New ClusterStor E1000 HPC/AI storage system from Cray, a Hewlett Packard Enterprise company

Introduction / Executive Summary

High-performance computing (HPC) is changing fundamentally with disruptive implications for HPC storage. The classic modeling and simulation workloads running on supercomputers and large HPC clusters are joined by new workloads like machine learning and deep learning (ML/DL) running on mission- or business critical workflows on the same machine. In addition, high-performance data analysis (HPDA) of experimental and observational data from much more powerful instruments and machine-generated data from Internet of Things (IoT) sensors at the edge — soon connected by fifth-generation wireless (5G) cellular networks — is integrated into the workflow.

Cray calls this new era the exascale era and it’s characterized by converged workloads for crucial workflows running on the same machine, often simultaneously.

Cray saw this coming years ago and has developed the new Cray® ClusterStor® E1000 HPC/AI storage platform to enable HPC users to efficiently cope with the data explosion in this new paradigm.

Storage challenges facing today’s customers

Most customers have separate infrastructures for classic HPC (modeling and simulation workloads) and AI (ML/DL workloads) today.

• Supercomputers and HPC clusters mainly based on compute nodes with CPUs served by HDD-based parallel HPC storage with storage capacity measured in petabytes (PB).
• Artificial intelligence (AI) infrastructures mainly based on GPUs served by SSD-based enterprise network attached storage (NAS) with storage capacity measured in terabytes (TB).

The current status quo becomes a challenge once AI methods like ML/DL are running together with modeling and simulation workloads in workflows on supercomputers and HPC clusters. More than 50% of HPC users are already running machine learning programs today.[1] In general, HDD-based storage was designed for the write-intensive, sequential input/output (I/O) for large files. It is not well suited to serve the read-intensive I/O of a larger number of often small files that need to be accessed in random order during the training phases of ML/DL models.

As workloads converge, customers cannot easily stay on current HPC storage infrastructure. If they do, I/O bottlenecks will result in job pipeline congestion and expensive compute infrastructure will idle waiting for I/O to complete. At the same time, they also cannot scale their current AI storage — deployed in capacities of terabytes — to the petabyte scale capacities needed by supercomputer and HPC clusters running converged HPC and AI workloads. SSD storage is much more expensive than HDD storage today and IDC research shows that SSD in 2023 is projected to still be eight times more expensive than HDD in $/GB. [2]

Cray ClusterStor E1000 solves the exascale storage challenge

Customers should consider moving to the Cray® ClusterStor® E1000 HPC/AI storage solution.

“It is a flexible HPC/AI storage platform that leverages the strength of both SSD and HDD without incurring their respective weaknesses. ClusterStor E1000 solves the I/O bottleneck in the era of converged workloads without breaking the bank by orchestrating the data flow with the workflow through intelligent software. Cray saw the storage challenges of converged workloads coming as early as 2017 when development of the next generation ClusterStor storage system began,” says Uli Plechschmidt, director of storage product marketing for Cray.

Introducing the Cray ClusterStor E1000

Conclusion

The converged workflows of the exascale era break legacy HPC storage and AI storage models. The “cost of doing nothing” is either massive job pipeline congestion through I/O bottlenecks or spending most of the total system budget on storage. A storage solution is needed for the new era.

“Unlike HPC/AI storage-only vendors we believe that storage is a means to an end. The end being insights and breakthroughs that can make the world a better place to work and live in,” says Plechschmidt. “Cray has architected the ClusterStor E1000 in a way that can meet the storage requirements of converged workloads in the most efficient way. Exploiting the strengths of HDD and SSD without incurring their weaknesses — for HDD the challenge to serve random I/O of large numbers of small files and for SSD the high cost per gigabyte — by intelligent hardware design and through intelligent software that aligns the data flow with the workflow. Due to that differentiated design, customers who choose the ClusterStor E1000 typically will be able to achieve their storage requirements in a more efficient way — enabling them to spend more of their total system budget on CPU/GPU compute nodes when compared to alternative HPC/AI storage offerings.”

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References

1] Intersect360 HPC User Budget Map Survey: Machine Learning’s Impact on HPC Environments, September 2018.

2] IDC, Worldwide 2019-2023 Enterprise SSD and HDD Combined Market Overview, June 2019.

 

About Cray

About Cray Inc.

Cray, a Hewlett Packard Enterprise company, combines computation and creativity so visionaries can keep asking questions that challenge the limits of possibility. Drawing on more than 45 years of experience, Cray develops the world’s most advanced supercomputers, pushing the boundaries of performance, efficiency and scalability. Cray continues to innovate today at the convergence of data and discovery, offering a comprehensive portfolio of supercomputers, high-performance storage, data analytics and artificial intelligence solutions.  Go to www.cray.com for more information.

CRAY and ClusterStor are registered trademarks of Cray Inc. in the United States and other countries, and Shasta and Slingshot are trademarks of Cray Inc. Other product and service names mentioned herein are the trademarks of their respective owners.

Copyright 2019