Technology empowering human development
BeeGFS is a hardware-independent POSIX parallel file system (a.k.a Software-defined Parallel Storage) developed with a strong focus on performance and designed for ease of use, simple installation, and management. BeeGFS is created on an Available Source development model (source code is publicly available), offering a self-supported Community Edition and a fully supported Enterprise Edition with additional features and functionalities.
BeeGFS works with lightly weighted, high performant service daemon(s) in the user space over the arbitrated filesystem, such as ext4, xfs, zfs, Hadoop. This allows users to release maximum bandwidths, the higher performance of the hardware realm and delivers network wires speed to the applications. The native BeeGFS client and server components are available for Linux on x86, x86_64, AMD, ARM & OpenPower, or any other CPU architectures.
BeeGFS transparently spreads user data across multiple servers. By increasing the number of servers and disks in the system, you can simply scale performance and capacity of the file system to the level that you need, seamlessly from small clusters up to enterprise-class systems with thousands of nodes.
BeeGFS is widely seen as an easy-deployable alternative to other parallel filesystems and is deployed at thousands of sites around the globe, providing fast access to storage systems of all kinds and sizes in all performance-oriented environments including and not limited to HPC, AI and Deep Learning, Lifesciences and Oil and Gas.
The BeeGFS userspace architecture is “state-of-the-art” allowing users to manage any IO profile requirements without performance restrictions and provides the scalability & flexibility that is required to run the most demanding HPC, AI, or business-critical applications.
BeeGFS allows customers to invest in scalable HPC and AI infrastructures, that deliver from small sites to large scale-out environments, relieving the full bandwidths of their hardware components. BeeGFS increases productivity by delivering faster results, enabling new data analysis methods without changing workflows or applications.