Discover the data engine for all your HPC and AI/ML applications

You’ve entered the era where modern high-performance computing (HPC), artificial intelligence (AI), and machine learning (ML) have converged into a unified ecosystem. HPC applications use AI/ML for large-scale modeling and simulation optimization, and data storage access patterns are diverse and require a wide performance profile. It all adds up to an urgent demand for a storage platform designed for dynamically changing workload patterns and volumes.

Gain extreme flexibility with no mixed results for mixed workloads

39% of global survey respondents find juggling multiple storage systems for different workloads time consuming. 38% say they have wasted time on tuning the overall infrastructure for each workload.¹

Challenge

Tuning and re-tuning mixed HPC and AI/ML workloads is a complex, iterative, and time-intensive process that requires specialized expertise to ensure optimal performance and reliability.

Solution

The PanFS^® parallel file system automates tasks — including optimal data placement, capacity balancing, tuning and retuning, and recovery logic — to deliver consistent high performance that actively adapts to diverse workloads with different I/O patterns that other parallel file systems struggle to support concurrently.

Banish the complexity of HPC storage

96% of survey respondents in US, UK, and Germany report challenges in building and managing high-performance storage.²

Challenge

Now that HPC has moved to the front room of enterprise IT, teams across all the top verticals face similar challenges: uneven performance, complex management, security concerns, and a scarcity of specialty knowledge.

Solution

With a single point of management, an intuitive interface, and automated enterprise functions, Panasas solutions require no HPC expertise. Easily managed at any scale by a single IT administrator, consolidating high-performance unstructured data workloads onto a scale-out ActiveStor^® platform simplifies storage in your data center and drastically reduces operational expenses.

Get the performance you need and the enterprise-class reliability you can’t afford to lose

1.7 days. Other HPC storage systems experience an average of 9.8 failures per year, with the average time to recover from a storage system failure clocking in at 1.7 days.³

Challenge

In the face of converging workloads and rapidly growing volumes of data, compute requirements are increasing, and data storage failures cost more than ever before — anywhere from $100,000 to over $1 million.⁴

Solution

The PanFS^® parallel file system delivers the most innovative reliability algorithms — per-file erasure coding, quadruple redundant directory copies, parity checks, and automatic background capacity balancing — to keep the system online and automatically tuned to your workloads. Both the storage system and the data on it benefit from fault tolerance and self-healing that together maximize uptime.⁵

Never trade manageability, usability, and reliability for performance again. Turn the convergence of HPC and AI/ML into a competitive advantage with a scale-out storage system that delivers non-stop high performance for whatever type of workload you can throw at it.

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¹ Statistics based on an independent survey conducted by Vanson Bourne, with the primary question being: “What challenges is your organisation [sic] facing when building and managing its storage infrastructure for high-performance applications?”
² Ibid.
³ Hyperion Research cited in Panasas, “Frequent Storage System Failures and Lost Productivity are the Norm in HPC,” May 2020.
⁴ Ibid.
⁵ One Panasas storage user reported zero instances of unplanned downtime in eight years. (We might need to check to see if this is a Guinness World Record.)