Efficiency of Data Centric Computing | SNIA

Abstract

In today's data-driven world, the rapid growth of large datasets presents a significant challenge for many applications. At the same time, AI workloads are becoming increasingly dominant in various aspects, exhibiting growth not only in data size but also in throughput, latency requirements, and more. Moving computation closer to data storage is more efficient than transferring large amounts of data, as federated data processing allows for better system efficiency, reduced network traffic, and lower latency, ultimately resulting in improved total cost of ownership (TCO).

Focusing on data-centric computing, we will discuss the need to integrate compute, data, and I/O acceleration closely with storage systems and data nodes. This approach can provide a highly efficient engine for data-heavy applications, such as training large language models, supporting the metaverse in next-generation data centers, and enabling edge computing. By addressing the requirements for low latency and high bandwidth services, this strategy helps tackle the coexistence of "big" and "fast" data challenges.