Introduction
For years, developers have sought ways to optimize file input and output (I/O) operations to improve application performance. As data volumes grow ever larger, the need for fast and efficient file I/O has only increased. High performance computing (HPC) applications in fields like genomics, physics, and machine learning rely on the ability to read and write massive datasets quickly. New specialized file systems aim to accelerate I/O while maintaining compatibility with existing applications. In this article, we’ll explore emerging file systems designed for speed, and how they can benefit data-intensive applications.
File System Basics
Before diving into new high-performance file systems, let’s review some file system basics. At its core, a file system manages the storage and retrieval of data on a persistent storage device like a hard disk or SSD. Key responsibilities include:
- Organizing data into files and directories
- Tracking free and used storage space
- Mapping file contents to physical storage locations
- Coordinating read and write operations
To applications, the file system provides an abstract interface to access data without worrying about physical details. Under the hood, the file system handles complex challenges like fragmentation, caching, concurrency control, and recovery after failures.
Limitations of Conventional File Systems
The POSIX interface used by most file systems provides a flexible and portable standard that works across many operating systems and hardware platforms. However, general purpose file systems like EXT4, XFS, and NTFS also have some inherent limitations when it comes to performance:
- Overhead from journaling and metadata management
- Fragmentation that degrades locality and caching
- Concurrency limits from coarse-grained locking
- Assumptions about storage devices that no longer match modern SSDs
These factors can result in high latency and low bandwidth utilization for HPC workloads that read and write massive files with frequent metadata operations.
Emerging Approaches for Faster I/O
To overcome limitations of conventional file systems, new specialized designs take innovative approaches to storage layout, metadata management, consistency guarantees, and software-hardware co-design. Let’s examine some leading next-generation file system projects.
DAOS
The Distributed Asynchronous Object Storage (DAOS) system developed by Intel aims to optimize I/O for massively distributed Non-Volatile Memory Express (NVMe) storage. Key features include:
- Object-based storage to improve scalability and reduce metadata load
- Asynchronous I/O with server-side request reordering and buffering
- Intent-based consistency that aligns with HPC workload needs
- Tiering across storage media including DRAM, NVMe SSDs, and HDDs
By shifting metadata workload off clients and tailoring consistency semantics, DAOS can deliver major gains for bandwidth and IOPS compared to legacy parallel file systems.
BeeGFS
BeeGFS from ThinkParQ leverages parallel network file system techniques to accelerate I/O access to centralized storage from compute clusters. Optimizations include:
- Server-side caching using Storage Class Memory to absorb and batch metadata updates
- Log-structured metadata for faster updates and crash consistency
- RDMA support for low-latency data transfer over lossless fabrics
- Tiering across interfaces like Ethernet, Infiniband, OmniPath
Designed for simplicity and flexibility, BeeGFS enables legacy applications to benefit from fast parallel storage backends.
Stratis
Backed by Red Hat, Stratis takes a software-defined approach to pool local storage devices into fast and flexible filesystems tailored to containerized environments. Key features include:
- Thin provisioning with allocation on demand from storage pools
- Copy-on-write snapshots to easily clone volumes and datasets
- SSD caching to accelerate hot data access
- Multi-tenancy through logical isolation of stratified volumes
With Stratis, organizations can deploy Docker and Kubernetes without giving up storage efficiency and performance as workloads scale.
When to Adopt Next-Gen File Systems
For many general purpose workloads, conventional file systems get the job done. But data-driven applications in HPC, analytics, and machine learning stand to benefit greatly from purpose-built file systems that overcome legacy limitations.
Typical use cases include:
- High-throughput streaming access to huge datasets
- Frequent creation and deletion of many small files
- Simultaneous access from massively parallel programs
- Complex storage topology spanning tiers and interfaces
Before adopting a new high-performance file system, carefully evaluate your workload’s I/O profile and bottlenecks to ensure the system’s strengths align with your needs.
Outlook
Rapid innovation in applications is driving equally rapid advances in specialized file systems to keep pace with data growth and performance demands. Beyond raw throughput, new approaches bring improved scalability, flexibility, and manageability. With open source systems like DAOS, BeeGFS, and Stratis maturing quickly, fast parallel file I/O is becoming accessible to more organizations running data-driven workloads. Carefully deployed, these emerging file systems promise to accelerate applications and enrich data insights.
