optimize algorithmic complexity

When implementing or refactoring algorithms, prioritize efficient data structures and computational approaches over simpler but slower alternatives. This is especially critical in performance-sensitive code where operations may be called frequently.

Key considerations:

• Replace linear searches with hash table lookups when dealing with node/element lookups
• Use platform-specific optimizations (like ARM NEON vectorization) when available, with proper feature detection
• Preallocate resources and precompute operations when possible to avoid runtime overhead
• Consider the computational complexity of your approach and choose algorithms that scale well

Example from the codebase:

// Instead of linear search through nodes:
int j = cgraph->n_nodes - 1;
for (; j >= 0; --j) {
    if (s == cgraph->nodes[j]) {
        break;
    }
}

// Use hash table lookup:
// Allocate use_counts array with hash_size and use ggml_hash_find
// to find the slot for the node, avoiding O(n) search complexity

This principle applies broadly - from choosing vectorized implementations over scalar ones, to using hash tables instead of linear searches, to preallocating command buffers rather than creating them on-demand. The goal is to minimize computational overhead, especially in hot code paths where these optimizations compound significantly.