only be achieved by a true parallel file system, one that is easy to use, durable, and simply a fast, total-performance storage system.

The VDURA® PanFS® parallel file system delivers the highest performance among competitive HPC and AI storage systems at any capacity while eliminating the complexity and unreliability associated with traditional data storage solutions. PanFS accomplishes all this using commodity hardware at competitive price points.

PanFS orchestrates multiple computer systems into a single entity that serves your data to your compute cluster. Through sophisticated software, multiple systems work together to support terabytes per second (TB/s) of data being read and written by your applications. PanFS manages this orchestration without manual intervention, continuously balancing the load across those systems, automatically ensuring resilience, scrubbing the stored data for the highest levels of data protection, and encrypting the stored data to safeguard it from unwanted exposure.

PanFS was the first storage system designed with the parallel file system architecture that is the de facto dominant storage architecture in HPC systems to this day. While the foundation for PanFS was laid over 20 years ago, the file system continues to leverage the latest technology advancements to provide the exceptionally high performance, unmatched reliability, and low-touch administration our customers have come to expect and rely upon.

In this document, we’re going to take a “breadth-first” tour of the architecture of PanFS, looking at its key components, then dive deep into the main benefits.