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
Pi Calculation Benchmark: Performance Comparison of 34 Programming Languages
Overview
This study compares the performance of 34 programming languages when calculating π (pi) with high precision. The benchmark uses Machin's formula and measures execution time across multiple decimal precision levels.
Test Environment
Hardware:
- Model: MacBook Neo (Mac17,5)
- Processor: Apple A18 Pro
- 6 cores: 2 performance cores + 4 efficiency cores
- Architecture: ARM64
- Memory: 8 GB RAM
- Operating System: macOS (Darwin)
Methodology:
- Each language runs 4 times per test
- First run is considered "warmup" and excluded
- Results are the average of the 3 subsequent runs
- Time measured in milliseconds (ms)
- Memory measured in bytes via RSS (Resident Set Size)
Method: Machin's Formula
All implementations use Machin's formula for π calculation:
π/4 = 4·arctan(1/5) - arctan(1/239)
Where arctan(x) is calculated using the Taylor series:
arctan(x) = x - x³/3 + x⁵/5 - x⁷/7 + ...
Advantages of this method:
- Fast convergence (few terms required)
- Simple implementation
- High precision possible
- Only integer arithmetic required
Performance Reports
Detailed performance reports are available for each decimal precision level:
- 1 Decimal - Minimal precision
- 2 Decimals - Low precision
- 5 Decimals - Medium precision
- 10 Decimals - Standard precision
- 100 Decimals - High precision
- 1000 Decimals - Very high precision
- 2000 Decimals - Extreme precision
Summary Results (100 Decimals)
All Languages Performance
| Rank | Language | Time (ms) | Memory (bytes) | Type |
|---|---|---|---|---|
| 1 | Assembly | 9 | 966,656 | Compiled |
| 1 | C | 9 | 180,224 | Compiled |
| 1 | C++ | 9 | 196,608 | Compiled |
| 1 | Rust | 9 | 0 | Compiled |
| 1 | Go | 9 | 180,224 | Compiled |
| 1 | Nim | 9 | 0 | Compiled |
| 1 | Odin | 9 | 0 | Compiled |
| 1 | Haskell | 9 | 0 | Compiled |
| 9 | Fortran | 27 | 196,608 | Compiled |
| 10 | Crystal | 28 | 180,224 | Compiled |
| 11 | Lua | 29 | 0 | Interpreted |
| 12 | Swift | 29 | 262,144 | Compiled |
| 13 | Bash | 32 | 2,000,000 | Interpreted |
| 14 | Zig | 33 | 2,730,000 | Compiled |
| 15 | Objective-C | 31 | 752,000 | Compiled |
| 16 | Dart | 31 | 11,749,000 | JIT |
| 17 | D | 35 | 10,154,000 | Compiled |
| 18 | Perl | 55 | 2,000,000 | Interpreted |
| 19 | Python | 57 | 2,000,000 | Interpreted |
| 19 | Java | 57 | 2,000,000 | JIT |
| 19 | C# | 57 | 2,016,000 | JIT |
| 22 | Scala | 58 | 2,000,000 | JIT |
| 23 | Brainfuck | 54 | 2,005,000 | Interpreted |
| 24 | PHP | 77 | 2,032,000 | Interpreted |
| 25 | Ruby | 79 | 2,000,000 | Interpreted |
| 26 | Kotlin | 83 | 2,032,000 | JIT |
| 27 | JavaScript | 84 | 2,032,000 | Interpreted |
| 28 | Erlang | 130 | 2,037,000 | Interpreted |
| 29 | TypeScript | 154 | 2,032,000 | Interpreted |
| 30 | R | 349 | 2,032,000 | Interpreted |
| 31 | Julia | 290 | 2,032,000 | JIT |
| 32 | Elixir | 898 | 2,037,000 | Interpreted |
Language Categories
Compiled Languages (Native Code):
- Fastest execution (9-35 ms)
- Minimal memory usage (0-966,656 bytes)
- Consistent performance across decimal levels
JIT-Compiled Languages:
- Moderate execution time (31-290 ms)
- Higher memory usage (~2 MB)
- Good performance after warmup
Interpreted Languages:
- Variable execution time (29-898 ms)
- Moderate memory usage (~2 MB)
- Performance varies widely
Key Findings
- Compiled languages dominate: C, Assembly, Rust, Go, and Nim all execute in ~9ms
- Memory efficiency varies: Compiled languages use minimal memory, JIT/interpreted use ~2 MB
- Performance scaling: Compiled languages maintain consistent performance across all decimal levels
- JIT overhead: Java, C#, Kotlin show startup overhead but good performance
- Interpreted languages: Python, Perl, PHP, Ruby, JavaScript show moderate performance
Languages Tested
Compiled (10): Assembly, C, C++, Rust, Go, Nim, Odin, Fortran, Swift, Crystal
JIT-Compiled (4): Java, C#, Kotlin, Julia
Interpreted (5): Python, Perl, PHP, Ruby, JavaScript
Other (15): Bash, Brainfuck, D, Dart, Elixir, Erlang, Haskell, Lua, Objective-C, R, Scala, TypeScript, Vimscript, Wolfram, Zig
Repository Structure
.
├── README.md # This file
├── reports/ # Detailed performance reports
│ ├── summary.md # Overall summary
│ ├── 1_decimals.md # 1 decimal precision
│ ├── 2_decimals.md # 2 decimals precision
│ ├── 5_decimals.md # 5 decimals precision
│ ├── 10_decimals.md # 10 decimals precision
│ ├── 100_decimals.md # 100 decimals precision
│ ├── 1000_decimals.md # 1000 decimals precision
│ └── 2000_decimals.md # 2000 decimals precision
├── timelines/ # Resource usage timeline data
├── assembly/ # Assembly implementation
├── c/ # C implementation
├── cpp/ # C++ implementation
├── rust/ # Rust implementation
└── ... # Other language implementations
Running the Benchmark
# Build all languages
./build.sh
# Run all tests
./run_all.sh
# Run specific language
cd c && ./build.sh && ./print_hej
License
MIT License - See LICENSE file for details.
Generated from Pi Calculation Benchmark - Apple A18 Pro Performance Study