Which file system is most suitable for allowing multiple compute instances to read/write data concurrently without any data loss?

The most suitable file system for allowing multiple compute instances to read and write data concurrently without any data loss is parallel file systems such as Lustre, IBM Spectrum Scale (GPFS), and BeeGFS.

Parallel file systems are designed specifically to handle high levels of concurrent access from multiple clients, which is essential in high-performance computing (HPC) environments where tasks often require significant input/output operations. These systems manage data distribution across multiple nodes or servers, allowing simultaneous read/write operations while maintaining consistency and integrity of the data. This architecture helps to eliminate bottlenecks that can occur when multiple processes attempt to access the same data at the same time.

For instance, Lustre achieves high performance by striping files across different storage devices, thus allowing multiple compute instances to access various parts of a file simultaneously. This reduces the time it takes to read and write large datasets, which is critical for applications in scientific computing, data analysis, and other computationally intensive tasks.

In contrast, while distributed file systems manage data across different nodes, they may not provide the same level of performance or data handling capabilities under high-concurrency scenarios as parallel file systems. Network file systems, like NFS and SMB, are generally optimized for ease of access and file sharing