![]() ![]() ![]() ![]() Panasas, the Panasas logo, PanFS, and ActiveStor are trademarks or registered trademarks of Panasas, Inc., in the U.S. For more information, visit © 2022 Panasas, Inc. The Panasas data engine solves the world’s most challenging problems: curing diseases, designing the next jetliner, creating mind-blowing visual effects, and using AI to predict new possibilities. The company’s flagship PanFS® data engine and ActiveStor® storage solutions uniquely combine extreme performance, scalability, and security with the reliability and simplicity of a self-managed, self-healing architecture. The Panasas data engine accelerates AI/ML and high performance applications in manufacturing, life sciences, energy, media, financial services, and government. Panasas builds a portfolio of data solutions that deliver exceptional performance, unlimited scalability, and unparalleled reliability – all at the best total cost of ownership and lowest administrative overhead. “AI/ML practitioners are doing some of the most revolutionary work today, and I am excited to help develop the benchmarks that will enable them to determine the storage systems they need to carry out their projects.” “It is an honor to be a co-chair of the MLPerf Storage working group, and I look forward to the meaningful progress that this team will accomplish,” said Anderson. “I’d like to thank Panasas for contributing their extensive storage knowledge, and Curtis specifically for the leadership he is providing as a co-chair of this working group.” “The end goal of the MLPerf Storage working group is to create a storage benchmark for the full ML pipeline which is compatible with diverse software frameworks and hardware accelerators,” said David Kanter, founder and executive director of MLCommons. MLCommons invited Panasas to attend the foundational meetings, after which Curtis Anderson, software architect at Panasas, was named co-chair. The discussion was timely, as MLCommons had been in the early stages of forming the MLPerf Storage working group to develop a storage benchmark that evaluates performance for ML workloads including data ingestion, training, and inference phases. Panasas approached MLCommons to discuss the storage challenge in the ETL (extract, transform, and load) process and its impact on the overall performance of the ML pipeline. MLCommons, the open and global engineering consortium dedicated to making machine learning better for everyone, promotes widespread ML adoption and democratization through benchmarks, large-scale public datasets, and best practices. All Panasas solutions are powered by the company’s flagship PanFS ® parallel file system, a reliable and autonomic data engine that orchestrates networked servers into a single file system serving data to clients at up to hundreds of gigabytes per second. The Panasas ActiveStor ® portfolio features the all-NVMe ActiveStor ® Flash system, which enables outstanding small and random file performance and enhanced support for AI/ML projects. Panasas builds industry-leading price/performance data storage solutions that drive innovation across a vast range of computing environments. Panasas will work with MLCommons to help steer these benchmarks by establishing best practices for measuring ML storage performance and ultimately helping to develop the next generation storage systems for AI/ML. SAN JOSE, Calif., JPanasas ®, the data engine for innovation, today announced its collaboration with MLCommons™, the consortium behind MLPerf™, to create industry-wide benchmarks for machine learning (ML) storage. Panasas Joins MLCommons to Advance Machine Learning Storage Innovation Enterprise-Grade Reliability and Availability.Artificial Intelligence (Deep Learning).
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