👉 Fat computing, also known as fat storage or fat-based computing, is an innovative approach that extends the concept of traditional computing by leveraging the inherent parallelism and distributed nature of data storage systems. In this model, data is not just stored in memory but is instead distributed across a network of storage devices, often including both high-performance and low-power storage media. This allows for efficient processing of large datasets by offloading computational tasks to the storage devices, which can perform parallel operations due to their massively parallel architecture. Fat computing systems use specialized hardware and software to manage data movement, ensuring that frequently accessed data remains close to the processing units, thereby reducing latency and improving overall system performance. This approach is particularly beneficial for big data analytics, machine learning, and other applications requiring high throughput and low latency.
