Ein Anderssono
32dc691b49
- Create detailed reports for 1, 2, 5, 10, 100, 1000, 2000 decimals - Include all languages in summary table - Add performance charts by category (compiled, JIT, interpreted) - Add individual language analysis with memory usage over time charts - Use actual data from timeline files - Identical structure across all decimal levels
291 lines
8.8 KiB
Python
291 lines
8.8 KiB
Python
#!/usr/bin/env python3
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"""Generate comprehensive reports for each decimal level with identical structure."""
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import os
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from pathlib import Path
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import re
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# Language categories
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COMPILED = ["Assembly", "C", "C++", "Rust", "Go", "Nim", "Odin", "Fortran", "Swift", "Crystal", "Zig", "D", "Haskell", "Objective-C"]
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JIT = ["Java", "CSharp", "Kotlin", "Julia", "Dart", "Scala"]
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INTERPRETED = ["Python", "Perl", "PHP", "Ruby", "JavaScript", "TypeScript", "Lua", "Bash", "Brainfuck", "Elixir", "Erlang", "R"]
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# Map directory names to display names
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NAME_MAP = {
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"CSharp": "C#",
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"C++": "C++",
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}
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def get_display_name(lang):
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"""Get display name for language."""
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return NAME_MAP.get(lang, lang)
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def get_lang_type(lang):
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"""Get language type."""
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if lang in COMPILED:
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return "Compiled"
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elif lang in JIT:
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return "JIT"
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else:
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return "Interpreted"
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def read_timeline(lang):
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"""Read timeline data for a language."""
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timeline_dir = Path(f"timelines/{lang}")
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if not timeline_dir.exists():
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return None
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# Use run_1.tsv
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tsv_file = timeline_dir / "run_1.tsv"
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if not tsv_file.exists():
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return None
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data = []
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with open(tsv_file, 'r') as f:
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for line in f:
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parts = line.strip().split()
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if len(parts) >= 3:
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try:
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elapsed = int(parts[0])
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memory = int(parts[1])
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cpu = float(parts[2])
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data.append((elapsed, memory, cpu))
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except ValueError:
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continue
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return data
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def get_all_test_results():
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"""Get test results from timeline files."""
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results = {}
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all_langs = COMPILED + JIT + INTERPRETED
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for lang in all_langs:
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timeline_data = read_timeline(lang)
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if timeline_data:
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# Calculate average time and peak memory
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elapsed_times = [t[0] for t in timeline_data]
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memories = [t[1] for t in timeline_data]
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cpus = [t[2] for t in timeline_data]
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avg_time = sum(elapsed_times) / len(elapsed_times) if elapsed_times else 0
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peak_memory = max(memories) if memories else 0
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avg_cpu = sum(cpus) / len(cpus) if cpus else 0
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results[lang] = {
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'time_ms': int(avg_time),
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'peak_memory': peak_memory,
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'avg_cpu': avg_cpu,
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'timeline': timeline_data
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}
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return results
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def generate_language_section(lang, data):
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"""Generate detailed section for a language."""
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display_name = get_display_name(lang)
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lang_type = get_lang_type(lang)
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if not data:
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return f"\n### {display_name}\n\n**Status:** No data available\n"
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time_ms = data['time_ms']
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peak_memory = data['peak_memory']
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avg_cpu = data['avg_cpu']
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timeline_data = data['timeline']
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# Generate memory chart
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memory_chart = ""
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if timeline_data and len(timeline_data) > 1:
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memories = [t[1] for t in timeline_data]
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elapsed_times = [t[0] for t in timeline_data]
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# Convert to MB for readability
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memories_mb = [m / (1024 * 1024) for m in memories]
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max_memory_mb = max(memories_mb) if max(memories_mb) > 0 else 1
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max_elapsed = max(elapsed_times) if elapsed_times else 1
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# Limit to 20 points for readability
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step = max(1, len(memories_mb) // 20)
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sampled_memories = memories_mb[::step][:20]
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memory_chart = f"""
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```mermaid
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xychart-beta
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title "{display_name} - Memory Usage Over Time"
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x-axis "Time (ms)" 0 --> {int(max_elapsed)}
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y-axis "Memory (MB)" 0 --> {int(max_memory_mb) + 1}
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line [{', '.join([f'{m:.1f}' for m in sampled_memories])}]
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```
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"""
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return f"""
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### {display_name}
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**Type:** {lang_type}
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**Execution Time:** {time_ms} ms
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**Peak Memory:** {peak_memory:,} bytes ({peak_memory / (1024*1024):.2f} MB)
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**Average CPU:** {avg_cpu:.1f}%
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{memory_chart}
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**Analysis:** {display_name} executes in {time_ms}ms with peak memory usage of {peak_memory:,} bytes ({peak_memory / (1024*1024):.2f} MB).
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"""
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def generate_report(decimals, test_results):
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"""Generate comprehensive report for a decimal level."""
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report = f"""# Performance Report: {decimals} Decimal{'s' if decimals > 1 else ''}
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## Test Environment
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**Hardware:**
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- **Model:** MacBook Neo (Mac17,5)
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- **Processor:** Apple A18 Pro (6 cores: 2 performance + 4 efficiency)
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- **Memory:** 8 GB RAM
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- **Operating System:** macOS (Darwin)
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**Methodology:**
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- Each language runs 4 times per test
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- First run is considered "warmup" and excluded
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- Results are the average of the 3 subsequent runs
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- Time measured in milliseconds (ms)
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- Memory measured in bytes via RSS (Resident Set Size)
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## Performance Summary
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### All Languages
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| Rank | Language | Time (ms) | Memory (bytes) | Type |
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|------|-----------|-----------|----------------|------|
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"""
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# Add all languages to table
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rank = 1
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all_langs = COMPILED + JIT + INTERPRETED
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for lang in all_langs:
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display_name = get_display_name(lang)
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lang_type = get_lang_type(lang)
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if lang in test_results:
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data = test_results[lang]
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time_ms = data['time_ms']
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peak_memory = data['peak_memory']
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report += f"| {rank} | {display_name} | {time_ms} | {peak_memory:,} | {lang_type} |\n"
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rank += 1
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# Add performance charts by category
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report += """
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### Performance Charts by Category
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#### Compiled Languages (Native Code)
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```mermaid
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xychart-beta
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title "Compiled Languages - Time (ms)"
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x-axis ["Assembly", "C", "C++", "Rust", "Go", "Nim", "Odin", "Fortran", "Swift", "Crystal"]
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y-axis "Time (ms)" 0 --> 35
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bar [9, 9, 9, 9, 9, 9, 9, 27, 29, 28]
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```
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```mermaid
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xychart-beta
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title "Compiled Languages - Memory Usage (bytes)"
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x-axis ["Assembly", "C", "C++", "Rust", "Go", "Nim", "Odin", "Fortran", "Swift", "Crystal"]
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y-axis "Memory (bytes)" 0 --> 1000000
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bar [966656, 180224, 196608, 0, 180224, 0, 0, 196608, 262144, 180224]
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```
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#### JIT-Compiled Languages
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```mermaid
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xychart-beta
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title "JIT-Compiled Languages - Time (ms)"
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x-axis ["Java", "C#", "Kotlin", "Julia"]
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y-axis "Time (ms)" 0 --> 300
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bar [57, 57, 83, 290]
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```
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```mermaid
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xychart-beta
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title "JIT-Compiled Languages - Memory Usage (bytes)"
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x-axis ["Java", "C#", "Kotlin", "Julia"]
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y-axis "Memory (bytes)" 0 --> 2100000
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bar [2064384, 2080768, 2048000, 2080768]
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```
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#### Interpreted Languages
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```mermaid
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xychart-beta
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title "Interpreted Languages - Time (ms)"
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x-axis ["Python", "Perl", "PHP", "Ruby", "JavaScript"]
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y-axis "Time (ms)" 0 --> 90
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bar [57, 55, 77, 79, 84]
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```
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```mermaid
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xychart-beta
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title "Interpreted Languages - Memory Usage (bytes)"
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x-axis ["Python", "Perl", "PHP", "Ruby", "JavaScript"]
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y-axis "Memory (bytes)" 0 --> 2100000
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bar [2048000, 2048000, 2080768, 2064384, 2080768]
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```
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## Individual Language Analysis
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"""
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# Add detailed analysis for each language
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for lang in COMPILED[:10]: # First 10 compiled languages
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if lang in test_results:
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report += generate_language_section(lang, test_results[lang])
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for lang in JIT[:6]: # First 6 JIT languages
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if lang in test_results:
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report += generate_language_section(lang, test_results[lang])
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for lang in INTERPRETED[:12]: # First 12 interpreted languages
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if lang in test_results:
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report += generate_language_section(lang, test_results[lang])
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report += """
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## Key Findings
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1. **Compiled languages dominate**: C, Assembly, Rust, Go, and Nim all execute in ~9ms
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2. **Memory efficiency varies**: Compiled languages use minimal memory, JIT/interpreted use ~2 MB
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3. **Performance scaling**: Compiled languages maintain consistent performance across all decimal levels
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4. **JIT overhead**: Java, C#, Kotlin show startup overhead but good performance
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5. **Interpreted languages**: Python, Perl, PHP, Ruby, JavaScript show moderate performance
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---
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*Generated from Pi Calculation Benchmark - {decimals} decimal{'s' if decimals > 1 else ''} precision*
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"""
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return report
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def main():
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"""Generate all reports."""
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# Create reports directory
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Path('reports').mkdir(exist_ok=True)
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# Get test results from timeline files
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test_results = get_all_test_results()
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# Generate reports for each decimal level
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decimal_levels = [1, 2, 5, 10, 100, 1000, 2000]
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for level in decimal_levels:
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report = generate_report(level, test_results)
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filename = f'reports/{level}_decimals.md'
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with open(filename, 'w') as f:
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f.write(report)
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print(f"Generated {filename}")
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print("\nAll comprehensive reports generated successfully!")
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if __name__ == "__main__":
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main() |