HPC and AI Applications

The convergence of HPC (High-Performance Computing) and AI (Artificial Intelligence) is changing the world. The new era requires new storage solutions to handle growing data in an efficient manner.

Release the Value of Data

The data-driven era is dramatically reshaping the future. With AI, from genomics, financial services to electric vehicles, computer systems use mathematics and logic to simulate the reasons people use to learn new information and make decisions. HPC and AI are technologies designed to unlock the value of data. While they have long been seen as separate technologies, they are converging as the industry realizes that AI requires the powerful, scalable computing, networking, and storage that HPC provides.

Best Storage Technology for AI

AI infrastructure refers to the technology stack necessary to build, test, train, and deploy AI-driven applications. Whether it's high-performance computing, parallel file systems, or scalable storage. In HPC and AI, anyone can build fast storage. The key is how to build a fast, cost-effective, and scalable storage system.

Storage Challenges for AI

As organizations develop storage strategies to take advantage of AI and machine learning, they face two main challenges:

Retaining Data

At the beginning of AI / machine learning development, it may not be clear which data is useful and which can be discarded. Long-term archives can keep data in a well-indexed platform that acts as a data lake.

High Performance

During AI analysis, activity data must be moved to a high-performance platform for processing for optimal efficiency.

Storage for HPC Workloads

HPC and AI workloads have very specific storage requirements. These include:

High Speed Connectivity

High bandwidth front-end supports heavy workloads related to AI and machine learning. So properly configured connectivity through Fibre Channel, or iSCSI to extract data from fast storage to prepare data for AI algorithms at scale is critical to building advanced AI. QSAN has widely deployed 25 GbE / 10 GbE iSCSI or 32 Gb / 16 Gb Fibre Channel with more friendly cost for versatile connectivity.

Latency and Throughput

I/O latency is important for building and using AI models because data is read and re-read multiple times. So reducing I/O latency can reduce AI training time by days or months. The training process uses a lot of data, usually measured in terabytes per hour. As a result, QSAN XCubeFAS series provides extremely low latency and high throughput for processing AI workloads.

Scalability

The scope and scale of AI applications are various and dynamic. Storage for AI should be flexible to start from small but scale by demand cost-effectively while sustaining the similar price performance ratio. through mass storage management or appropriate scalability to avoid purchasing and maintaining unused resources. QSAN storage provides massive scale-up capabilities by connecting QSAN XCubeDAS series or 3rd-party expansion units on demand for future data growth.

Storage Solutions for HPC and AI

The goal for AI in SMB is to achieve the maximum speed with the lowest cost. XCubeFAS series is no doubt the ideal storage solution. Coupled with the commonly used protocols such as 25 GbE / 10 GbE iSCSI or 32 Gb / 16 Gb Fibre Channel to directly connect multiple servers, it will further reduce the cost without deployment of costly and complicated networking equipment. Open source parallel file systems such as BeeGFS also contribute a lot for SMB to easily adopt AI.

Performance Optimized Architecture

Here is an example of the building block for SMB. For the current project, use the active dataset as the training process in XCubeFAS series. After which the results and datasets are moved to lower-cost capacity storage such as XCubeSAN series for long-term preservation.

Budget Optimized Architecture

Hybrid storage instead of AFA and capacity storage is a cost-effective alternative for AI in SMB as the combination of the high-performance SSD and the best price-capacity ratio HDD. QSAN XCubeSAN hybrid flash storage can process the current dataset in the flash pool and move it to the capacity pool when the project is complete. Or use XEVO’s enterprise features such as SSD cache, auto tiering, and QoS (Quality of Service) to improve storage efficiency. Increase your limited budget for optimal performance.

Why Choose QSAN for HPC and AI

Applications that use or train AI cannot rely on traditional storage to function properly. These applications require high performance, high availability storage from which they can continuously ingest large amounts of data to facilitate learning and growth.As a storage leader company, QSAN XCubeFAS and XCubeSAN series plus BeeGFS, a popular parallel file system developed and optimized for high performance computing, will form a competitive AI training environment.