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GPFS
GPFS
GPFS
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GPFS
Developer(s)IBM
Full nameIBM Spectrum Scale
Introduced1998; 27 years ago (1998) with AIX
Limits
Max volume size8 YB
Max file size8 EB
Max no. of files264 per file system
Features
File system
permissions		POSIX
Transparent
encryption		yes
Other
Supported
operating systems		AIX, Linux, Windows Server

GPFS (General Parallel File System, brand name IBM Storage Scale and previously IBM Spectrum Scale)[1] is a high-performance clustered file system software developed by IBM. It can be deployed in shared-disk or shared-nothing distributed parallel modes, or a combination of these. It is used by many of the world's largest commercial companies, as well as some of the supercomputers on the Top 500 List.[2] For example, it is the filesystem of the Summit [3] at Oak Ridge National Laboratory which was the #1 fastest supercomputer in the world in the November 2019 Top 500 List.[4] Summit is a 200 Petaflops system composed of more than 9,000 POWER9 processors and 27,000 NVIDIA Volta GPUs. The storage filesystem is called Alpine.[5]

Like typical cluster filesystems, GPFS provides concurrent high-speed file access to applications executing on multiple nodes of clusters. It can be used with AIX clusters, Linux clusters,[6] on Microsoft Windows Server, or a heterogeneous cluster of AIX, Linux and Windows nodes running on x86, Power or IBM Z processor architectures.

History

[edit]

GPFS began as the Tiger Shark file system, a research project at IBM's Almaden Research Center as early as 1993. Tiger Shark was initially designed to support high throughput multimedia applications. This design turned out to be well suited to scientific computing.[7]

Another ancestor is IBM's Vesta filesystem, developed as a research project at IBM's Thomas J. Watson Research Center between 1992 and 1995.[8] Vesta introduced the concept of file partitioning to accommodate the needs of parallel applications that run on high-performance multicomputers with parallel I/O subsystems. With partitioning, a file is not a sequence of bytes, but rather multiple disjoint sequences that may be accessed in parallel. The partitioning is such that it abstracts away the number and type of I/O nodes hosting the filesystem, and it allows a variety of logically partitioned views of files, regardless of the physical distribution of data within the I/O nodes. The disjoint sequences are arranged to correspond to individual processes of a parallel application, allowing for improved scalability.[9][10]

Vesta was commercialized as the PIOFS filesystem around 1994,[11] and was succeeded by GPFS around 1998.[12][13] The main difference between the older and newer filesystems was that GPFS replaced the specialized interface offered by Vesta/PIOFS with the standard Unix API: all the features to support high performance parallel I/O were hidden from users and implemented under the hood.[7][13] GPFS also shared many components with the related products IBM Multi-Media Server and IBM Video Charger, which is why many GPFS utilities start with the prefix mm—multi-media.[14]: xi 

In 2010, IBM previewed a version of GPFS that included a capability known as GPFS-SNC, where SNC stands for Shared Nothing Cluster. This was officially released with GPFS 3.5 in December 2012, and is now known as FPO [15] (File Placement Optimizer).

Architecture

[edit]

It is a clustered file system. It breaks a file into blocks of a configured size, less than 1 megabyte each, which are distributed across multiple cluster nodes.

The system stores data on standard block storage volumes, but includes an internal RAID layer that can virtualize those volumes for redundancy and parallel access much like a RAID block storage system. It also has the ability to replicate across volumes at the higher file level.

Features of the architecture include

  • Distributed metadata, including the directory tree. There is no single "directory controller" or "index server" in charge of the filesystem.
  • Efficient indexing of directory entries for very large directories.
  • Distributed locking. This allows for full POSIX filesystem semantics, including locking for exclusive file access.
  • Partition Aware. A failure of the network may partition the filesystem into two or more groups of nodes that can only see the nodes in their group. This can be detected through a heartbeat protocol, and when a partition occurs, the filesystem remains live for the largest partition formed. This offers a graceful degradation of the filesystem — some machines will remain working.
  • Filesystem maintenance can be performed online. Most of the filesystem maintenance chores (adding new disks, rebalancing data across disks) can be performed while the filesystem is live. This maximizes the filesystem availability, and thus the availability of the supercomputer cluster itself.

Other features include high availability, ability to be used in a heterogeneous cluster, disaster recovery, security, DMAPI, HSM and ILM.

Compared to Hadoop Distributed File System (HDFS)

[edit]

Hadoop's HDFS filesystem, is designed to store similar or greater quantities of data on commodity hardware — that is, datacenters without RAID disks and a storage area network (SAN).

  • HDFS also breaks files up into blocks, and stores them on different filesystem nodes.
  • GPFS has full Posix filesystem semantics[16].
  • GPFS distributes its directory indices and other metadata across the filesystem. Hadoop, in contrast, keeps this on the Primary and Secondary Namenodes, large servers which must store all index information in-RAM.
  • GPFS breaks files up into small blocks. Hadoop HDFS likes blocks of 64 MB or more, as this reduces the storage requirements of the Namenode. Small blocks or many small files fill up a filesystem's indices fast, so limit the filesystem's size.

Information lifecycle management

[edit]

Storage pools allow for the grouping of disks within a file system. An administrator can create tiers of storage by grouping disks based on performance, locality or reliability characteristics. For example, one pool could be high-performance Fibre Channel disks and another more economical SATA storage.

A fileset is a sub-tree of the file system namespace and provides a way to partition the namespace into smaller, more manageable units. Filesets provide an administrative boundary that can be used to set quotas and be specified in a policy to control initial data placement or data migration. Data in a single fileset can reside in one or more storage pools. Where the file data resides and how it is migrated is based on a set of rules in a user defined policy.

There are two types of user defined policies: file placement and file management. File placement policies direct file data as files are created to the appropriate storage pool. File placement rules are selected by attributes such as file name, the user name or the fileset. File management policies allow the file's data to be moved or replicated or files to be deleted. File management policies can be used to move data from one pool to another without changing the file's location in the directory structure. File management policies are determined by file attributes such as last access time, path name or size of the file.

The policy processing engine is scalable and can be run on many nodes at once. This allows management policies to be applied to a single file system with billions of files and complete in a few hours.[citation needed]

See also

[edit]

References

[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

GPFS

View on Grokipedia
from Grokipedia
General Parallel File System (GPFS), now branded as Storage Scale, is a high-performance developed by that enables multiple nodes to access a single or dataset concurrently, supporting scalable across distributed environments. It provides robust features such as data replication for reliability, policy-based storage management for efficient data placement, and multisite operations for wide-area , making it suitable for high-availability and scale-out scenarios. GPFS originated in the late as a solution for needs and has evolved over more than two decades into a mature platform with contributions from over 100 developers, supporting strict semantics across thousands of clients in complex cluster configurations. The system's is flexible, accommodating SAN-attached, network-attached, mixed, or shared-nothing cluster setups, and it integrates seamlessly with protocols like SMB and S3 for broader accessibility. It runs on multiple operating systems, including AIX, , and Windows, as well as virtualized environments using logical partitioning or hypervisors. In practice, GPFS is widely used for demanding workloads such as (HPC), analytics, and enterprise storage, where it facilitates active file management, metadata-intensive operations, and resilient data access across local or wide-area networks. Its design emphasizes performance optimization, with capabilities for handling application requests directly on the node where the application runs, minimizing latency in clustered environments. This positions Storage Scale as a foundational technology for modern data-intensive infrastructures, evolving from its GPFS roots to address contemporary needs in , AI, and hybrid storage ecosystems.

Overview

Definition and Core Functionality

GPFS, or General Parallel File System, is a distributed and scalable designed for high-throughput access across multiple nodes in a computing environment. Developed by , it aggregates storage resources from various servers to create a unified , enabling efficient management of large-scale in (HPC), analytics, and enterprise applications. Now marketed as IBM Storage Scale, GPFS provides a robust foundation for handling massive structured and unstructured datasets with sustained performance. At its core, GPFS facilitates simultaneous read and write operations to shared files from thousands of clients, supporting standards for compatibility with standard interfaces and APIs. This allows applications to perform parallel I/O, distributing access across cluster nodes to achieve high bandwidth and low latency for data-intensive workloads. By maintaining consistency and through clustering mechanisms, GPFS ensures reliable access even in dynamic environments with node failures or expansions. GPFS operates on the principle of clustering to pool storage from multiple servers into a single, coherent view, where nodes perceive files as locally accessible despite their distributed nature. Key architectural limits include a maximum volume size of 8 yottabytes (YB), a maximum of 8 exabytes (EB), and support for up to 2642^{64} files per , enabling exascale data handling without compromising performance. In practice, GPFS optimizes bandwidth-intensive applications such as scientific simulations, where cluster nodes concurrently access and update petabyte-scale datasets—appearing as local storage—to accelerate computations in fields like climate modeling or .

Evolution to IBM Spectrum Scale

GPFS, or General Parallel File System, was introduced by in 1998 as a high-performance initially developed for the AIX operating system, enabling concurrent access across multiple nodes in environments. In 2015, IBM rebranded GPFS as IBM Spectrum Scale to align it with the company's broader software-defined storage initiatives, emphasizing its evolution from a specialized to a versatile . This rebranding highlighted Spectrum Scale's capabilities in handling large-scale data across diverse workloads. In 2023, it underwent another to IBM Storage Scale, reflecting further integration into 's storage ecosystem and a focus on unified data access. As part of Storage Scale, the product now emphasizes multi-protocol support, allowing seamless access to the same data via NFS, SMB, and S3 protocols, extending its utility beyond traditional parallel roles to include and hybrid environments. As of 2025, Storage Scale version 6.0.0 supports hybrid cloud deployments, enabling scalable across on-premises, cloud, and edge infrastructures, with ongoing enhancements targeted at AI workloads and scenarios.

History

Initial Development

The General Parallel File System (GPFS) originated as a research project at IBM's Almaden Research Center in the early 1990s, initially under the name , aimed at creating a scalable parallel for handling large-scale data access in distributed environments. was designed to support interactive applications on IBM's AIX operating system, running across platforms from RS/6000 workstations to the SP2 parallel supercomputer, with an emphasis on continuous-time data handling, , and online management. This foundational work evolved into GPFS to address broader needs in (HPC), particularly the demand for efficient storage in supercomputing clusters. Key motivations for GPFS's development stemmed from the limitations of traditional file systems in supporting parallel I/O access for HPC workloads, where multiple nodes required simultaneous read and write operations to shared data without bottlenecks. Traditional systems often struggled with in cluster environments, leading researchers to prioritize distributed locking mechanisms and recovery techniques that could handle large-scale clusters effectively. The project drew inspiration from earlier efforts, such as the Vesta parallel file system, which provided experimental support for parallel access on multicomputers with parallel I/O subsystems, influencing GPFS's approach to striping data across disks for improved throughput. Additionally, GPFS incorporated concepts from to enable shared-nothing architectures, where nodes operate independently without , enhancing and in non-dedicated clusters. The initial development was led by a team of engineers at the Almaden Research Center, including key contributors like Frank Schmuck and Roger Haskin, who focused on extending distributed locking and token management to support clusters of hundreds of nodes. Their work built on Tiger Shark's prototype, shifting emphasis from multimedia-specific features to general-purpose parallel capabilities compatible with standard Unix APIs. This engineering effort addressed the growing needs of supercomputing environments in the mid-1990s, where scalable storage was essential for handling massive datasets in scientific simulations. GPFS was first released in as a POSIX-compliant integrated with IBM's AIX operating system, specifically for the RS/6000 SP parallel supercomputer. Early adoption centered on HPC applications in scientific and engineering domains, such as and large-scale simulations, where its ability to provide concurrent access to shared files across cluster nodes proved critical for performance. Deployments on the RS/6000 SP enabled users to manage petabyte-scale storage pools with high reliability, marking GPFS as a foundational for IBM's supercomputing ecosystem.

Key Milestones and Releases

In 2001, GPFS was ported to , extending its availability beyond AIX on Power servers to x86 and Power architectures, thereby broadening its adoption in clustered environments. From to 2013, enhancements included the introduction of policy-based storage management for automated data placement and tiering, and multi-site replication capabilities for disaster recovery in high-availability configurations. In 2014, version 4.1 introduced Active File Management (AFM), enabling scalable caching and remote data access across clusters. The following year, 2015, marked the rebranding to IBM Spectrum Scale as part of IBM's software-defined storage initiative, with added support for Hadoop integration via native connectors, object storage protocols like S3, and cloud bursting features for hybrid environments. Version 5.1, released in 2020 with key updates in 2021, enhanced support for AI workloads through integration with GPUDirect Storage, allowing direct GPU-to-storage data transfers to reduce latency. In 2023, the product was rebranded to Storage Scale. In 2024, version 5.2 further improved security with advanced encryption at rest and multi-tenancy features for isolated environments in shared clusters. In October 2025, version 6.0 was released, introducing features such as the Data Acceleration Tier for high IOPS and low-latency AI inference workloads, along with enhanced automation and Nvidia certifications. Significant adoption milestones include its deployment in the Summit supercomputer in 2018, where IBM Spectrum Scale powered a 250 PB file system delivering 2.5 PB/s bandwidth for exascale computing. By 2022, it supported petabyte-scale deployments in leading HPC systems, demonstrating its scalability for massive data-intensive applications.

Architecture

Core Components and Design Principles

IBM Storage Scale (formerly GPFS) employs a cluster-based consisting of multiple nodes that function as both clients and managers, interconnected through high-speed networks such as Ethernet, , or (RoCE). These nodes collectively form a single, unified that spans distributed storage resources, enabling parallel access without a central server bottleneck. The architecture supports to thousands of nodes, with recent enhancements in version 6.0.0 including the Data Acceleration Tier for optimizing AI workloads. Within the cluster, node roles are distributed to maintain coordination and reliability: the monitors disk leases to detect failures and elects the manager, which oversees configuration, quotas, and metadata operations; meanwhile, all nodes actively participate in token management, granting and revoking tokens to coordinate locking and ensure data consistency across the system. The core design principles center on a loosely coupled, shared-nothing model that promotes and , allowing nodes to operate independently while synchronizing through minimal interactions for . This approach incorporates disk leases for timely failure detection and recovery, alongside byte-range locking mechanisms that support fine-grained, concurrent file access while adhering to semantics. For network and storage integration, Storage Scale accommodates (DAS) via local disks, () through protocols like NFS, and NVMe over Fabrics (NVMe-oF) for ultra-low-latency I/O in disaggregated environments, leveraging Network Shared Disks (NSDs) to abstract and distribute access across up to eight servers per disk.

Data and Metadata Management

In IBM Storage Scale (formerly GPFS), data striping divides files into fixed-size blocks and distributes them across multiple Network Shared Disks (NSDs) within a storage pool to enable parallel I/O access and balance load across disks. This declustered layout spreads blocks evenly, minimizing hotspots and facilitating efficient reconstruction during failures by leveraging spare space distributed across the array. For redundancy, the system supports with a configurable replication factor of up to three copies per block, placed in distinct failure groups to tolerate site or disk failures, alongside automatic managed through servers operating in active-active mode. Erasure coding, available via the Erasure Code Edition, provides an alternative by dividing into strips (e.g., 8 + 3 parity) using Reed-Solomon codes, achieving 2- or 3-fault tolerance with higher storage —up to 73% usable capacity compared to 33% for triple —while integrating seamlessly with s for reconstruction. Metadata management employs a distributed approach, with metadata striped across disks and managed by a designated metanode that handles updates for each open file to ensure and avoid bottlenecks. The file system descriptor stores configuration details such as block size and replication settings, while inode tables maintain and are replicated for reliability. is enhanced through this striping of metadata across disks, journaling to a recovery log for crash recovery of metadata and small-file , and sub-block allocation via segmented maps to optimize space for files smaller than the block size without excessive coordination overhead. Quota and space management is enforced at the , user, group, or fileset levels by the file system manager, which tracks allocations and limits disk space or inode counts to prevent overuse, with configurable to span the entire system or confine to fileset boundaries. Online , performed via the mmdefragfs command, maintains performance by relocating fragmented data to consolidate free blocks and sub-blocks while the file system remains mounted, iterating until a target utilization threshold is reached or no improvements are possible.

Key Features

Scalability and Performance Optimizations

IBM Storage Scale achieves high scalability through its clustered architecture, supporting up to 10,000 nodes in a single cluster to accommodate large-scale deployments in and analytics environments. The file system scales to capacities of 8 exabytes while maintaining a capable of handling up to 9 quintillion files, enabling efficient management of massive datasets without performance degradation. Multi-site , facilitated by Active File Management (AFM), extends this scalability across geographically distributed locations, creating a unified global that allows seamless data access and synchronization over wide-area networks. Key performance optimizations focus on minimizing latency and maximizing throughput for demanding workloads. I/O shipping enables direct data transfer between network-shared disk (NSD) clients and servers using RDMA, bypassing unnecessary copies and reducing remote access overhead in distributed environments. Prefetching algorithms automatically detect common access patterns, such as sequential reads, and preload data into buffers to accelerate I/O operations. Caching hierarchies further enhance efficiency, including the client-side pagepool for buffering file data and metadata, as well as protocol-based caches in AFM that retain frequently accessed files locally to mask network latencies. Additional optimizations include Storage Scale Native RAID, a declustered RAID implementation that distributes parity across all disks in a virtual disk group, enabling cost-effective scaling with higher and faster rebuild times compared to traditional configurations. File system-level compression, applied transparently via policies, reduces data volume on disk to boost effective throughput, particularly for compressible workloads like logs or analytics data. In the Edition, integrated data reduction techniques further optimize storage efficiency while preserving performance. Tuning parameters allow customization for specific I/O patterns; for instance, adjusting the file system block size—effectively the stripe width—optimizes , with larger values (such as 1 MB) favoring sequential workloads by aligning with large transfers, while smaller sizes (like 256 KB) suit scenarios. This builds on core data striping mechanisms that distribute blocks across multiple disks for parallel access. Integration with RDMA over or RoCE networks in HPC setups delivers sub-millisecond latencies for inter-node communications, supporting extreme bandwidth requirements in simulations and AI training.

Information Lifecycle Management

IBM Storage Scale's Information Lifecycle Management (ILM) provides a policy-driven framework to the placement, migration, and management of files across heterogeneous storage tiers, ensuring data is stored on the most appropriate media based on predefined criteria such as file age, access frequency, and usage patterns. The core policy engine uses an SQL-like rule language to evaluate files during periodic scans, enabling actions like migration from high-performance disk to lower-cost options without manual intervention. This integrates seamlessly with external storage systems, including tape libraries and object stores, to handle the full data lifecycle from active use to long-term archiving. Tiering mechanisms in ILM leverage (HSM) to identify and relocate "cold" data—files that have not been accessed recently—to cost-effective tiers, such as TS4500 tape libraries via Spectrum Archive or cloud services like AWS S3. Pre-migration policies copy data to these external pools before freeing space on primary storage, while full migration replaces files with stubs for efficient recall when needed. Policies can exclude critical directories, such as snapshots or metadata areas, and incorporate thresholds like THRESHOLD(80,70) to trigger actions based on storage utilization or access age. Detailed policy rules support a range of operations, including replication to additional tiers for , automatic deletion of obsolete files, and enforcement during movement, all executed through the mmapplypolicy command in phases of scanning, evaluation, and action. For custom workflows, ILM exposes APIs and interface scripts that allow integration with external ILM systems, enabling string substitutions for pool-specific parameters like tape library assignments. These capabilities prioritize weight-based rules, such as favoring older or less-accessed files, to optimize resource use across the cluster. In large-scale deployments, ILM delivers significant cost savings by shifting inactive data to tape or . For instance, in analytics environments, policies can archive petabytes of historical data by migrating files older than 30 days to tape, facilitating quick recalls for ad-hoc queries while minimizing ongoing operational costs.

Integrations and Comparisons

Support for Protocols and Ecosystems

IBM Storage Scale, formerly known as GPFS, provides native support for POSIX standards, enabling direct file system access for applications requiring standard Unix-like interfaces. It extends this capability through multi-protocol sharing, including NFSv4 for network file access and SMB3 for Windows-compatible sharing, allowing concurrent read/write operations across diverse client environments without data duplication. Additionally, an S3-compatible object interface facilitates integration with cloud-native applications, supporting high-performance object storage operations on data managed within the file system. For broader ecosystem compatibility, Storage Scale integrates with frameworks like Hadoop and Spark through a dedicated Hadoop connector that emulates HDFS APIs, enabling in-place analytics on file and object data without movement. This connector allows Hadoop workloads to treat the parallel as a transparent HDFS layer, supporting Spark's distributed processing for tasks such as and data querying. In containerized environments, the Spectrum Scale Container Storage Interface (CSI) driver provisions persistent volumes for clusters, managing dynamic storage allocation and lifecycle for stateful applications across and vanilla deployments. Multi-site operations leverage federation protocols for wide-area network (WAN) replication, including stretched clusters that span data centers for synchronous data mirroring over low-latency links, ensuring high availability and disaster recovery. Asynchronous replication extends this to multi-site setups via Active File Management (AFM), caching and syncing data across remote clusters. Hybrid cloud extensions support bursting to platforms like IBM Cloud and Microsoft Azure, allowing seamless workload scaling by attaching cloud resources to on-premises clusters for elastic capacity during peak demands. Security integrations include support for LDAP and (AD) for centralized user authentication, mapping identities across protocols to enforce access controls. Kerberos is utilized for secure authentication and encryption in transit, particularly with NFS and SMB protocols, while TLS secures LDAP communications and S3 object access, providing end-to-end protection in multi-protocol environments.

Comparison with Hadoop Distributed File System

GPFS, now known as IBM Storage Scale, and the Hadoop Distributed File System (HDFS) represent distinct approaches to distributed storage, with GPFS emphasizing a , shared-disk that provides a unified and full compliance, enabling seamless integration with traditional applications without modification. In contrast, HDFS employs a block-based, scale-out model centered on a NameNode for metadata management, which introduces a potential bottleneck and limits it to non- semantics, requiring applications to use Hadoop-specific APIs. Furthermore, GPFS supports concurrent multi-writer access to files, allowing multiple clients to modify the same file simultaneously, whereas HDFS enforces an append-only policy with a single writer per file to simplify consistency in distributed environments. In terms of performance, GPFS is optimized for low-latency, parallel I/O operations critical to (HPC) workloads, achieving aggregate throughputs exceeding 100 GB/s in configured clusters with high-speed networking. HDFS, however, prioritizes high-throughput for , tolerating higher latency due to its focus on sequential reads and writes in MapReduce-style jobs, often resulting in comparable but less versatile performance on equivalent hardware. Both systems manage petabyte-scale datasets, but GPFS demonstrates superior scalability to thousands of nodes without a centralized metadata server, mitigating single-point-of-failure risks inherent in HDFS's NameNode —even with high-availability configurations. HDFS can scale to large clusters via but remains constrained by NameNode metadata handling, limiting it to around 350 million files per instance. Use cases for GPFS center on real-time simulations, AI model training, and HPC environments requiring low-latency access and multi-protocol support, while HDFS is tailored for batch-oriented pipelines, such as processing in analytics workflows.

Deployment and Applications

Implementation Requirements

Spectrum Scale requires 64-bit processors, supporting x86_64, POWER (ppc64le), (s390x), and technical preview for ARM64 architectures, with a minimum of multi-core CPUs such as or for x86 and or later for POWER systems. Minimum memory is 4 GB per node for basic operations, though 128 GB or more is recommended for production workloads to handle caching and metadata operations effectively. For networking, a high-speed interconnect like 10 GbE or faster Ethernet, , or (RoCE) is essential for inter-node communication, with SSDs strongly recommended for metadata servers to optimize performance. The software supports Linux distributions including Red Hat Enterprise Linux (RHEL) 8.10 and 9.4-9.6, SUSE Linux Enterprise Server (SLES) 15 SP5-SP7, and Ubuntu 20.04.5-20.04.6, 22.04.4-22.04.5 on x86_64, POWER, and Z platforms; AIX 7.2 TL4-TL5 and 7.3 TL0-TL3 on POWER; and Windows Server 2019 (build 1809 or later) and 2022 (build 20348 or later) for client nodes only (as of November 2025). Installation requires kernel development packages (e.g., kernel-devel on Linux), GNU Compiler Collection (GCC), and other dependencies like Python 3.8+ and Ansible 2.9+ for the installation toolkit, along with IBM kernel modules that must be built for the specific OS kernel version. Licensing is managed through IBM's entitlement system, with node-based server or client designations applied via the mmchlicense command, and clusters up to thousands of nodes supported under appropriate entitlements. Deployment begins with installing the Spectrum Scale packages using platform-specific methods: RPM or on , installp on AIX, or MSI installers on Windows, followed by building the portability layer on with mmbuildgpl if needed. Cluster creation uses the mmcrcluster command, specifying node lists (e.g., mmcrcluster -N node1,node2:[quorum](/page/Quorum) -p ssh -r /usr/bin/scp), which establishes the cluster configuration file and elects a manager node. Network Shared Disks (s) are then defined with mmcrnsd -F nsd_[stanza](/page/Stanza)_file, where the stanza file details device paths, failure groups, and usage (e.g., dataAndMetadata), supporting up to 8 servers per . Finally, the is created and mounted via mmcrfs gpfs0 -F nsd_[stanza](/page/Stanza)_file -A yes to enable automatic mounting on daemon startup, with options like -k nfs4 for protocol compatibility. Licensing follows a capacity-based model measured in TiB or PiB of usable storage, available as perpetual licenses (one-time purchase with optional Software Subscription and Support) or term-based subscriptions scalable by capacity and protocols (e.g., additional for SMB or object access). Costs vary by edition (Data Access, Data Management, Erasure Code) and node count, with no charge for unlimited clients in capacity-licensed clusters, but server nodes require entitlements based on sockets or capacity thresholds.

Use Cases in High-Performance Computing

Spectrum Scale, formerly known as GPFS, serves as the parallel for (HPC) environments on supercomputers such as at , where it manages 250 PB of storage capacity with up to 2.5 TB/s bandwidth to support shared access across thousands of nodes. This configuration enables efficient handling of large-scale simulations, including those in climate modeling, by providing concurrent read/write access to petabyte-scale datasets for parallel processing on GPU-accelerated systems. In , Spectrum Scale facilitates the storage and analysis of massive sequencing outputs, such as those from next-generation sequencers, through its unified and policy-driven placement, allowing researchers to ingest, process, and query terabytes of genomic across clustered nodes without performance bottlenecks. For and AI workloads, Spectrum Scale integrates with GPUDirect Storage to enable direct data transfers between storage and GPU memory, bypassing CPU involvement and accelerating pipelines by reducing latency for training on large datasets. This capability supports real-time analytics in , where it handles terabyte-scale datasets for and high-frequency trading simulations by delivering high-throughput I/O to distributed compute clusters. In enterprise media production, such as rendering workflows, Spectrum Scale provides scalable shared storage for collaborative access to high-resolution assets, enabling parallel rendering on render farms while maintaining across global teams. In healthcare applications, Spectrum Scale manages imaging archives through its Information Lifecycle Management (ILM) features, automatically tiering petabyte volumes of medical images from high-performance tiers to cost-effective archival storage based on access patterns and retention policies. A notable case study is its deployment at Lawrence Livermore National Laboratory, where Spectrum Scale supports a 154 PB file system on the Sierra supercomputer, achieving aggregate I/O rates of 1.54 TB/s to sustain exascale simulations in scientific computing.

References

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  31. https://www.ibm.com/docs/en/storage-scale-ece/5.2.1?topic=features-declustered-raid
  32. https://parallelstorage.com/2018/06/09/failure-groups-in-spectrum-scale/
  33. https://www.redbooks.ibm.com/redpapers/pdfs/redp5557.pdf
  34. https://www.ibm.com/docs/en/storage-scale/5.2.2?topic=administering-managing-gpfs-quotas
  35. https://www.ibm.com/docs/en/storage-scale/5.2.1?topic=reference-mmdefragfs-command
  36. https://www.spectrumscaleug.org/wp-content/uploads/2025/03/SSD25-E05-Scale-Concepts-Part2-Christof_Schirra.pdf
  37. https://www.advancedhpc.com/pages/ibm-spectrum-scale
  38. https://www.ibm.com/docs/en/storage-scale/5.2.2?topic=storage-multi-site-replication
  39. https://www.ibm.com/docs/en/storage-scale/5.2.2?topic=scale-improved-system-performance
  40. https://www.ibm.com/docs/en/STXKQY/pdf/gpfsclustersfaq.pdf
  41. https://www.redbooks.ibm.com/redpapers/pdfs/redp5484.pdf
  42. https://www.redbooks.ibm.com/redpieces/pdfs/redp5739.pdf
  43. https://www.ibm.com/support/pages/ibm%C2%AE-storage-scale%E2%84%A2-information-lifecycle-management-policies
  44. https://www.ibm.com/docs/en/storage-scale/5.2.1?topic=gpfs-nfs-support-overview
  45. https://www.ibm.com/docs/en/storage-scale/5.2.2?topic=gpfs-s3-support-overview
  46. https://www.ibm.com/docs/en/storage-scale-bda?topic=storage-scale-support-hadoop
  47. https://www.ibm.com/docs/en/scalecsi?topic=200-introduction-spectrum-scale-container-storage-interface-driver
  48. https://www.spectrumscaleug.org/wp-content/uploads/2020/07/SSUGDigital-2-Spectrum-Scale-stretched-cluster-best-practices.pdf
  49. https://community.ibm.com/community/user/storage/blogs/david-wohlford1/2021/01/06/IBM-Storage-hybrid-cloud-Azure
  50. https://www.ibm.com/docs/en/storage-scale/5.2.2?topic=access-configuring-ldap-tls-kerberos-file
  51. https://www.ibm.com/docs/en/storage-scale/5.2.1?topic=data-configuring-protocol-security
  52. https://www.redbooks.ibm.com/redpapers/pdfs/redp5397.pdf
  53. https://www.spectrumscaleug.org/wp-content/uploads/2024/01/SSUG23SC-Performance-Update.pdf
  54. https://hadoop.apache.org/docs/r3.2.3/hadoop-project-dist/hadoop-hdfs/HDFSHighAvailabilityWithNFS.html
  55. https://www.ibm.com/support/pages/ibm-storage-scale-technical-preview-support-arm-based-processors
  56. https://www.ibm.com/docs/en/spectrum-scale?topic=STXKQY/gpfsclustersfaq.html
  57. https://www.ibm.com/docs/STXKQY/IBMScale_ESS_Licensing.pdf
  58. https://extremecomputingtraining.anl.gov/wp-content/uploads/sites/96/2023/08/ATPESC-2023-Track-1-Talk-2-Papatheodore-Frontier-Overview.pdf
  59. https://www.spectrumscaleug.org/wp-content/uploads/2019/07/IBM-Spectrum-Scale-Use-Cases.pdf
  60. https://www.redbooks.ibm.com/redpapers/pdfs/redp5479.pdf
  61. https://www.ibm.com/docs/en/storage-scale/5.2.1?topic=installing-gpudirect-storage-storage-scale
  62. https://www.rubrik.com/content/dam/rubrik/en/resources/solutions-brief/brf-rubrik-nas-cloud-direct-for-ibm-spectrum-scale-gpfs.pdf
  63. https://www.ibm.com/case-studies/pixitmedia-spectrum-scale
  64. https://www.ibm.com/support/pages/efficiently-manage-life-time-your-medical-images-ibm-spectrum-scale-ibm-transparent-cloud-tiering-and-ibm-cloud-object-storage
  65. https://www.nextplatform.com/2021/03/09/livermore-converges-a-slew-of-new-ideas-for-exascale-storage/
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