WEKA: A High-Performance Storage Solution for AI Workloads
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Continuum Knowledge
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WEKA is a software-defined storage solution that provides a distributed file system called WekaFS.
It is designed to meet the demanding performance and scalability requirements of modern workloads, particularly in the fields of artificial intelligence (AI), machine learning (ML), high-performance computing (HPC), and financial trading.
WekaFS runs on standard x86 servers and NVMe SSDs, eliminating the need for specialised hardware. This allows organisations to leverage the latest advancements in hardware technology without being tied to proprietary storage solutions.
High Performance: WEKA leverages NVMe flash storage and a distributed architecture to provide high throughput and low latency, enabling it to handle AI and HPC workloads.
Scalability: WEKA is highly scalable, both in terms of capacity and performance. It can scale to petabytes of storage and billions of files, while maintaining consistent performance.
Unified Namespace: WEKA provides a single, unified namespace that spans both high-performance flash storage and cost-effective object storage, with transparent tiering between the two.
Protocol Support: WekaFS supports a wide range of protocols, including POSIX, NFS, SMB, S3, and GPUDirect Storage, making it compatible with a variety of applications and workflows.
Data Protection: WEKA ensures data protection through distributed erasure coding, providing resilience against multiple concurrent drive or node failures without sacrificing performance.
Storage Efficiency: Features like thin provisioning, inline compression, and deduplication help optimize storage utilization and reduce costs.
Ease of Management: WEKA provides a user-friendly GUI for managing the storage system, including features like snapshots, clones, and quality of service (QoS) controls.
Weka is designed to optimise GPU utilisation by addressing the key challenges that can lead to GPU underutilisation, such as data stalls and pipeline starvation.
Weka's architecture and features enable it to constantly feed data to GPUs, ensuring they are fully utilised.
Here's how Weka achieves this:
High-performance, low-latency storage
Weka is built on a distributed, parallel file system architecture that leverages NVMe flash storage.
By using high-speed NVMe drives and a software-defined approach, Weka provides extremely high throughput and low latency storage performance.
This high-performance storage ensures that data can be quickly delivered to GPUs, minimizing wait times and keeping them busy.
Efficient metadata management
AI and ML workloads often involve a large number of small files, which can create metadata bottlenecks.
Weka efficiently handles this by creating virtual metadata servers that scale dynamically with each server added to the cluster.
This distributed metadata architecture allows Weka to manage billions of files and trillions of metadata operations, eliminating metadata as a bottleneck.
Distributed data and metadata
Weka distributes and parallelises both data and metadata across the entire cluster in small, 4K chunks.
This distribution ensures low latency and high performance regardless of the I/O size (small, large, or mixed).
By spreading data and metadata across all nodes, Weka maximizes the aggregate bandwidth and IOPS of the system.
Tiered storage and data locality
Weka automatically tiers data between the high-performance NVMe flash tier and a cheaper object storage tier.
The tiering mechanism keeps hot data on the flash tier closest to the GPUs, while cold data is moved to the object tier.
This ensures that the most frequently accessed data is always available with the lowest latency, optimizing GPU utilization.
Direct data access and GPU-aware storage
Weka supports direct access to data from GPUs, bypassing the CPU and reducing latency.
Integration with technologies like GPUDirect Storage allows GPUs to directly read and write data to Weka storage, further reducing data movement overhead.
This direct data path between storage and GPU memory minimizes data stalls and keeps GPUs fed with data.
Scalability and performance scaling
Weka's distributed architecture allows it to scale linearly by adding more nodes to the cluster.
As the cluster grows, both capacity and performance increase, ensuring that storage can keep pace with the demands of additional GPUs.
This scalability ensures that storage performance can be easily increased to match the needs of growing GPU clusters.
Optimised data pipelines
Weka's architecture is designed to minimise data movement and copies throughout the AI/ML pipeline.
By providing a unified namespace across flash and object tiers, Weka eliminates the need for manual data copies between storage layers.
This streamlined data pipeline reduces latency and ensures that data is efficiently delivered to GPUs at each stage of the workflow.
