Rescale Announces Cloud File System for Automated Fast, Parallel Storage to Power Secure Collaboration in Applied AI and R&D Computing

New Rescale storage service brings automated administration, secure collaboration, and unified data management using the latest cloud services and parallel file system technologies to accelerate innovations enabled by R&D simulation and AI-assisted engineering.

Rescale, a leader in high performance computing built for the cloud, announces Rescale Cloud File System, a new storage service that automates parallel file system management for applied AI and R&D computing. Rescale Cloud File System brings automation and control to leading high performance storage technologies enabling collaboration with data security and cost efficiency.

Modern product development increasingly applies AI and R&D computing across multiple stages in the product life cycle, creating an explosion of data that is fragmented across the enterprise. The accumulation of petabytes of data across various storage stacks and organizational silos makes sharing files and collaboration more important than ever.

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Yet, enterprises are challenged to facilitate collaboration between developers, engineers, and researchers who need to share insights to make timely and accurate product decisions.

While a variety of high performance storage technologies exist, complexity in managing them prevents many enterprises from delivering secure, shared file access. To support modern HPC and AI computing, IT teams often need to set up and manage multiple storage technologies while managing access, security, and cost-performance tradeoffs. Manual storage management can create costly provisioning delays, security risks, or obstacles to sharing critical insights.

Rescale Cloud File System brings automation and policy-based control to the latest high performance cloud-based storage technologies for turnkey deployment and simplified management. Rescale Cloud File System automates file systems from a variety of leading cloud storage providers such as Microsoft Azure Managed Lustre, ensuring users and their applications have fast, parallel access to data while balancing cost and performance objectives.

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“At TempoQuest, we develop state-of-the-art technologies for weather and climate modeling,” said Samm Elliott, Director of Software Engineering and Customer Solutions at TempoQuest. “Delivering accurate and timely predictions requires extremely fast shared filesystem access across the large GPU clusters running our simulations. Effectively managing our massive storage challenges is critical for the success of our customers who rely on our predictions to make informed decisions regarding everything from launching rockets to emergency management in high-impact weather events.”

“Rescale makes it possible for Microsoft Azure customers to solve their engineering data challenges to build a foundation for an AI-driven future by seamlessly harnessing Lustre-powered file systems to meet the stringent demands of large-scale compute workloads,” said Dilip Sundarraj, Principal Product Manager, Azure Managed Lustre.

Rescale Cloud File System bolsters Rescale’s storage fabric to give users secure shared access to critical data, and increase engineering productivity, all while improving efficiency by automating the latest cloud technologies. As data complexity continues to grow, innovation-driven enterprises can now focus more on unifying their R&D teams to uncover new insights from advanced simulations, modeling, and applied AI initiatives.

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“Providing users secure shared access to engineering data is one of the biggest challenges our customers face to commercializing innovations faster,” said Edward Hsu, Chief Product Officer at Rescale. “Just as Rescale’s cloud-based control plane democratized access to specialized computing architectures at cloud-scale, Rescale Cloud File System enables engineering-driven organizations to provide users secure shared access to data across teams, associated to relevant projects, all while getting the best possible performance at the lowest possible cost.”

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