Performance Optimization Skill

Apply these principles when optimizing code for performance. Focus on the critical 3% where performance truly matters - a 12% improvement is never marginal in engineering.

Core Philosophy

1. Measure First: Never optimize without profiling data
2. Estimate Costs: Back-of-envelope calculations before implementation
3. Avoid Work: The fastest code is code that doesn't run
4. Reduce Allocations: Memory allocation is often the hidden bottleneck
5. Cache Locality: Memory access patterns dominate modern performance

Reference Latency Numbers

Use these for back-of-envelope calculations:

Operation	Latency
L1 cache reference	0.5 ns
Branch mispredict	5 ns
L2 cache reference	7 ns
Mutex lock/unlock	25 ns
Main memory reference	100 ns
Compress 1KB (Snappy)	3 us
SSD random read (4KB)	20 us
Round trip in datacenter	50 us
Disk seek	5 ms

Optimization Techniques (Priority Order)

1. Algorithmic Improvements (Highest Impact)

Always check algorithm complexity first:

O(N^2) → O(N log N)  = 1000x faster for N=1M
O(N)   → O(1)        = unbounded improvement

Common patterns:

• Replace nested loops with hash lookups
• Use sorted-list intersection → hash table lookups
• Process in reverse post-order to eliminate per-element checks
• Replace interval trees (O(log N)) with hash maps (O(1)) when ranges aren't needed

2. Reduce Memory Allocations

Allocation is expensive (~25-100ns + GC pressure)

# BAD: Allocates on every call
def process(items):
    result = []  # New allocation
    for item in items:
        result.append(transform(item))
    return result

# GOOD: Pre-allocate or reuse
def process(items, out=None):
    if out is None:
        out = [None] * len(items)
    for i, item in enumerate(items):
        out[i] = transform(item)
    return out