Hammerspace Unveils Reference Architecture for Large Language Model Training

Details the Requirements for High-Performance Data Pipelines

Hammerspace, the company orchestrating the Next Data Cycle, today released the data architecture being used for training inference for Large Language Models (LLMs) within hyperscale environments. This architecture is the only solution in the world that enables artificial intelligence (AI) technologists to design a unified data architecture that delivers the performance of a super computing-class parallel file system coupled with the ease of application and research access to standard NFS.

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Image 1: Hyperscale LLM Training Architecture with Hammerspace (Graphic: Business Wire)

For AI strategies to succeed, organizations need the ability to scale to a massive number of GPUs, as well as the flexibility to access local and distributed data silos. Additionally, they need the ability to leverage data regardless of the hardware or cloud infrastructure on which it currently resides, as well as the security controls to uphold data governance policies. The magnitude of these requirements is particularly critical in the development of LLMs, which often necessitate utilizing hundreds of billions of parameters, tens of thousands of GPUs, and hundreds of petabytes of diverse types of unstructured data.

Hammerspace’s announcement unveils the proven architecture uniquely delivering the performance, ease of deployment, and standards-based software and hardware support required to meet the unique requirements of LLM data pipelines and data storage.

• Hammerspace Ultra High-Performance File System
  
  
  
  AI architects and technologists may need to take advantage of existing networks, storage hardware, and compute clusters while strategically adding new infrastructure as their AI operations grow.
  
  
  
  Hammerspace unifies the entire data pipeline into a single, parallel global file system that integrates existing infrastructure and data with new datasets and resources as they are added.
  
  
  
  The parallel file system architecture is critical for training AI as countless processes or nodes need to access the same data simultaneously. Hammerspace delivers efficient and concurrent data access, reduces workflow bottlenecks, and improves overall system utilization of the client servers, GPUs, network, and data storage nodes.

• Hammerspace Standards-Based Software Approach
  
  
  
  The Hammerspace parallel file system client is an NFS4.2 client built into Linux, leveraging Hammerspace’s contribution of FlexFiles into the Linux distribution. This approach uniquely enables existing standard Linux client servers to achieve direct, high-performance access to data via Hammerspace’s software. The use of a standard NAS interface empowers researchers and applications to easily access data over the widely adopted NFS protocol and enables the ability to tap into the larger user and vendor communities that are troubleshooting, updating and improving a standards-based environment.

• Hammerspace on Commodity Hardware
  
  
  
  Hammerspace provides a software-defined data platform compatible with any standards-based hardware such as white box Linux servers, Open Compute Project (OCP) hardware, Supermicro, etc. This allows organizations to better leverage their existing hardware investment and benefit from cost-effective infrastructure at scale.

• Hammerspace Streamlined Data Pipelines
  
  
  
  The Hammerspace architecture creates a unified, high-performance global data environment that provides concurrent and continuous execution of all phases of LLM training and inference workloads. Hammerspace is unique in its ability to break down data silos, seamlessly accessing training data scattered across diverse data center and cloud storage systems from any vendor or location.
  
  
  
  By leveraging training data wherever it might be stored, Hammerspace streamlines AI workloads by minimizing the need to copy and move files into a consolidated new repository. This approach reduces overhead, as well as the risk of introducing errors and inaccuracies in LLMs. At the application level, data is accessed through a standard NFS file interface to ensure direct access to files in the standard format applications are typically designed for.

• Hammerspace High-Speed Data Path
  
  
  
  Hammerspace reduces network transmissions and data hops at every point possible within the data path. This approach ensures near 100 percent utilization of the available infrastructure while delivering a streamlined high-bandwidth, low-latency data path between applications, compute, and data storage nodes. More detail about the innovation and benefits can be found in the IEEE article, “Overcoming Performance Bottlenecks With a Network File System in Solid State Drives” by David Flynn and Thomas Coughlin.

• Hammerspace Fault-Tolerant Design
  
  
  
  LLM environments are massive, complex systems with extensive power and infrastructure. These AI systems often rely on continuously updating models based on new data. Hammerspace is capable of operating at peak performance through a system outage, allowing AI technologies to focus less on recovery from power, network, or system failures and more on persistence through those failures.

• Hammerspace Objective-Based Data Placement
  
  
  
  Hammerspace software decouples the file system layer from the storage layer, enabling independent scaling of I/O and IOPS at the data layer. Extremely high-performance NVMe storage can co-exist with lower cost, lower performing, and geographically distributed storage tiers – including the cloud – in a global data environment. Data orchestration between tiers and/or locations is controlled transparently as a background operation based on objective-based policies. These software objectives enable powerful automation to ensure data is automatically placed on the nodes, delivering the required performance when in use. When not in use, data can remain in high-performance storage nodes or be automatically placed in a more efficient location to reduce storage costs on inactive data. This approach ensures data is always available to saturate GPUs and network capacities when needed.
  
  
  
  Integrated machine learning (ML) capabilities within the Hammerspace architecture will begin to place related data sets in high-performance, local NVMe storage when the first file from the data set is accessed.

“The most powerful AI initiatives will incorporate data from everywhere,” said David Flynn, Hammerspace Founder and CEO. “A high-performance data environment is critical to the success of initial AI model training. But even more important, it provides the ability to orchestrate the data from multiple sources for continuous learning. Hammerspace has set the gold standard for AI architectures at scale.”

Learn More

• White Paper: Revolutionize Your AI Workloads with Hammerspace
• eBook: Unstructured Data Management for Dummies, Hammerspace Special Edition
• Solution Page: Hammerspace for Artificial Intelligence
• Podcast: Best Practices in Building AI Data Pipelines with David Flynn
• Podcast: Making the Potential of pNFS a Reality with Trond Myklebust

Press Kit Images

About Hammerspace

Hammerspace is the data orchestration system that unlocks innovation and opportunity within unstructured data. It orchestrates and provides the high-performance data needed to build new products, uncover new insights, and accelerate time to revenue across industries like AI, scientific discovery, machine learning, extended reality, autonomy, corporate video and more. Hammerspace delivers the world’s first and only solution to connect global users with their data and applications on any vendor’s data center storage or public cloud services, including AWS, Google Cloud, Microsoft Azure and Seagate Lyve Cloud.

Hammerspace and the Hammerspace logo are trademarks of Hammerspace, Inc. All other trademarks used herein are the property of their respective owners.

©2023 Hammerspace, Inc. All rights reserved.

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