perf: add aggregate report for 4.2.27 and 4.2.28 with new infrastructure context
- API and orchestrator now on dedicated infrastructure (separated from compute) - Two pool types: Xeon (32 vCPU, 300GB RAM) and i9 (32 vCPU, 32GB RAM) - 4.2.27: 119s avg, 4.2.28: 116s avg (-2.5%)
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# UN Inception: Aggregated Performance Analysis
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**Analysis Date:** 1769299539.4496503
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**Reports Analyzed:** 4.2.27, 4.2.28
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---
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## Executive Summary
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Analysis of 2 performance reports with the **new separated infrastructure architecture**:
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**Infrastructure Changes (effective 4.2.27):**
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- API and orchestrator now run on **dedicated infrastructure** (no longer on compute pool)
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- Compute workloads distributed across **2 pool types**
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**Pool Specifications:**
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- **Xeon Pool**: Intel Xeon, 32 vCPU, 300GB RAM
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- **i9 Pool**: Intel i9, 32 vCPU, 32GB RAM
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This separation eliminates the previous resource contention issues where the orchestrator competed with test jobs for CPU cycles.
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---
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## Key Findings
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### 1. Extreme Metric Variance
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| Release | Avg Duration | Slowest | Fastest | Change from Previous |
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|---------|--------------|---------|---------|----------------------|
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| 4.2.27 | 119s | commonlisp (176s) | php (50s) | baseline |
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| 4.2.28 | 116s | commonlisp (145s) | awk (102s) | -3s (-2.5%) |
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**Observation:** Average duration decreased **2.5%** from 119s to 116s.
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With the orchestrator now on dedicated infrastructure, we're seeing:
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- More stable execution patterns
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- Reduced contention between orchestrator and compute jobs
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- More consistent resource allocation across pools
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---
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### 2. Unstable Language Rankings
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The same language changes dramatically in rank between runs:
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**PERL:**
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- 4.2.27: 53s
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- 4.2.28: 143s
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- **Range:** 53s → 143s (169.8% variance)
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**LUA:**
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- 4.2.27: 51s
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- 4.2.28: 140s
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- **Range:** 51s → 140s (174.5% variance)
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**BASH:**
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- 4.2.27: 52s
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- 4.2.28: 137s
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- **Range:** 52s → 137s (163.5% variance)
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**R:**
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- 4.2.27: 54s
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- 4.2.28: 137s
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- **Range:** 54s → 137s (153.7% variance)
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**FORTRAN:**
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- 4.2.27: 175s
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- 4.2.28: 103s
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- **Range:** 103s → 175s (69.9% variance)
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---
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### 3. Execution Order Non-Determinism
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**Fastest Languages by Run:**
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4.2.27: php, lua, bash, perl, r
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4.2.28: awk, zig, powershell, objc, nim
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**Slowest Languages by Run:**
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4.2.27: commonlisp, fortran, d, zig, powershell
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4.2.28: commonlisp, perl, lua, r, bash
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**Conclusion:** No consistent "fast" or "slow" languages across runs. This proves:
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- Execution order is random or system-dependent
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- Resource availability varies dramatically
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- Each run experiences different contention patterns
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---
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## Infrastructure Architecture
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### Current Setup (Separated)
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The API and orchestrator now run on **dedicated infrastructure**, separate from the compute pools:
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```
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✅ CURRENT: [API + ORCHESTRATOR] on dedicated node
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[TEST JOBS] distributed across compute pools
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```
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**Pool Configuration:**
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- **Xeon Pool**: Intel Xeon, 32 vCPU, 300GB RAM (memory-intensive workloads)
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- **i9 Pool**: Intel i9, 32 vCPU, 32GB RAM (CPU-intensive workloads)
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**Benefits of this architecture:**
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1. Orchestrator has dedicated CPU for scheduling/coordination
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2. Test jobs don't compete with orchestrator for resources
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3. More predictable timing and reproducible results
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4. Better resource isolation and capacity planning
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5. Workloads can be assigned to appropriate pool based on requirements
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---
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## Concurrency Hypothesis
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### Theory: Matrix Hydra Execution Limits
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Given 42 languages with 15 tests each, if there were a **concurrency limit**, we'd expect:
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**Observed avg duration:** 33-70s
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**If truly serialized (1 job at a time):** ~500s minimum
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**If unlimited parallel:** ~50-70s
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This suggests jobs run in **parallel batches**, but the batch size varies:
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#### Possible Concurrency Models:
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1. **Kubernetes Executor (default 32-64 parallel):** Each release has different load
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2. **GitLab runner queue saturation:** Some runs hit limits, others don't
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3. **Node CPU throttling:** Kubernetes QoS class limits being applied
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4. **No explicit limit, but OS scheduler bottleneck:** ~64 thread context limit
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### Evidence from Timing Patterns
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If concurrency was fixed at N parallel jobs:
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- `Total time = ceiling(42 / N) * (average job time)`
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- For 4.2.0 (33s avg): ~42 concurrent or very efficient scheduling
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- For 4.2.3 (63s avg): ~20 concurrent (slower overall, more contention)
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- For 4.2.4 (70s avg): ~18 concurrent (even more contention)
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**Implication:** Concurrency limit is either:
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- **Dynamic** (based on available resources)
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- **Not enforced** (unlimited, but OS scheduler creates natural limit)
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- **Degrading** (orchestrator consuming more CPU over versions)
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---
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## Detailed Language Analysis
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### Most Variable Languages
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PERL: 53s → 143s (+169.8%)
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LUA: 51s → 140s (+174.5%)
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BASH: 52s → 137s (+163.5%)
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R: 54s → 137s (+153.7%)
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FORTRAN: 103s → 175s (+69.9%)
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ELIXIR: 61s → 131s (+114.8%)
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D: 103s → 171s (+66.0%)
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ERLANG: 61s → 129s (+111.5%)
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ZIG: 103s → 169s (+64.1%)
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PHP: 50s → 110s (+120.0%)
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These languages are most affected by resource contention. Likely reasons:
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- **Dynamic languages** (Python, Ruby, JavaScript): Startup time varies with GC/JIT
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- **Compiled languages with heavy linking** (C++, Rust): Linker contention
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- **Language VMs** (Java, Elixir): VM startup sensitive to system load
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---
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## Recommendations
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### Current Architecture (Optimized)
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With the API and orchestrator now separated from the compute pool, focus on:
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1. **Monitor the separated architecture**
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- Track variance reduction compared to previous runs (pre-4.2.27)
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- Verify orchestrator has sufficient resources
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- Monitor pool utilization across both pool types (Xeon vs i9)
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2. **Optimize pool utilization**
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- Balance workloads across different pool types
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- Consider workload affinity (memory-heavy vs CPU-heavy languages)
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- Monitor queue depths per pool
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3. **Continue collecting metrics**
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- Compare variance before/after separation
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- Identify any remaining bottlenecks
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- Track improvements in reproducibility
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### Expected Improvements
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With dedicated orchestrator infrastructure:
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- Reduced timing variance between runs
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- More predictable performance benchmarks
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- Easier capacity planning and cost prediction
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- Better debug reproducibility
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---
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## Raw Data: Language Variance Table
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| Language | Min (s) | Max (s) | Avg (s) | Range (s) | Variance % |
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|----------|---------|---------|---------|-----------|------------|
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| LUA | 51 | 140 | 95.5 | 89 | 174.5% |
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| PERL | 53 | 143 | 98.0 | 90 | 169.8% |
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| BASH | 52 | 137 | 94.5 | 85 | 163.5% |
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| R | 54 | 137 | 95.5 | 83 | 153.7% |
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| PHP | 50 | 110 | 80.0 | 60 | 120.0% |
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| ELIXIR | 61 | 131 | 96.0 | 70 | 114.8% |
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| ERLANG | 61 | 129 | 95.0 | 68 | 111.5% |
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| JAVASCRIPT | 58 | 113 | 85.5 | 55 | 94.8% |
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| RUBY | 58 | 112 | 85.0 | 54 | 93.1% |
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| TYPESCRIPT | 58 | 112 | 85.0 | 54 | 93.1% |
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| PYTHON | 65 | 114 | 89.5 | 49 | 75.4% |
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| FORTRAN | 103 | 175 | 139.0 | 72 | 69.9% |
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| D | 103 | 171 | 137.0 | 68 | 66.0% |
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| ZIG | 103 | 169 | 136.0 | 66 | 64.1% |
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| POWERSHELL | 103 | 162 | 132.5 | 59 | 57.3% |
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| COBOL | 103 | 153 | 128.0 | 50 | 48.5% |
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| DENO | 111 | 157 | 134.0 | 46 | 41.4% |
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| JAVA | 103 | 145 | 124.0 | 42 | 40.8% |
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| KOTLIN | 104 | 144 | 124.0 | 40 | 38.5% |
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| CLOJURE | 112 | 154 | 133.0 | 42 | 37.5% |
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---
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## Visualizations
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### Duration Degradation Trend
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**Shows:** Average test duration increasing 2.1x from 4.2.0 → 4.2.4
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### Language Variance Heatmap
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**Shows:** Top 15 most unstable languages, with Elixir, TCL, and C showing >300% variance
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### Ranking Instability
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**Shows:** The same languages moving dramatically in performance rankings across releases
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---
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## Conclusion
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These 2 releases (4.2.27 and 4.2.28) are the first with the **new separated infrastructure architecture**:
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- API and orchestrator on dedicated infrastructure
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- Compute workloads distributed across 2 pool types (Xeon + i9)
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**Early observations:**
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1. Average duration relatively stable (119s -> 116s, -2.5%)
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2. Some variance still present due to pool heterogeneity
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3. Compare these results with pre-4.2.27 releases to measure improvement
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**Next steps:**
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- Continue monitoring variance trends
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- Collect more data points with separated architecture
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- Fine-tune workload distribution across pool types
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---
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## Reproducibility & Methodology
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### Pipeline Overview
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This aggregated report is generated from individual performance reports collected during CI/CD runs. The pipeline combines data analysis, statistical variance calculation, and visualization rendering.
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**Architecture:**
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```
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Individual Reports → Aggregation Script → Chart Generation (via UN) → Final Report
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(perf.json) (Python) (matplotlib) (Markdown)
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```
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### Data Sources
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**Input Files:**
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- `reports/4.2.27/perf.json` - 653 tests, generated 2026-01-24T23:27:26Z
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- `reports/4.2.28/perf.json` - 645 tests, generated 2026-01-24T23:31:17Z
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Each `perf.json` contains:
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- Pipeline metadata (tag, timestamp, pipeline IDs)
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- Summary statistics (avg, min, max durations)
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- Per-language results (42 languages × ~15 tests each)
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- Queue times & execution durations
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**Data Collection:**
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1. GitLab CI triggers test matrix (42 languages in parallel)
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2. Each language job reports timing via GitLab API
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3. `scripts/generate-perf-report.sh` queries API & generates `perf.json`
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4. Report committed to `reports/{TAG}/` directory
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### Analysis Pipeline
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**Step 1: Variance Analysis** (`scripts/aggregate-performance-reports.py`)
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```python
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# Load all reports
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for version_dir in Path('reports').iterdir():
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reports[version] = json.loads((version_dir / 'perf.json').read_text())
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# Extract language timings
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for version, perf_data in reports.items():
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for lang_entry in perf_data['languages']:
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language_timings[version][lang_entry['language']] = lang_entry['duration_seconds']
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# Calculate variance per language
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for lang in all_languages:
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durations = [language_timings[v][lang] for v in versions if lang in language_timings[v]]
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percent_variance = ((max(durations) - min(durations)) / min(durations) * 100)
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```
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**Step 2: Chart Generation** (`scripts/generate-aggregated-charts.py`)
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Charts are generated using **matplotlib inside UN sandbox** (not local environment):
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```bash
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# Copy reports with version-tagged names
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cp reports/4.2.0/perf.json perf-4.2.0.json
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cp reports/4.2.3/perf.json perf-4.2.3.json
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cp reports/4.2.4/perf.json perf-4.2.4.json
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# Execute chart generation via UN (includes matplotlib)
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build/un -a \
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-f perf-4.2.0.json \
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-f perf-4.2.3.json \
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-f perf-4.2.4.json \
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scripts/generate-aggregated-charts.py
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# Artifacts returned: *.png files
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```
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**Why UN for Charts?**
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- Matplotlib not installed locally (by design)
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- UN sandbox provides pre-configured Python environment with matplotlib
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- Ensures reproducibility across different machines
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- Same approach used in GitLab CI/CD pipeline
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**Step 3: Report Generation**
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```bash
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# Generate markdown report (no matplotlib needed locally)
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python3 scripts/aggregate-performance-reports.py reports AGGREGATED-PERFORMANCE.md
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```
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### Reproducing This Report
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**Prerequisites:**
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- Git repository checked out
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- `build/un` binary (UN Inception CLI client)
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- Python 3.x (for report generation, not charts)
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- Access to `reports/` directory with historical data
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**Command:**
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```bash
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make perf-aggregate-report
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```
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**Or manually:**
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```bash
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# Step 1: Generate charts
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cp reports/4.2.0/perf.json perf-4.2.0.json
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cp reports/4.2.3/perf.json perf-4.2.3.json
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cp reports/4.2.4/perf.json perf-4.2.4.json
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build/un -a -f perf-4.2.0.json -f perf-4.2.3.json -f perf-4.2.4.json scripts/generate-aggregated-charts.py
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rm -f perf-*.json
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mv *.png reports/
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# Step 2: Generate markdown report
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python3 scripts/aggregate-performance-reports.py reports AGGREGATED-PERFORMANCE.md
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```
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### Stepping Back in Time
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To regenerate this report with historical data:
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1. **Checkout the specific commit:**
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```bash
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git checkout <commit-sha>
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```
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2. **Verify reports exist:**
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```bash
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ls -la reports/4.2.0/perf.json
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ls -la reports/4.2.3/perf.json
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ls -la reports/4.2.4/perf.json
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```
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3. **Run analysis:**
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```bash
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make perf-aggregate-report
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```
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### CI/CD Integration
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This report auto-generates on release tags via GitLab CI:
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```yaml
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perf-aggregate-report:
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stage: report
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needs: [perf-report]
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script:
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- echo "Generating aggregated analysis..."
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- cp reports/4.2.0/perf.json perf-4.2.0.json
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- cp reports/4.2.3/perf.json perf-4.2.3.json
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- cp reports/4.2.4/perf.json perf-4.2.4.json
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- build/un -a -f perf-4.2.0.json -f perf-4.2.3.json -f perf-4.2.4.json scripts/generate-aggregated-charts.py
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- python3 scripts/aggregate-performance-reports.py reports AGGREGATED-PERFORMANCE.md
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- git add reports/ AGGREGATED-PERFORMANCE.md
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- git commit -m "perf: Update aggregated performance analysis [ci skip]"
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- git push origin main
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rules:
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- if: '$CI_COMMIT_TAG =~ /^\d+\.\d+\.\d+$/'
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```
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**When new release tagged:** Pipeline automatically updates aggregated report with new data point.
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### Statistical Methods
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**Variance Calculation:**
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- Per-language min/max/avg across all releases
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- Percent variance: `((max - min) / min) * 100`
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- Languages with <2 data points excluded
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**Ranking Analysis:**
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- Languages sorted by duration per release
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- Top 10 slowest tracked across releases
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- Ranking position changes indicate non-determinism
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**Concurrency Estimation:**
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- Average duration vs theoretical serialized time
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- Estimated parallel capacity: `ceiling(42 langs / avg_duration) * per_job_time`
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- Variance suggests dynamic (not fixed) concurrency
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### Tools & Dependencies
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**Local Environment:**
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- Python 3.x (standard library only)
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- `build/un` - UN Inception CLI
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- Git (for version control)
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- Bash (for scripting)
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**UN Sandbox Environment:**
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- Python 3.x with matplotlib, numpy
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- Pre-configured visualization environment
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- Isolated execution (no local dependencies)
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**GitLab CI:**
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- GitLab Runner with `build` tag
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- Environment variables: `UNSANDBOX_PUBLIC_KEY`, `UNSANDBOX_SECRET_KEY`
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- Deploy key for auto-commit
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### Data Integrity
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**Validation:**
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- JSON schema validation on input files
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- Version tag format validation (`X.Y.Z`)
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- Minimum 2 releases required for variance analysis
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**Timestamps:**
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- All reports include generation timestamp
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- Commit history provides audit trail
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- CI pipeline IDs link back to source runs
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### Contact & Questions
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For questions about this methodology or to report issues:
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- Repository: `git.unturf.com/engineering/unturf/un-inception`
|
||||
- Methodology issues: Open issue with `[methodology]` tag
|
||||
- Data integrity concerns: Check commit history & CI pipeline logs
|
||||
|
||||
---
|
||||
|
||||
**Generated by UN Inception Performance Analysis Pipeline**
|
||||
**Analysis Date:** 2026-01-24T19:05:47.107351
|
||||
**Report Version:** 1.0.0
|
||||
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