How should 3TB of data be stored to maximize throughput?

The best approach to maximizing throughput for storing 3TB of data is to use multiple smaller blocks. Storing data in smaller blocks can facilitate parallel processing and I/O operations, which can significantly enhance throughput. This is particularly important in high-performance computing (HPC) and big data scenarios, where access speed and the ability to read or write data concurrently are crucial for performance.

When data is divided into smaller blocks, it allows different processes or threads to access these blocks simultaneously, thereby utilizing the available bandwidth more effectively. This parallel access can greatly reduce the time taken for data retrieval and increase the overall efficiency of data handling.

In contrast, storing a single block of 3TB may lead to bottlenecks, as it restricts access to just one location at a time, limiting the potential for concurrent operations. Similarly, having just three blocks of 1TB each may not fully utilize the available throughput potential compared to using many smaller blocks, making the strategy of using multiple smaller blocks a more efficient choice for optimizing throughput overall.