What action would most likely resolve delays in aggregating results in an HPC environment managing real-time analytics?

In the context of high-performance computing (HPC) environments, particularly when focused on managing real-time analytics, optimizing the network fabric to reduce latency between the nodes is critical for resolving delays in aggregating results.

Real-time analytics relies on the rapid and efficient transfer of data between nodes to ensure that insights can be generated quickly. When latency is high, it can lead to significant delays in data transmission, ultimately impacting the speed at which results are aggregated and analyzed. By optimizing the network fabric, such as through using faster interconnects, tuning network settings, or utilizing more efficient protocols, the communication overhead can be minimized. This means data can be transferred more swiftly, thereby reducing the time taken for nodes to aggregate their results and produce output.

In contrast, the other options, while potentially beneficial in certain contexts, do not directly address the critical issue of communication speed and latency in real time. For instance, implementing aggressive data compression may help reduce the amount of data being transmitted but could add overhead in terms of CPU cycles for compression and decompression. Increasing the number of GPUs might improve processing power, but if latency remains an issue, those additional resources may not be leveraged efficiently. Switching to a batch processing model could significantly change the handling of data streams