Dropping eBPF CPU Cost by About 90% with Memoization (Not AI Gen)

A developer, Nathan Naveen, has achieved a 90% reduction in CPU cost for eBPF (Extended Berkeley Packet Filter) by utilizing memoization, a technique that stores the results of expensive function calls so they can be reused instead of recalculated. This breakthrough is particularly significant for applications that rely heavily on eBPF, such as network monitoring and security tools. To achieve the reduction, Naveen applied memoization to the eBPF functions that generated the most overhead, resulting in a substantial decrease in CPU utilization. The memoization was implemented using a hash table to store the results of previous function calls, allowing the functions to quickly retrieve the cached results instead of recalculating them.