In a fast-paced environment, you need a file system that allows for concurrent reads from multiple nodes. The IBM General Parallel File System (GPFS) was developed in 1998, but it’s one option for businesses leveraging artificial intelligence (AI) and machine learning (ML) in their applications. These applications need high-volume and high-performance storage accessible from multiple nodes for faster processing.
What Is GPFS?
Enterprise-level applications work with multiple disks with potentially petabytes of stored data. The IBM GPFS file system allows for fast delivery of data to avoid bottlenecks from slower disk storage technology. New GPFS technology distributes its metadata across multiple disk storage nodes, and data is also spread across multiple disks. Distributing data across multiple disks allows applications to retrieve data from multiple disks at the same time (i.e., in parallel) so that more data can be retrieved at the same time. This technology overcomes common bottlenecks when applications are forced to wait for all data to be retrieved from a single disk.
Features of GPFS
Parallel input and output in GPFS is what makes the file system one of the better options for AI and ML applications, but the technology has several others:
- Works well with billions of files stored on a storage area network (SAN)
- Convenient management and integration of your SAN devices and GPFS
- High-speed reads and writes to support applications with high-volume concurrent users
- Reads and writes exabytes of data with low latency
Use Cases for GPFS
High-performance computing (HPC) requires the best in technology, but businesses often forget that bottlenecks happen at the storage level. You can have the fastest CPUs, servers, memory, and network transfer speeds available that feed into storage hardware to read or write data. But if your storage technology is slow, you introduce a bottleneck and slow down applications.
A few use cases for GPFS:
- Performance engineering for data centers
- Applications requiring high volumes of data processing
- Machine learning and artificial intelligence ingestion and processing
- Multi-application storage and processing
- High-volume storage of several petabytes
GPFS Architecture
GPFS uses distributed architecture, which means that data spans multiple storage devices. Multiple servers or SAN locations hold your data, and multiple network connections link these storage devices. When an application needs to read data, it can use multiple network locations to read data in parallel, meaning that data is read at the same time from all storage locations.
A few key components in GPFS architecture:
- Data is stored across multiple storage locations, but metadata describing the data is also stored across multiple servers.
- Servers storing data could be in multiple cloud or on-premises locations.
- Fast network connections interlink storage locations and applications using GPFS storage.
- Advanced technologies for storage devices are essential.
GPFS vs. Traditional File Systems
GPFS is often compared to the Hadoop Distributed File System (HDFS). Both are meant to store large amounts of data, but they have some differences that affect performance and scalability. While both file systems break up data and store them on nodes across the network, GPFS has Posix semantics to allow for compatibility with various Linux distributions and operating systems including Windows.
Large primary and secondary metadata servers are necessary for Hadoop indexing, but GPFS distributes metadata across the system without the need for specialized servers. Distributed data is also in smaller blocks than Hadoop, so reads occur faster especially since data is read in parallel. GPFS requires more data storage capacity than Hadoop, but it’s much faster during read cycles.
GPFS Best Practices
To keep file reads and writes at optimal speeds, first ensure that you have the network infrastructure for performance. A GPFS storage system will read in parallel, so having performance-first networking equipment ensures that it will not be a bottleneck for data transfers. Infrastructure from Pure Storage, including Pure Cloud Block Store™, Portworx®, and FlashArray™, preserves application performance for large-volume disk reads.
File sharing should be used with directory-level mount points so that applications do not access the entire file system, including operating system files. Mounting based on directories rather than entire disks better secures data and the integrity of the server hosting disks. Administrators should also separate sensitive files unrelated to application read procedures to lower risks of unauthorized access.
Conclusion
If you need fast storage for high-performance compute power in AI and machine learning applications, Pure Storage has the infrastructure to help with the scalability necessary for business growth and user satisfaction. Administrators can deploy disks for HPC without expensive provisioning and installation. Our HPC infrastructure is built to bring integrity, performance, scalability, and next-generation processing to your high-speed application.
