Supercharge Your Intelligent Computing Center with AI-Ready Data Infrastructure
Increasing numbers of enterprises are harnessing the power of AI to unlock the full potential of data. The importance of data infrastructure in this endeavor cannot be overstated. Today, we delve into the pivotal role of AI-ready data infrastructure in bolstering intelligent computing centers.
Major challenges you cannot overlook
With the rise of large AI models, intelligent computing centers typically face the following challenges:
Compute performance
- Available computing power fails to increase linearly with AI training cluster growth.
- Compute clusters need 10 TB/s aggregate bandwidth and hundreds of millions of IOPS.
- Siloed construction of service systems creates data silos, preventing a large AI cluster from synchronous, consistent data access.
- A unified namespace is needed to ensure strong data consistency for synchronous access.
- The rapid growth of multimodal AI models like Sora is causing massive data growth.
- AI storage capacity needs to be expanded from petabytes to exabytes.
We recommend a unified AI data lake solution that can efficiently manage exabytes of data in an intelligent computing center. This solution should have the following features:
Key feature 1: Unified namespace
A unified namespace is a high-performance file system that enables large-scale shared access and elastic scalability. It has the following characteristics:
Unified metadata management
This enables a unified namespace to achieve almost unlimited scalability and mass data management.
Multi-protocol convergence and interworking
- A unified namespace supports data access through NFS SMB, S3, and HDFS protocols.
- A unified storage architecture enables file, object, and other storage protocols to work together more closely for easier data sharing.
Global unified storage across data centers in different regions enables a unified data view for higher data consistency and availability and lower access latency. This lays a data foundation for cross-region distributed parallel training.
Key feature 2: Scalability in the exabytes and intelligent tiering
The rapid growth of multimodal applications such as Suno and Sora demonstrates how large AI models thrive on ever-increasing data volumes. At its core, AI is about extracting knowledge from mass data.
That's why the storage foundation of an AI data lake needs to be scalable from petabytes to exabytes and provide cost-effective data tiering.
Elastic expansion in the exabytes
To handle the growing training and inference needs of large multimodal models, a storage foundation should use a fully symmetric scale-out architecture that can effortlessly expand to thousands of nodes and EB-scale capacity.
Additionally, the storage system should be equipped with built-in automatic load balancing policies to evenly distribute data and metadata across all nodes, eliminating metadata access bottlenecks and ensuring system performance after ultra-large-scale expansion.
Intelligent tiering
When a large amount of low-value data exists, it consumes critical system resources and occupies substantial storage space.
Intelligent tiering automatically migrates data of varying value so that hot and cold data is stored in appropriate storage spaces, making it essential for AI data lake storage.
Key feature 3: Data and control plane separation
Research shows that data processing before GPU/NPU computing makes up 70% of the training and inference time for large AI models. Storage cluster performance is key to improving AI cluster utilization.
The innovative data and control plane separation architecture enables CPUs to only process control flows while using DPUs for dedicated data processing. This means data flows bypass CPUs and memory, creating a simplified and fast data access path for metadata and data passthrough. And the result? 10x higher system performance.
Key feature 4: One-stop knowledge generation
Large AI model training requires extensive, high-quality data. However, training is often plagued by large amounts of inaccurate, superfluous, and machine-generated junk data.
Therefore, filtering out low-quality data before training large AI models is essential. A one-stop data processing tool is recommended to simplify this time-consuming and labor-intensive process. The tool should have the following key capabilities:
- Data loading
- Data cleansing
- Data compliance response
- High-quality corpus generation
- Knowledge generation
Your O&M platform should have comprehensive capabilities that cover the entire lifecycle of AI workflows, including managing the following items:
- AI infrastructure
- AI data
- AI training jobs
- AI inference applications
- AI service operations
You need strong ransomware protection measures for your data resilience strategies. A four-layer protection system is typically required to establish the last line of defense.
- Layer 1: Detection and analytics functions intercept ransomware.
- Layer 2: Production storage uses secure snapshots to recover data in seconds.
- Layer 3: Local backups contain clean and valid data copies to prevent data loss.
- Layer 4: A data copy is kept offline in an air-gap isolation zone, invisible to viruses.
If you don't want your intelligent computing center to be burdened with data silos or performance and capacity issues, consider upgrading to AI-ready data infrastructure.
Huawei is an industry leader with over 20 years of extensive investment in data infrastructure. It offers a broad range of products, solutions, and case studies to help you handle AI workloads with ease. Learn more about our award-winning OceanStor Data Storage and how to unleash the full potential of your data.
Disclaimer: Any views and/or opinions expressed in this post by individual authors or contributors are their personal views and/or opinions and do not necessarily reflect the views and/or opinions of Huawei Technologies.
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