In high-performance computing (HPC), storage protocol decisions directly impact performance, scalability, and operational complexity. For decades, parallel file systems (PFS) have been the default for high-performance workloads, but they come with steep management and tuning costs that are only magnified by the heavy data demands of AI development. Now, leading enterprises are turning to network file systems (NFS) for storage to avoid these complexities and ready themselves for the AI future.
This post compares PFS and NFS as HPC storage systems, and shows how modern NFS implementations can deliver PFS-class performance without the headaches.
NFS Storage in HPC: A Workhorse Built for Scale
Network file systems aren’t exactly new — they’ve been around in one form or another since 1984, and have had a long-standing presence in Linux-based HPC environments. Their appeal has remained strong largely due to their simplicity, broad client compatibility, and standards-based integration capabilities. They’re straightforward to set up, reliable, and easy to maintain.
The one drawback of NFS has been their potential performance limitations. Without built-in load balancing, NFS have often been considered less efficient for large file transfers or heavy data loads, especially when network traffic is high. This concern goes away with NFS-over-RDMA.
NFS-over-RDMA is a newer high-performance storage technology that combines the NFS protocol with Remote Direct Memory Access (RDMA). This innovation essentially bypasses the step of copying data from one network location to another at traffic-dependent speeds, and instead allows data to be copied directly between the memory of the client and the storage server. By minimizing the network’s CPU utilization in this way, NFS latency is substantially reduced, and data throughput is greatly accelerated.
The rise of NFS-over-RDMA is a game-changer for today’s HPC teams — allowing them to achieve parallel performance with standard NFS for any high-throughput HPC workload.
Parallel File Systems: Power with Complexity
Given the potential performance limitations of traditional network file systems, parallel file systems became a popular choice for HPC initiatives. Systems such as Lustre, GPFS (now IBM Spectrum Scale), and BeeGFS emerged as go-to HPC tools thanks to their high performance capabilities, achieved through techniques such as file striping, concurrent file access, and centralized metadata handling.
This performance, however, does come with some significant operational tradeoffs when running a parallel file system architecture:
Limited number of client agents and kernel modules, hindering compatibility.
Potential metadata bottlenecks, introducing unexpected latency.
Heavy maintenance and tuning requirements for large scale.
Complexity and downtime risk during system upgrades.
NFS vs. Parallel File Systems: Key Differences
How does NFS storage compare vs. Lustre, GPFS, and other parallel file systems for HPC? The key differences come down to performance, scalability, and overhead.
Performance
When it comes to HPC performance, PFS are designed for massive parallelism and can handle heavy data workloads. However, they require a very complex and costly set-up in order to do so, largely stemming from the need for specialized clients due to their less standardized structure.
NFS-over-RDMA has removed the performance barriers of legacy NFS and greatly accelerated data transfer speeds. This allows enterprises to achieve comparable PFS throughput with much simpler integration and operational processes.
Scalability
Enterprises need an HPC storage system that can seamlessly scale with their innovation work. Parallel file systems are known for being able to maintain strong data performance as they grow, but often at the expense of operational fragility.
HPC is a high-concurrency data processing environment where the demand for metadata operations is extremely high. With hundreds or thousands of concurrent clients accessing the system, the centralized metadata server of a PFS can be overwhelmed by simultaneous requests for metadata operations like file lookups or directory traversals. Therefore, even if the data servers have high bandwidth, the metadata server itself can become a chokepoint that limits the overall performance of the system.
On the other hand, an NFS-over-RDMA system running on VAST’s Disaggregated, Shared-Everything (DASE) architecture avoids the metadata pitfalls that often inhibit scale. By separating cluster CPUs from file storage, this modern architecture approach permits an NFS to scale linearly without metadata contention or other bottlenecks.
Management Overhead
Besides being high-performing and scalable, an HPC storage system must also be straightforward and cost-effective to manage and operate long-term. This is an area where PFS have historically fallen short — their above-mentioned complexity requires the oversight of multiple expert system administrators, as well as constant structural tuning and refinement. These requirements add significantly to the ongoing overhead costs of running a PFS.
Thanks to their widespread standardization, NFS are comparatively much simpler and cost-effective to operate. They offer drop-in client integrations and support with minimal operational lift, and require less day-to-day maintenance and oversight.
The Case for Multi-Protocol Storage in HPC
The growing diversity of HPC workloads — AI/ML, hybrid cloud, edge HPC, etc. — makes protocol simplicity essential. While single-protocol data storage is of course ideal, it’s not always realistic or feasible for well-established enterprises running multiple disparate systems. This is where multi-protocol storage comes in.
Multi-protocol data storage creates a single pool of storage for data that’s using multiple network protocols, such as NFS, SMB, and S3. By eliminating the need for complex data conversions or separate storage solutions, multi-protocol storage helps HPC teams access all data simultaneously and reduce the administrative burden of HPC projects.
VAST’s DASE architecture, with its all-flash backend and RDMA support, puts any multi-protocol data storage system on par with the performance of a leading PFS.
How VAST Delivers Parallel Performance Without the PFS Complexity
Multiple technological advances have converged to position VAST Data as the best data storage choice for HPC environments:
DASE Architecture: All servers share metadata and capacity, with no performance chokepoints.
NFS-over-RDMA: Multiple terabytes per second of data throughput, sub-millisecond latency, and no client agents.
Unified Global Namespace: One centralized access point for multi-site and hybrid cloud workflows.
Multi-Protocol Support: Seamlessly combine data from NFS, SMB, NVMe/TCP, or S3 protocols without data duplication or translation.
Simplified Access: Connect research teams, compute clusters, and data pipelines with consolidated system access.
NFS storage has emerged as the best-suited HPC storage alternative for achieving parallel file system performance without the complexity. However, protocol flexibility remains critical, and is now a must-have for any modern HPC storage system.
Schedule a personalized demo today and explore high-performance, multi-protocol HPC storage with VAST.
