An organization's chosen data storage architecture can have a huge impact on the scale, complexity, and success of high-performance computer (HPC) initiatives. Parallel file systems and distributed file systems are the two most commonly used storage architectures, but they’re often misunderstood or thought of as interchangeable technologies.
This post breaks down the primary differences between distributed and parallel file systems, evaluates the tradeoffs of each, and explores an emerging alternative that combines the advantages of the two approaches for a best-of-both-worlds HPC storage solution.
What Is a Distributed File System?
A distributed file system (DFS) is a type of data storage architecture that partitions data files into smaller blocks and distributes them across multiple networked servers. A DFS then employs a client-server architecture model whereby client end-points can request and access data from this distributed network of servers. Examples of DFS systems include Hadoop Distributed File System (HDFS), Ceph, and GlusterFS.
Key Characteristics
Distributed file storage systems rely heavily on data replication, node independence, and load balancing. For greater data availability and resiliency, multiple copies of data blocks are created and stored across different servers. This replication of data helps to establish node independence, with each server able to function independently, and no single server holding full control over the file system. In turn, this model of decentralized control helps to balance data requests across the network and avoid overloading any individual server.
Common Limitations
While the distributed nature of DFS systems helps to ensure high data availability and good fault tolerance in the event of hardware failures or network outages, limitations emerge when attempting to use a DFS for high-performance computing:
High Latency: Frequent data transfers, consistency checks, and synchronization protocols running over the network all introduce latency and impact speed. Additionally, DFS performance relies heavily on a stable and high-bandwidth network, meaning that sudden network issues or congestion can create bottlenecks that hinder performance.
Limited Scalability: Some DFS solutions have limitations in terms of the number of nodes or the file sizes they can effectively handle, which impacts their scalability. DFS systems scale horizontally by adding more physical servers to the network, which makes them a very hardware-heavy solution.
Bloated Costs: Given their extensive hardware and network requirements, DFS solutions can be more expensive to implement and maintain than other traditional file storage systems.
What is a Parallel File System?
A parallel file system (PFS) is a type of data storage architecture designed to handle very large datasets. Like DFS systems, PFS systems also divide data files into smaller blocks or “stripes” and store them across multiple drives. However, when it comes to retrieving the data, a PFS allows data stripes to be accessed concurrently (in parallel) from multiple storage nodes, rather than needing to compile the blocks one at a time from a single server (as is the case with a DFS). Examples of PFS systems include Lustre, GPFS, and BeeGFS.
Key Characteristics
Parallel file systems are defined by their file striping, concurrent access, and high throughput. When a file is written to a parallel file system, instead of storing the entire file on a single drive, it’s divided into smaller stripes and stored across multiple drives. Then, when a client needs to access a file, all of its component stripes are retrieved in parallel, speeding up the retrieval process. This accelerated file access produces the higher throughput required by high-performance computing environments.
Common Limitations
PFS systems are known for their impressive speed, strong performance, and ability to scale to process massive volumes of data. However, parallel file systems have a few inherent weaknesses when it comes to handling AI and HPC data:
High Complexity: PFS systems are notoriously challenging to set up, configure, and manage, largely due to their specialized components, bespoke configurations, and poor handling of smaller files. Tasks like upgrades and troubleshooting can be more complicated compared to traditional file systems.
Moderate Resiliency: Managing data consistency across multiple storage nodes, and supporting concurrent access to those nodes, adds an extra degree of complexity that parallel file systems sometimes struggle with. A single point of failure can impact the stability of the entire system and cause an outage.
Resource Heavy: PFS systems require multiple experienced system administrators to deploy, manage, maintain, and continually upgrade the system, making them costly to operate. Additionally, users often require in-depth training in order to utilize the provided resources properly and maximize performance.
Parallel vs. Distributed File Systems
The differences between parallel and distributed file systems are important to consider when evaluating high-performance computing storage options, and can be summarized as follows:
Data Access: DFS deliver requested data from one storage node at a time, whereas PFS deliver data from multiple nodes simultaneously, increasing speed.
Metadata Handling: DFS rely on centralized metadata servers within the storage network, while PFS typically separate out metadata storage onto dedicated drives, adding complexity.
Performance Scaling: DFS are scaled by adding more server hardware, whereas PFS can scale without such a heavy reliance on hardware due to their concurrent access.
Management Overhead: DFS are simpler to launch and run, while PFS involve extensive tuning complexity and administration costs.
Distributed file systems can be a good fit for cloud storage, enterprise file sharing, and other use cases involving smaller file sizes. Parallel file systems are well-suited for situations with larger file sizes and greater performance demands, such as scientific research, visual effects processing, and financial analysis. However, given the tradeoffs of each system, neither is perfect for today’s high-performance computing needs, causing HPC teams to look for an alternative model.
A New Approach: Parallel File System Performance Without the Complexity
Considering that distributed file systems fall short on performance, and parallel file systems suffer from excessive complexity, AI and HPC workloads require a more adaptable data infrastructure. This new approach is called Disaggregated Shared-Everything architecture — or DASE, for short.
DASE is a modern data storage approach developed by VAST that combines the performance and scalability of PFS with the simplicity and cost-effectiveness of DFS. DASE completely separates the data being stored from the CPUs that provide the storage services. This disaggregated storage, including all system metadata, is then shared by all servers in the cluster.
This separation allows data storage capacity and computational power to be scaled independently, providing significant cost advantages given the high demands of AI workloads. In addition, DASE does away with complex storage tiering, simplifying the entire data architecture for better performance and easier maintenance.
Other benefits of the DASE architecture include:
No Client Agents: DASE achieves agentless operation for clients by focusing on leveraging standard protocols such as NFS-over-RDMA, eliminating the need for specialized client-side drivers or agents that are typically required with PFS.
Low Latency: Its disaggregated, stateless architecture and all-flash storage design equip DASE with optimal data speed and availability that outperforms leading parallel file systems.
Extreme Scalability: With simplified, independent scaling of storage capacity and compute power plus its shared-everything architecture, DASE allows enterprise AI and HPC teams to quickly scale their initiatives, with exabyte capacity.
Simplified Management: DASE makes data management straightforward and seamless with its multi-protocol platform and unified global namespace, reducing operational complexity and administrative effort.
Conclusion
High-performance computing storage has unique needs requiring unique solutions. Distributed file systems such as HDFS, Ceph, and GlusterFS struggle to keep up with the performance and scale demands of most HPC initiatives. Parallel file systems such as Lustre, GPFS, and BeeGFS, on the other hand, offer the right level of performance but come with substantial configuration and management challenges. The solution for today’s data-driven organizations is Disaggregated, Shared-Everything architecture.
DASE is the backbone of the VAST AI Operating System, which empowers enterprises to unlock the full potential of their data for AI-driven innovation by providing a unified, high-performance, scalable, and cost-effective data infrastructure. Organizations such as NASA, Pixar, ServiceNow, and the U.S. Department of Defense rely on the VAST Data for their HPC storage needs.
Discover how modern HPC storage — without the parallel file system complexity — can propel your organization and its AI innovation forward.
