A deep dive into BPF LPM trie performance and optimization
6 months ago
- #Performance Optimization
- #BPF
- #Data Structures
- BPF LPM trie maps are critical for IP and IP+Port matching in network routing but face performance issues with large datasets.
- Performance bottlenecks include slow entry lookups (hundreds of milliseconds) and CPU lockups during map freeing (over 10 seconds).
- Tries are efficient for prefix matching, especially with IP routing using CIDR, due to their memory-efficient and search-optimized structure.
- Multibit tries offer faster traversal than binary tries by comparing multiple bits at once, trading space for time.
- Path compression and level compression are optimizations that can reduce memory usage and improve lookup times in tries.
- BPF LPM trie implementation lacks full optimization, leading to inefficiencies like high cache and TLB miss rates as entry count grows.
- Future work includes refactoring to use Level Compressed tries from net/ipv4/fib_trie.c to improve BPF LPM trie performance.