DDN|Grid & Cluster Computing
Accelerating discovery by speeding data access and shared access to results
Modern clusters and grids of multi-core CPU servers access, consume and create data far faster than conventional storage systems can keep pace. By speeding data to these ravenous processors and then providing shared access to the results, DDN plays a mission-critical role in accelerating workflows powering engineering breakthroughs, speeding scientific insight, and shortening time to market with far faster results than ever before achievable with conventional storage systems.
DDN | GRIDScaler is the fastest, most robust Parallel File System Appliance on the planet. Accelerating data access and providing high speed file ingest to a broad range of applications:
- Seismic data acquisition and interpretation
- Financial and Market data analysis
- Engineering Design
- Monte Carlo simulation
- High-performance parallel computing
- Computational physics, chemistry
- Weather modeling
- Real-time surveillance and recognition
With turn-key appliance simplicity and enterprise reliability, the massive amounts of data generated in scientific research, design, simulation, and testing of new products, much of it in unstructured large files, are quickly accessed and shared among scientist and engineers across the organization using a single global namespace enabling a powerful enterprise-wide workflow accelerating results.
DDN also optimized data management in grid and cluster computing environments with integrated information lifecycle management, HSM, and backup for low costs data administration, preservation and of digital assets and business continuity. Improving storage utilization and lowering costs.
The Most Trusted System Architect Team
With more than a decade of experience and hundreds of successful grid and cluster computing deployments, the DDN worldwide team of HPC storage and systems architects and engineers design and deliver HPC systems on time and budget, better than any other technology company.




















































