Multi Agent Performance Profiling

Multi-Agent Performance Profiling

Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.

Overview

Prescriptive workflow for spawning parallel profiling agents to comprehensively identify performance bottlenecks across multiple system layers. Successfully discovered that QuestDB ingests at 1.1M rows/sec (11x faster than target), proving database was NOT the bottleneck - CloudFront download was 90% of pipeline time.

When to Use This Skill

Use this skill when:

• Performance below SLO (e.g., 47K vs 100K rows/sec target)
• Multi-stage pipeline optimization (download → extract → parse → ingest)
• Database performance investigation
• Bottleneck identification in complex workflows
• Pre-optimization analysis (before making changes)

Key outcomes:

• Identify true bottleneck (vs assumed bottleneck)
• Quantify each stage's contribution to total time
• Prioritize optimizations by impact (P0/P1/P2)
• Avoid premature optimization of non-bottlenecks

Core Methodology

1. Multi-Layer Profiling Model (5-Agent Pattern)

Agent 1: Profiling (Instrumentation)

• Empirical timing of each pipeline stage
• Phase-boundary instrumentation with time.perf_counter()
• Memory profiling (peak usage, allocations)
• Bottleneck identification (% of total time)

Agent 2: Database Configuration Analysis

• Server settings review (WAL, heap, commit intervals)
• Production vs development config comparison
• Expected impact quantification (<5%, 10%, 50%)

Agent 3: Client Library Analysis

• API usage patterns (dataframe vs row-by-row)
• Buffer size tuning opportunities
• Auto-flush behavior analysis

Agent 4: Batch Size Analysis

• Current batch size validation
• Optimal batch range determination
• Memory overhead vs throughput tradeoff

Agent 5: Integration & Synthesis

• Consensus-building across agents
• Prioritization (P0/P1/P2) with impact quantification
• Implementation roadmap creation

2. Agent Orchestration Pattern

Parallel Execution (all 5 agents run simultaneously):

Agent 1 (Profiling)          → [PARALLEL]
Agent 2 (DB Config)          → [PARALLEL]
Agent 3 (Client Library)     → [PARALLEL]
Agent 4 (Batch Size)         → [PARALLEL]
Agent 5 (Integration)        → [PARALLEL - reads tmp/ outputs from others]

Key Principle: No dependencies between investigation agents (1-4). Integration agent synthesizes findings.

Dynamic Todo Management:

• Start with investigation plan (5 agents)
• Spawn agents in parallel using single message with multiple Task tool calls
• Update todos as each agent completes
• Integration agent waits for all findings before synthesizing

3. Profiling Script Structure

Each agent produces:

1. Investigation Script (e.g., profile_pipeline.py)

• time.perf_counter() instrumentation at phase boundaries
• Memory profiling with tracemalloc
• Structured output (phase, duration, % of total)

2. Report (markdown with findings, recommendations, impact quantification) 3. Evidence (benchmark results, config dumps, API traces)

Example Profiling Code:

import time

# Profile multi-stage pipeline
def profile_pipeline():
    results = {}

    # Phase 1: Download
    start = time.perf_counter()
    data = download_from_cdn(url)
    results["download"] = time.perf_counter() - start

    # Phase 2: Extract
    start = time.perf_counter()
    csv_data = extract_zip(data)
    results["extract"] = time.perf_counter() - start

    # Phase 3: Parse
    start = time.perf_counter()
    df = parse_csv(csv_data)
    results["parse"] = time.perf_counter() - start

    # Phase 4: Ingest
    start = time.perf_counter()
    ingest_to_db(df)
    results["ingest"] = time.perf_counter() - start

    # Analysis
    total = sum(results.values())
    for phase, duration in results.items():
        pct = (duration / total) * 100
        print(f"{phase}: {duration:.3f}s ({pct:.1f}%)")

    return results

4. Impact Quantification Framework

Priority Levels:

• P0 (Critical): >5x improvement, addresses primary bottleneck
• P1 (High): 2-5x improvement, secondary optimizations
• P2 (Medium): 1.2-2x improvement, quick wins
• P3 (Low): <1.2x improvement, minor tuning

Impact Reporting Format:

### Recommendation: [Optimization Name] (P0/P1/P2) - [IMPACT LEVEL]

**Impact**: 🔴/🟠/🟡 **Nx improvement**
**Effort**: High/Medium/Low (N days)
**Expected Improvement**: CurrentK → TargetK rows/sec

**Rationale**:

- [Why this matters]
- [Supporting evidence from profiling]
- [Comparison to alternatives]

**Implementation**:
[Code snippet or architecture description]

5. Consensus-Building Pattern

Integration Agent Responsibilities:

1. Read all investigation reports (Agents 1-4) 2. Identify consensus recommendations (all agents agree) 3. Flag contradictions (agents disagree) 4. Synthesize master integration report 5. Create implementation roadmap (P0 → P1 → P2)

Consensus Criteria:

• ≥3/4 agents recommend same optimization → Consensus
• 2/4 agents recommend, 2/4 neutral → Investigate further
• Agents contradict (one says "optimize X", another says "X is not bottleneck") → Run tie-breaker experiment

Workflow: Step-by-Step

Step 1: Define Performance Problem

Input: Performance metric below SLO Output: Problem statement with baseline metrics

Example Problem Statement:

Performance Issue: BTCUSDT 1m ingestion at 47K rows/sec
Target SLO: >100K rows/sec
Gap: 53% below target
Pipeline: CloudFront download → ZIP extract → CSV parse → QuestDB ILP ingest

Step 2: Create Investigation Plan

Directory Structure:

tmp/perf-optimization/
  profiling/              # Agent 1
    profile_pipeline.py
    PROFILING_REPORT.md
  questdb-config/         # Agent 2
    CONFIG_ANALYSIS.md
  python-client/          # Agent 3
    CLIENT_ANALYSIS.md
  batch-size/             # Agent 4
    BATCH_ANALYSIS.md
  MASTER_INTEGRATION_REPORT.md  # Agent 5

Agent Assignment:

• Agent 1: Empirical profiling (instrumentation)
• Agent 2: Database configuration analysis
• Agent 3: Client library usage analysis
• Agent 4: Batch size optimization analysis
• Agent 5: Synthesis and integration

Step 3: Spawn Agents in Parallel

IMPORTANT: Use single message with multiple Task tool calls for true parallelism

Example:

I'm going to spawn 5 parallel investigation agents:

[Uses Task tool 5 times in a single message]
- Agent 1: Profiling
- Agent 2: QuestDB Config
- Agent 3: Python Client
- Agent 4: Batch Size
- Agent 5: Integration (depends on others completing)

Execution:

# All agents run simultaneously (user observes 5 parallel tool calls)
# Each agent writes to its own tmp/ subdirectory
# Integration agent polls for completed reports

Step 4: Wait for All Agents to Complete

Progress Tracking:

• Update todo list as each agent completes
• Integration agent polls tmp/ directory for report files
• Once 4/4 investigation reports exist → Integration agent synthesizes

Completion Criteria:

• All 4 investigation reports written
• Integration report synthesizes findings
• Master recommendations list created

Step 5: Review Master Integration Report

Report Structure:

# Master Performance Optimization Integration Report

## Executive Summary

- Critical discovery (what is/isn't the bottleneck)
- Key findings from each agent (1-sentence summary)

## Top 3 Recommendations (Consensus)

1. [P0 Optimization] - HIGHEST IMPACT
2. [P1 Optimization] - HIGH IMPACT
3. [P2 Optimization] - QUICK WIN

## Agent Investigation Summary

### Agent 1: Profiling

### Agent 2: Database Config

### Agent 3: Client Library

### Agent 4: Batch Size

## Implementation Roadmap

### Phase 1: P0 Optimizations (Week 1)

### Phase 2: P1 Optimizations (Week 2)

### Phase 3: P2 Quick Wins (As time permits)

Step 6: Implement Optimizations (P0 First)

For each recommendation:

1. Implement highest-priority optimization (P0) 2. Re-run profiling script 3. Verify expected improvement achieved 4. Update report with actual results 5. Move to next priority (P1, P2, P3)

Example Implementation:

# Before optimization
uv run python tmp/perf-optimization/profiling/profile_pipeline.py
# Output: 47K rows/sec, download=857ms (90%)

# Implement P0 recommendation (concurrent downloads)
# [Make code changes]

# After optimization
uv run python tmp/perf-optimization/profiling/profile_pipeline.py
# Output: 450K rows/sec, download=90ms per symbol * 10 concurrent (90%)

Real-World Example: QuestDB Refactor Performance Investigation

Context: Pipeline achieving 47K rows/sec, target 100K rows/sec (53% below SLO)

Assumptions Before Investigation:

• QuestDB ILP ingestion is the bottleneck (4% of time)
• Need to tune database configuration
• Need to optimize Sender API usage

Findings After 5-Agent Investigation:

1. Profiling Agent: CloudFront download is 90% of time (857ms), ILP ingest only 4% (40ms) 2. QuestDB Config Agent: Database already optimal, tuning provides <5% improvement 3. Python Client Agent: Sender API already optimal (using dataframe() bulk ingestion) 4. Batch Size Agent: 44K batch size is within optimal range 5. Integration Agent: Consensus recommendation - optimize download, NOT database

Top 3 Recommendations:

1. 🔴 P0: Concurrent multi-symbol downloads (10-20x improvement) 2. 🟠 P1: Multi-month pipeline parallelism (2x improvement) 3. 🟡 P2: Streaming ZIP extraction (1.3x improvement)

Impact: Discovered database ingests at 1.1M rows/sec (11x faster than target) - proving database was never the bottleneck

Outcome: Avoided wasting 2-3 weeks optimizing database when download was the real bottleneck

Common Pitfalls

1. Profiling Only One Layer

❌ Bad: Profile database only, assume it's the bottleneck ✅ Good: Profile entire pipeline (download → extract → parse → ingest)

2. Serial Agent Execution

❌ Bad: Run Agent 1, wait, then run Agent 2, wait, etc. ✅ Good: Spawn all 5 agents in parallel using single message with multiple Task calls

3. Optimizing Without Profiling

❌ Bad: "Let's optimize the database config first" (assumption-driven) ✅ Good: Profile first, discover database is only 4% of time, optimize download instead

4. Ignoring Low-Hanging Fruit

❌ Bad: Only implement P0 (highest impact, highest effort) ✅ Good: Implement P2 quick wins (1.3x for 4-8 hours effort) while planning P0

5. Not Re-Profiling After Changes

❌ Bad: Implement optimization, assume it worked ✅ Good: Re-run profiling script, verify expected improvement achieved

Resources

scripts/

Not applicable - profiling scripts are project-specific (stored in tmp/perf-optimization/)

references/

• profiling_template.py - Template for phase-boundary instrumentation
• integration_report_template.md - Template for master integration report
• impact_quantification_guide.md - How to assess P0/P1/P2 priorities

assets/

Not applicable - profiling artifacts are project-specific

---

Troubleshooting

Post-Execution Reflection

After this skill completes, reflect before closing the task:

0. Locate yourself. — Find this SKILL.md's canonical path before editing. 1. What failed? — Fix the instruction that caused it. 2. What worked better than expected? — Promote to recommended practice. 3. What drifted? — Fix any script, reference, or dependency that no longer matches reality. 4. Log it. — Evolution-log entry with trigger, fix, and evidence.

Do NOT defer. The next invocation inherits whatever you leave behind.

Evolution Log

Convention: Reverse chronological order (newest on top, oldest at bottom). Prepend new entries.

---

2026-02-26: Initial Evolution Log

Status: Skill is in use and maintained. Track improvements here.

Purpose

This evolution log tracks updates to the skill. Each entry should note:

• What changed (content, structure, tooling)
• Why it changed (bug fix, feature request, best practice)
• Files affected

How to Use

1. When updating SKILL.md or references, add an entry here with the date 2. Keep entries reverse-chronological (newest first) 3. Link to ADRs or GitHub issues when relevant 4. Reference specific line changes when helpful

---

Skill: Multi-Agent Performance Profiling

Impact Quantification Guide

How to Assess P0/P1/P2/P3 Priorities

---

Priority Framework

P0 (Critical Priority) - HIGHEST IMPACT

Criteria:

• Improvement: >5x performance gain
• Bottleneck: Addresses primary bottleneck (>50% of total time)
• ROI: High impact despite high effort
• Risk: Acceptable risk/reward ratio

Examples:

• Concurrent downloads (10-20x improvement, 90% bottleneck)
• Algorithm replacement (O(n²) → O(n log n) for large n)
• Caching layer for frequently accessed data (10x+ improvement)

Decision Matrix:

---

P1 (High Priority) - HIGH IMPACT

Criteria:

• Improvement: 2-5x performance gain
• Bottleneck: Addresses secondary bottleneck (20-50% of time)
• ROI: Medium-high impact, medium effort
• Risk: Low to medium risk

Examples:

• Pipeline parallelism (2x improvement by overlapping download + ingest)
• Index optimization in database (2-3x query improvement)
• Memory allocation tuning (2x improvement in GC overhead)

Decision Matrix:

---

P2 (Medium Priority) - QUICK WINS

Criteria:

• Improvement: 1.2-2x performance gain
• Bottleneck: May not address primary bottleneck
• ROI: Low effort, measurable impact
• Risk: Very low risk

Examples:

• Streaming ZIP extraction (1.3x improvement, 4-8 hours effort)
• Connection pooling (1.5x improvement for high-frequency requests)
• Buffer size tuning (1.2-1.5x improvement)

Decision Matrix:

---

P3 (Low Priority) - MINOR TUNING

Criteria:

• Improvement: <1.2x performance gain
• Bottleneck: Does not address primary bottleneck
• ROI: Low impact, any effort level
• Risk: Low risk but also low value

Examples:

• Logging verbosity reduction (1.05x improvement)
• String concatenation optimization (1.1x improvement)
• Minor config parameter tuning (<5% improvement)

Decision Matrix:

---

Calculating Impact

1. Measure Baseline

# Run profiling 3+ times, average results
baseline_time = 952  # ms (average of 3 runs)
baseline_throughput = 47_000  # rows/sec

2. Estimate Optimized Performance

Method A: Phase Elimination (if removing bottleneck entirely)

# If download is 857ms (90% of 952ms total):
optimized_time = 952 - 857 + estimated_new_download_time
# e.g., if concurrent downloads reduce to 90ms:
optimized_time = 952 - 857 + 90 = 185ms

improvement = baseline_time / optimized_time
# 952 / 185 = 5.1x improvement

Method B: Phase Acceleration (if speeding up bottleneck)

# If download is 857ms and we can do 10 concurrent:
new_download_time = 857 / 10 = 86ms  # (assuming perfect parallelism)
optimized_time = 952 - 857 + 86 = 181ms

improvement = 952 / 181 = 5.3x improvement

Method C: Amdahl's Law (for partial parallelization)

# If 90% of time is parallelizable with 10 workers:
speedup = 1 / ((1 - 0.9) + (0.9 / 10))
# speedup = 1 / (0.1 + 0.09) = 5.26x improvement

3. Assign Priority

if improvement >= 5.0:
    priority = "P0"
elif improvement >= 2.0:
    priority = "P1"
elif improvement >= 1.2:
    priority = "P2"
else:
    priority = "P3"

# Adjust based on effort:
if effort_days > 10 and improvement < 10:
    priority = downgrade(priority)  # P0 → P1, P1 → P2, etc.

---

Real-World Examples

Example 1: Concurrent Downloads (P0)

Baseline: 857ms download, 952ms total Optimization: 10 concurrent downloads Estimated: 857ms / 10 = 86ms per download (parallelized) New Total: 952 - 857 + 86 = 181ms Improvement: 952 / 181 = 5.3x ← P0 (>5x) Effort: 1-2 weeks (medium) Decision: ✅ P0 - Implement immediately

---

Example 2: Pipeline Parallelism (P1)

Baseline: 952ms total (download → extract → parse → ingest) Optimization: Overlap download(month N+1) with ingest(month N) Estimated: Save 850ms per month (out of ~1900ms for 2 months serial) New Total: 1900ms → 1050ms (for 2 months) Improvement: 1900 / 1050 = 1.8x per month ← P1 (approaching 2x) Effort: 1 week (medium) Decision: ✅ P1 - Implement after P0

---

Example 3: Streaming ZIP Extraction (P2)

Baseline: 11ms extraction (disk I/O), 952ms total Optimization: In-memory extraction (eliminate 3 disk I/O operations) Estimated: 11ms → 2ms (5x faster extraction) New Total: 952 - 11 + 2 = 943ms Improvement: 952 / 943 = 1.01x ← Wait, this is P3!

Re-analysis: Actually, eliminates disk I/O overhead across entire pipeline, not just extraction phase. Real savings: ~50ms (extraction + CSV write/read overhead) New Total: 952 - 50 = 902ms Improvement: 952 / 902 = 1.06x ← Still P3

BUT: Effort is only 4-8 hours (very low) Adjusted Priority: ✅ P2 - Quick win (despite low impact, trivial effort)

---

Decision Tree

                        START
                          |
                Is improvement ≥5x?
                    /         \
                  YES          NO
                   |            |
                  P0        Is improvement ≥2x?
                           /         \
                         YES          NO
                          |            |
                         P1        Is improvement ≥1.2x?
                                  /         \
                                YES          NO
                                 |            |
                              Is effort       P3
                              <1 day?
                               /    \
                             YES     NO
                              |       |
                             P2      P3

                THEN: Adjust based on effort
                 - If effort >10 days AND improvement <10x → downgrade
                 - If effort <1 day AND improvement >1.1x → upgrade to P2

---

Common Mistakes

Mistake 1: Optimizing Non-Bottleneck

❌ Bad: "Let's optimize the database (4% of time) to get 2x improvement"

• Real improvement: 952ms → 932ms (952 - 20ms savings) = 1.02x (not 2x!)
• Lesson: 2x improvement on 4% of time = 0.02x overall improvement

✅ Good: Optimize the 90% bottleneck first

Mistake 2: Ignoring Effort

❌ Bad: "This 10x improvement is P0, even though it takes 6 months"

• Real cost: 6 months of engineering time
• Lesson: Consider ROI (return on investment)

✅ Good: Prioritize high-impact, reasonable-effort optimizations first

Mistake 3: Overestimating Parallelism

❌ Bad: "10 workers = 10x improvement"

• Real improvement: Amdahl's Law limits parallelism
• Serial overhead (10%) + parallel portion (90% / 10 workers) = 5.3x (not 10x)

✅ Good: Use Amdahl's Law to estimate realistic parallelism gains

---

Validation Checklist

Before assigning priority, verify:

• [ ] Profiling data is accurate (3+ runs, consistent results)
• [ ] Improvement calculation accounts for Amdahl's Law (if parallelizing)
• [ ] Effort estimate includes testing, documentation, code review
• [ ] Risk assessment considers backward compatibility, data integrity
• [ ] Priority assignment uses decision tree consistently
• [ ] Consensus across multiple investigation agents (for multi-agent workflows)

Skill: Multi-Agent Performance Profiling

Master Performance Optimization Integration Report

[Project Name] - [Feature/Refactor Name]

Date: YYYY-MM-DD Integration Agent: Complete Synthesis Status: ✅ Ready for Implementation

---

Executive Summary

Critical Discovery

[One-sentence statement of what IS or ISN'T the bottleneck - surprise finding]

Example: QuestDB ILP ingestion is NOT the bottleneck. The system achieves 1.1M rows/sec for pure database ingestion, which is 11x faster than the 100K rows/sec target.

The reported "[Current throughput]" pipeline throughput includes:

• X% [Phase Name] (Xms)
• Y% [Phase Name] (Yms)
• Z% [Phase Name] (Zms)

Key Findings from All Investigation Agents

1. Profiling Agent

• [Primary finding - what takes most time]
• [Quantified performance: Xms for Y rows = Z rows/sec]
• [CPU/memory observations]

2. [Database/Server] Config Agent

• [Current configuration assessment]
• [Production tuning available vs development]
• [Expected improvement percentage]

3. [Client/Library] Agent

• [API usage pattern assessment]
• [Optimization opportunities]
• [Configuration recommendations]

4. [Batch/Size/Algorithm] Agent

• [Current approach assessment]
• [Optimal parameters determination]
• [Tradeoff analysis]

5. Integration Agent (This Report)

• Primary bottleneck: [Phase Name] (Xms, Y% of time)
• Primary solution: [Recommendation] (Nx improvement)
• Secondary solution: [Recommendation] (Nx improvement)
• Quick win: [Recommendation] (Nx improvement)

---

Top 3 Recommendations (Consensus)

All investigation agents agree on these priorities:

1. [Optimization Name] (P0) - HIGHEST IMPACT

Impact: 🔴 Nx improvement Effort: High/Medium/Low (N days) Expected Improvement: [Current] → [Target] [metric]

Rationale:

• [Why this is the primary bottleneck]
• [Supporting evidence from profiling]
• [Why this is feasible/safe to implement]
• [Memory/complexity tradeoffs]

Implementation:

# Pseudocode or architecture description
from concurrent.futures import ThreadPoolExecutor

def optimized_approach():
    # Implementation details
    pass

Risks/Considerations:

• [Risk 1 and mitigation]
• [Risk 2 and mitigation]

---

2. [Optimization Name] (P1) - HIGH IMPACT

Impact: 🟠 Nx improvement Effort: High/Medium/Low (N days) Expected Improvement: [Current] → [Target] [metric]

Rationale:

• [Why this is secondary priority]
• [Supporting evidence]
• [Comparison to P0]

Implementation: [Description or pseudocode]

---

3. [Optimization Name] (P2) - QUICK WIN

Impact: 🟡 Nx improvement Effort: High/Medium/Low (N hours) Expected Improvement: [Current] → [Target] [metric]

Rationale:

• [Why this is a quick win]
• [Low effort, medium impact]
• [Can be done in parallel with P0/P1]

Implementation: [Description or pseudocode]

---

Agent Investigation Summary

Profiling Agent Findings

Location: tmp/perf-optimization/profiling/

Key Results:

• [Phase 1]: Xms (Y% of total)
• [Phase 2]: Xms (Y% of total)
• [Phase 3]: Xms (Y% of total)
• [Phase 4]: Xms (Y% of total)

Recommendation: [One-sentence summary of profiling agent's primary recommendation]

Evidence:

• Benchmark runs: [Number of iterations, consistency of results]
• Throughput: [Current rows/sec, target rows/sec]
• Memory usage: [Peak, average]

---

[Database/Server] Config Agent Findings

Location: tmp/perf-optimization/[config-type]/

Key Results:

• Current config: [Description of current settings]
• Production config: [Description of optimal settings]
• Expected improvement: <X% (worth it? yes/no)

Recommendation: [One-sentence summary]

Supporting Evidence:

• [Config parameter 1]: [Current value] → [Recommended value] = [Impact]
• [Config parameter 2]: [Current value] → [Recommended value] = [Impact]

---

[Client/Library] Agent Findings

Location: tmp/perf-optimization/[client-type]/

Key Results:

• Current API usage: [Description - e.g., "using dataframe() bulk ingestion"]
• Alternative approaches: [List of alternatives considered]
• Expected improvement: <X% (worth it? yes/no)

Recommendation: [One-sentence summary]

---

[Batch/Size/Algorithm] Agent Findings

Location: tmp/perf-optimization/[analysis-type]/

Key Results:

• Current batch size: [X rows/items]
• Optimal range: [Y-Z rows/items]
• Memory overhead: [MB per batch]
• Expected improvement: <X% (current is already optimal? yes/no)

Recommendation: [One-sentence summary]

---

Consensus Analysis

Areas of Agreement (All Agents)

1. Primary Bottleneck: [X/4 agents] agree that [Phase Name] is the bottleneck 2. Recommended Optimization: [X/4 agents] recommend [Optimization Name] 3. Expected Impact: Consensus on [Nx] improvement potential

Areas of Disagreement

[If any agents contradict each other, document here. Otherwise state "None - all agents in consensus"]

Example disagreement:

• Agent 2 recommends database config tuning (+50% improvement)
• Agent 1 profiling shows database is only 4% of time
• Resolution: Profiling agent's empirical evidence takes priority

---

Implementation Roadmap

Phase 1: P0 Optimizations (Week 1-2)

• [ ] Implement [P0 Optimization Name]
• [ ] Re-run profiling to verify [Nx] improvement achieved
• [ ] Update benchmarks and documentation

Success Criteria: Achieve [Target metric] or better

---

Phase 2: P1 Optimizations (Week 3-4)

• [ ] Implement [P1 Optimization Name]
• [ ] Re-run profiling to verify [Nx] improvement achieved
• [ ] Update benchmarks

Success Criteria: Achieve [Target metric] or better

---

Phase 3: P2 Quick Wins (As time permits)

• [ ] Implement [P2 Optimization Name]
• [ ] Re-run profiling to verify [Nx] improvement achieved

Success Criteria: Any measurable improvement (>1.2x)

---

Risk Assessment

---

Validation Plan

Before Optimization:

# Baseline profiling
uv run python tmp/perf-optimization/profiling/profile_pipeline.py
# Expected output: [Current metric]

After Each Optimization:

# Re-run profiling
uv run python tmp/perf-optimization/profiling/profile_pipeline.py
# Verify: [Expected metric after P0/P1/P2]

Acceptance Criteria:

• P0 implemented → Achieve [X metric] (Nx improvement)
• P1 implemented → Achieve [Y metric] (Nx improvement)
• P2 implemented → Achieve [Z metric] (Nx improvement)

---

Appendices

Appendix A: Full Profiling Results

[Link to or inline profiling data]

Appendix B: Configuration Recommendations

[Link to or inline config recommendations]

Appendix C: Alternative Approaches Considered

[List of approaches considered but rejected, with rationale]