ClusterStor 1500 Departmental Scale-Out Storage for HPC
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Transcript of ClusterStor 1500 Departmental Scale-Out Storage for HPC
New Product Launch
ClusterStor™ 1500
Departmental Scale-Out Storage for HPC
April 29, 2013
“Missing Middle” Widely Distributed Industrial Users – Many Years Underserved
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Department Level
Critical Mass Exploding Now!
Middle Market Shifting from
Vendor Push to Strong User Pull
*National Center for Manufacturing Sciences
Previous Barriers • Lack of affordable HPC
technology for mid market
• Too hard to build your own using piece parts
• Lack of skills and expertise
• No time, need results now
• Lack of scale, proprietary lock in, too complex
Wide Open Growth
• Affordable engineered HPC solution for mid market
• Pre-built, optimized and highly efficient appliance
• Easy to use with integrated solution management
• Out of box ready to use
• Best of large HPC, packaged for departmental users
ClusterStor 1500 Resolves Departmental Capabilities Gap
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Affordable Lustre® Appliance Opportunity Lustre Expands Beyond the Top 100
NFS struggles to keep up with departmental 32 node and above sweet spot
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IDC HPC Storage Market Segmentation Revenue 2011
$0 $200 $400 $600 $800 $1,000 $1,200 $1,400 $1,600 $1,800
Workgroup
Departmental
Divisional
Supercomputer
Workgroup Departmental Divisional Supercomputer $Ms $460 $1,322 $473 $1,657
Up to 16 Nodes
16 to 128 Nodes
> 512 Nodes
128 to 512 Nodes
NFS
Affordable
Easy to Use Lustre
Appliance Opportunity-
Lustre Dominates Maximum Scale-Out- TOP 100
ClusterStor 1500 Features & Benefits Departmental Scale-Out Storage for HPC and Big Data
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No compromise best-in-class affordable departmental
storage
Superior ROI
Works out of the box with ease of use
Fully integrated solution visibility and management
Proactive support and professional services
Investment protection
Purpose Built to Satisfy Department Level Needs
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ClusterStor 1500 value for departmental HPC
– Cost-efficient
– Easy to deploy and manage
– Works out of the box
– Reliable even under heavy workloads
– Highly scalable in performance and storage capacity to match evolving compute cluster needs
Thank You [email protected]