WEKA: A High-Performance Storage Solution for AI Workloads

Introduction to WEKA

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.

Key Characteristics of WEKA

1. 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.

2. 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.

3. 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.

4. 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.

5. Data Protection: WEKA ensures data protection through distributed erasure coding, providing resilience against multiple concurrent drive or node failures without sacrificing performance.

6. Storage Efficiency: Features like thin provisioning, inline compression, and deduplication help optimize storage utilization and reduce costs.

7. 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.

WekaFS combines NVMe flash with cloud object storage in a single global namespace

An explanation of Weka's cloud architecture

Weka's cloud architecture is designed to provide a high-performance, scalable, and flexible storage solution for demanding workloads such as AI, machine learning, and high-performance computing. The core components and their interactions are as follows:

Weka Cluster

• The Weka cluster consists of a group of EC2 instances (e.g., i3en instances in AWS or LSv3 instances in Azure) that run the Weka software.

• These instances leverage their local NVMe storage to create a distributed, high-performance flash tier.

• The Weka software aggregates and virtualizes the NVMe capacity across all instances, presenting it as a single, unified storage pool.

• Data is distributed across the cluster using Weka's proprietary data protection scheme, which provides redundancy and fault tolerance.

Data Tiering and Object Storage Integration

• Weka extends the storage capacity by seamlessly tiering data between the high-performance flash tier and a cheaper object storage tier (e.g., Amazon S3 or Azure Blob Storage).

• The Weka filesystem manages the tiering process transparently, ensuring that hot data resides on the flash tier for optimal performance, while cold data is moved to the object tier for cost-effective storage.

• Tiering granularity is at the file level, with Weka using a standard block size to chunk large files and aggregate small files for efficient tiering and metadata management.

• Weka maintains a single namespace that spans both the flash tier and the object tier, providing a unified view of the entire dataset.

Data Access and Protocol Support

• Applications can access the Weka filesystem using various standard protocols such as NFS, SMB, and S3, as well as Weka's native POSIX client for optimal performance.

• In Kubernetes environments, Weka provides a CSI driver for seamless integration and dynamic provisioning of persistent volumes.

• Weka's client software runs in user space and communicates directly with the storage nodes, bypassing the kernel to minimize latency and maximize throughput.

Deployment Automation and Scalability

• Weka integrates with infrastructure-as-code tools like Terraform and AWS CloudFormation to automate the deployment process.

• These tools provision the necessary cloud resources (e.g., VPC, security groups, instances) and configure the Weka cluster based on predefined templates.

• Weka supports user-driven scaling, allowing administrators to easily add or remove storage nodes to adapt to changing capacity and performance requirements.

• The Weka cluster automatically rebalances data across nodes when the cluster size changes, ensuring optimal data distribution and performance.

Hybrid and Multi-Cloud Capabilities

• Weka enables hybrid and multi-cloud deployments through its "Snap-to-Object" feature, which creates a point-in-time, self-describing snapshot of the entire Weka filesystem (data and metadata) and stores it in an object store.

• These snapshots can be replicated to object stores in different regions or cloud providers, providing a mechanism for data mobility and disaster recovery.

• Weka clusters can be rapidly provisioned in the target cloud environment using the snapshot data, enabling use cases like cloud bursting, data migration, and multi-cloud workflows.

Converged Deployment Mode

• In addition to the traditional dedicated storage cluster deployment, Weka supports a converged deployment mode where the Weka software runs on the same instances as the application.

• This mode leverages the abundant compute resources (vCPUs and memory) available in modern instance types, particularly those with GPU acceleration.

• Converged deployments can offer cost and efficiency benefits by reducing the need for separate storage infrastructure, especially in scenarios with ample instance resources.

Key Architectural Advantages

• Weka's distributed, parallel filesystem architecture enables high performance and scalability by leveraging the aggregate bandwidth and IOPS of multiple NVMe devices.

• The tiering mechanism extends capacity cost-effectively while maintaining a single namespace and transparent data movement between tiers.

• Support for standard protocols and integration with Kubernetes simplifies application access and deployment.

• Infrastructure-as-code integration and user-driven scaling streamline cluster management and enable elasticity.

• Snap-to-Object and multi-cloud capabilities unlock data mobility, disaster recovery, and flexibility in choosing cloud providers.

• The converged deployment mode optimizes resource utilization and reduces infrastructure complexity in suitable scenarios.

Overall, Weka's cloud architecture combines high-performance NVMe storage, intelligent tiering, and multi-cloud mobility to address the storage challenges of data-intensive workloads in the cloud.

Why WEKA's Architecture Excels for AI Workloads

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.