Unlocking High-Performance AI with EOS Supercomputer and DDN Hot Nodes
Table of Contents
- Introduction
- The Data Center Systems Engineering Team at Nvidia
- The Design of the EOS Supercomputer
- Storage Architecture on EOS
- Instrumenting the Clusters for Extensive Monitoring
- Performance testing and Achieving 2 Terabytes per Second Read Performance
- DL Training and the Importance of Read Performance
- Utilizing the DDN Hot Nodes Feature for Enhanced Read Performance
- Ongoing Development and Collaboration with DDN
- Conclusion
🚀 Introduction
In this article, we will delve into the fascinating world of high-performance computing and artificial intelligence. We will specifically focus on the work done by the Data Center Systems Engineering Team at Nvidia and their collaboration with DDN's Exos Scaler Systems. The main highlight of this article will be the EOS Supercomputer, its design, and the storage architecture that enables its impressive performance. Moreover, we will explore the crucial role of read performance in deep learning training and how the team utilizes the DDN Hot Nodes feature to enhance efficiency. So fasten your seatbelts and let's explore the cutting-edge innovations in the world of supercomputing!
The Data Center Systems Engineering Team at Nvidia
The Data Center Systems Engineering Team at Nvidia is a group of experts dedicated to designing and building high-Scale HPC (High-Performance Computing) and AI (Artificial Intelligence) systems. With a focus on achieving lightning-fast AI performance, this team is responsible for groundbreaking projects such as the Seline Supercomputer, which debuted on the Top 500 List in June 2020. The team combines expertise from various domains, including data center management, application development, networking, and storage. Their latest project, the EOS Supercomputer, showcases their commitment to pushing the boundaries of performance.
The Design of the EOS Supercomputer
The EOS Supercomputer is a marvel of engineering, designed from the ground up to deliver exceptional performance in AI and other computational tasks. The team employed a hierarchical approach, building scalable units called "pods" that can be deployed incrementally. These pods, ranging from 32 to 128 nodes, form the foundation of the EOS system. To achieve high-scale computing, multiple pods are connected using advanced non-blocking NDR InfiniBand fabrics. This design allows the team to scale the system to its maximum potential.
Storage Architecture on EOS
A critical aspect of the EOS Supercomputer is its storage architecture. In order to ensure optimum read performance, the team strategically distributes the storage units across the pods. This balanced approach allows for efficient utilization of ports and networking capabilities. The system incorporates 48 AI 400x storage appliances connected with HDR InfiniBand, achieving a minimum read performance of 2 terabytes per second. Extensive telemetry and monitoring are carried out at various levels, enabling the team to optimize the system's performance and identify any potential bottlenecks.
Instrumenting the Clusters for Extensive Monitoring
To gain a comprehensive understanding of the EOS Supercomputer's performance, the Data Center Systems Engineering Team implements an extensive instrumentation process. The clusters are thoroughly monitored at the data center, node, and network levels. This monitoring includes capturing system metrics, behavior analysis of the scheduler and network management systems, and studying application performance. By observing the entire system comprehensively, the team can fine-tune and optimize the performance of individual components.
Performance Testing and Achieving 2 Terabytes per Second Read Performance
The performance goals of the EOS Supercomputer were ambitious but attainable. The team aimed for a minimum read performance of 2 terabytes per second to support deep learning training at scale. Through rigorous testing and optimization, they were able to achieve this impressive milestone. Real DL (Deep Learning) workloads were run to validate the system's performance, and it exceeded expectations by delivering the desired read performance. This accomplishment solidified the EOS Supercomputer's capabilities and positioned it as a powerful tool for AI researchers and practitioners.
DL Training and the Importance of Read Performance
Deep learning training is a computationally intensive process that heavily relies on read operations. Large data sets hosted on network storage need to be accessed by multiple GPUs across the system. These data sets are often too large to be completely cached on individual nodes. Therefore, efficient and high-performance read operations are crucial to minimize I/O time and maximize compute time. The EOS Supercomputer excels in this aspect, enabling researchers to focus on the compute-intensive tasks of training deep learning models.
Utilizing the DDN Hot Nodes Feature for Enhanced Read Performance
One notable feature utilized by the Data Center Systems Engineering Team is the DDN Hot Nodes feature, leveraging the persistent client cache system in the Luster file system. The EOS Supercomputer's DGX H100 nodes include local NVMe storage, with approximately half of it allocated to the hot nodes feature. The team dynamically builds a cache of frequently read data, reducing the need for network storage access and speeding up read-intensive workloads. This intelligent caching mechanism decreases network congestion and allows for Parallel execution of multiple jobs without interference.
Ongoing Development and Collaboration with DDN
The collaboration between Nvidia's Data Center Systems Engineering Team and DDN continues to drive innovation in high-performance computing. The team actively collaborates with DDN to enhance performance and explore future improvements. One significant development includes cross-realm CEROS support in the Luster file system, facilitating integration with Microsoft Active Directory for robust account management with detailed storage activity tracking. The team's flexible configuration enables the exploration of additional features, such as right caching, to further enhance performance and efficiency.
Conclusion
The EOS Supercomputer represents a remarkable achievement in high-performance computing and AI. Nvidia's Data Center Systems Engineering Team has demonstrated their expertise in designing and building cutting-edge systems. The collaboration with DDN and the strategic utilization of the DDN Hot Nodes feature have further enhanced the EOS Supercomputer's read performance and efficiency. As the world of artificial intelligence continues to evolve, these advancements will play a pivotal role in enabling groundbreaking research and driving innovation in various domains.
Highlights
- The EOS Supercomputer showcases exceptional read performance, achieving a minimum of 2 terabytes per second.
- The Data Center Systems Engineering Team at Nvidia combines expertise in data center management, application development, networking, and storage.
- The DDN Hot Nodes feature enhances read performance by dynamically caching frequently accessed data on local NVMe storage.
- Ongoing collaboration with DDN focuses on future performance enhancements and integration with Microsoft Active Directory for robust account management.
FAQ
Q: How does the EOS Supercomputer achieve its high read performance?
A: The EOS Supercomputer utilizes a strategic storage architecture combined with the DDN Hot Nodes feature, which dynamically caches frequently accessed data on local NVMe storage.
Q: What is the role of the Data Center Systems Engineering Team at Nvidia?
A: The team is responsible for designing and building high-scale HPC and AI systems, with a focus on achieving lightning-fast AI performance.
Q: How does the collaboration between Nvidia and DDN contribute to system performance?
A: The ongoing collaboration enables continuous development and optimization, leading to enhanced performance and integration with Microsoft Active Directory for improved account management.
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