Interactive Comparison

Language Benchmark

Compare Joule's energy efficiency against other programming languages. See equivalent code side-by-side and benchmark performance.

75x

Python consumes up to 75x more energy than C for equivalent computations

1.03x

Rust achieves energy efficiency within 3% of C while providing memory safety

1,000 TWh

Projected data center energy consumption by 2026 (IEA)

Execution Environment

Languages without WASM support will be disabled. Results vary by browser and device.

Select Benchmark

Recursive Fibonacci (n=30) - Tests CPU computation and function call overhead

Select Languages (6 selected)

Browser WASMBrowser RuntimeServer-SideEstimated

Systems

JVM/.NET

Mobile/Modern

Functional

Scripting

Scientific

WASM-First

JouleEnergy-Aware
// Joule - Energy-efficient Fibonacci
#[energy_budget(max_joules = 0.001)]
fn fib(n: i32) -> i32 {
    if n <= 1 { n }
    else { fib(n - 1) + fib(n - 2) }
}

fn main() -> i32 { fib(30) }
Rust
// Rust - Memory-safe Fibonacci
fn fib(n: i32) -> i32 {
    if n <= 1 { n }
    else { fib(n - 1) + fib(n - 2) }
}

fn main() { println!("{}", fib(30)); }
C
// C - Manual memory management
#include <stdio.h>

int fib(int n) {
    if (n <= 1) return n;
    return fib(n - 1) + fib(n - 2);
}

int main() { printf("%d\n", fib(30)); }
Python
# Python - Interpreted
def fib(n):
    if n <= 1: return n
    return fib(n - 1) + fib(n - 2)

print(fib(30))
Java
// Java - Enterprise standard
public class Fib {
    static int fib(int n) {
        if (n <= 1) return n;
        return fib(n - 1) + fib(n - 2);
    }
    public static void main(String[] args) {
        System.out.println(fib(30));
    }
}
Go
// Go - Garbage-collected
package main
import "fmt"

func fib(n int) int {
    if n <= 1 { return n }
    return fib(n-1) + fib(n-2)
}

func main() { fmt.Println(fib(30)) }

Research Background

The energy efficiency data is based on peer-reviewed research comparing programming languages across standardized benchmarks. Key findings:

  • Compiled languages (C, Rust, Joule) are significantly more energy-efficient than interpreted languages
  • Garbage collection adds 5-25% energy overhead depending on allocation patterns
  • Memory access patterns (cache efficiency) have ~100x impact on energy consumption
  • Joule's energy budgets enable compile-time verification of energy constraints

Citation: Pereira, R., et al. (2017). "Energy Efficiency across Programming Languages." Proceedings of the 10th ACM SIGPLAN International Conference on Software Language Engineering.