cpu-profiling

name	cpu-profiling
description	Profile CPU usage to identify hot spots and bottlenecks. Optimize code paths consuming most CPU time for better performance and resource efficiency.

CPU Profiling

Overview

CPU profiling identifies which functions consume most CPU time, enabling targeted optimization of expensive code paths.

When to Use

• High CPU usage
• Slow execution
• Performance regression
• Before optimization
• Production monitoring

Instructions

1. Profiling Tools

Browser Profiling:

Chrome DevTools:
  Steps:
    1. DevTools → Performance
    2. Click record
    3. Perform action
    4. Stop recording
    5. Analyze flame chart
  Metrics:
    - Function call duration
    - Call frequency
    - Total time vs self time

Firefox Profiler:
  - Built-in performance profiler
  - Flame graphs
  - Timeline view
  - Export and share

React Profiler:
  - DevTools → Profiler
  - Component render times
  - Phase: render vs commit
  - Why component re-rendered

---

Node.js Profiling:

node --prof app.js
node --prof-process isolate-*.log > profile.txt

Clinic.js:
  clinic doctor -- node app.js
  clinic flame -- node app.js
  Shows: functions, memory, delays

V8 Inspector:
  node --inspect app.js
  Open chrome://inspect
  Profiler tab
  Take CPU profile

2. Analysis & Interpretation

// Understanding profiles

Flame Graph Reading:
- Wider = more time spent
- Taller = deeper call stack
- Hot path = wide tall bars
- Idle = gaps

Self Time vs Total Time:
- Self: time in function itself
- Total: self + children
- Example:
  main() calls work() for 1s
  work() itself = 0.5s (self)
  work() itself + children = 1s (total)

Hot Spots Identification:
- Find widest bars (most time)
- Check if avoidable
- Check if optimizable
- Profile before/after changes

Example (V8 Analysis):
Function: dataProcessing
  Self time: 500ms (50%)
  Total time: 1000ms
  Calls: 1000 times
  Time per call: 0.5ms
  Optimization: Reduce call frequency

3. Optimization Process

Steps:

1. Establish Baseline
  - Profile current behavior
  - Note hottest functions
  - Record total time
  - Check system resources

2. Identify Bottlenecks
  - Find top 5 time consumers
  - Analyze call frequency
  - Understand what they do
  - Check if necessary

3. Create Hypothesis
  - Why is function slow?
  - Can algorithm improve?
  - Can we cache results?
  - Can we parallelize?

4. Implement Changes
  - Single change at a time
  - Measure impact
  - Profile after change
  - Compare flame graphs

5. Verify Improvement
  - Baseline: 1s
  - After optimization: 500ms
  - Confirmed 50% improvement

---

Common Optimizations:

Algorithm Improvement:
  Before: O(n²) nested loop = 100ms for 1000 items
  After: O(n log n) with sort+search = 10ms
  Impact: 10x faster

Caching:
  Before: Recalculate each call
  After: Cache result, return instantly
  Impact: 1000x faster for repeated calls

Memoization:
  Before: fib(40) recalculates each branch
  After: Cache computed values
  Impact: Exponential to linear

Lazy Evaluation:
  Before: Calculate all values upfront
  After: Calculate only needed values
  Impact: 90%+ reduction for partial results

Parallelization:
  Before: Sequential processing, 1000ms
  After: 4 cores, 250ms
  Impact: 4x faster (8 cores = 8x)

4. Monitoring & Best Practices

Monitoring:

Production Profiling:
  - Lightweight sampling profiler
  - 1-5% overhead typical
  - Tools: New Relic, DataDog, Clinic
  - Alert on CPU spikes

Key Metrics:
  - CPU usage % per function
  - Call frequency
  - Time per call
  - GC pause times
  - P95/P99 latency

---

Best Practices:

Before Optimizing:
  [ ] Profile to find actual bottleneck
  [ ] Don't guess (verify with data)
  [ ] Establish baseline
  [ ] Measure improvement

During Optimization:
  [ ] Change one thing at a time
  [ ] Profile after each change
  [ ] Verify improvement
  [ ] Don't prematurely optimize

Premature Optimization:
  - Profile first
  - Hot path only (80/20 rule)
  - Measure impact
  - Consider readability

---

Tools Summary:

Framework: Chrome DevTools, Firefox, Node Profiler
Analysis: Flame graphs, Call trees, Timeline
Monitoring: APM tools, Clinic.js
Comparison: Before/after profiles

---

Red Flags:

- Unexpected high CPU
- GC pauses >100ms
- Function called 1M times per request
- Deep call stacks
- Synchronous I/O in loops
- Repeated calculations
- Memory allocation in hot loop

Key Points

• Profile before optimizing (measure, not guess)
• Look for wide/tall bars in flame graphs
• Distinguish self time vs total time
• Optimize top bottlenecks first
• Verify improvements with measurement
• Consider caching and memoization
• Use production profiling for real issues
• Algorithm improvements beat micro-optimizations
• Measure before and after
• Focus on hot paths (80/20 rule)