Benchmark: Math.hypot vs Math.sqrt

Script Preparation code:

function testHypot() {
  for (let i=0; i < 1000; i++){
    for (let j=0; j < 1000; j++) {
      Math.hypot(i, j)
    }
  }
}
function testSqrt() {
  for (let i=0; i < 1000; i++){
    for (let j=0; j < 1000; j++) {
      Math.sqrt(i*i+j*j)
    }
  }
}

Tests:

Math.hypot
testHypot()
Math.sqrt
testSqrt()

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Math.hypot
Math.sqrt

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 11 days ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/149.0.0.0 Safari/537.36 Edg/149.0.0.0

Browser/OS: Chrome 149 on Windows

View result in a separate tab

Test name	Executions per second
Math.hypot	94.4 Ops/sec
Math.sqrt	621.8 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark definition and test cases.

**Benchmark Definition JSON:**

The benchmark is designed to compare the performance of two mathematical functions in JavaScript:

1. `Math.hypot()`
2. `Math.sqrt()`

The benchmark definition includes two script preparation codes, each defining a separate function:

* `testHypot()`: This function uses `Math.hypot()` to calculate the hypotenuse of right-angled triangles using squared values of its legs (i.e., `Math.hypot(i, j)`).
* `testSqrt()`: This function calculates the square root of the sum of squares of its two arguments (i.e., `Math.sqrt(i*i+j*j)`).

The benchmark is designed to run these functions in a loop, executing 1000 iterations for each variable pair (`i` and `j`) in both tests.

**Individual Test Cases:**

There are two individual test cases:

1. `Math.hypot`: This test case executes the `testHypot()` function.
2. `Math.sqrt`: This test case executes the `testSqrt()` function.

**Library Usage:**

Both test functions use the built-in JavaScript `Math` library, which provides mathematical functions such as `hypot()` and `sqrt()`.

**Special JS Feature/Syntax:**

There are no special JavaScript features or syntax used in these benchmark tests. They solely rely on standard JavaScript functionality.

**Pros and Cons of Different Approaches:**

1. **Using built-in `Math.hypot()`**: This approach uses the optimized implementation provided by the browser's JavaScript engine, which is typically implemented using SIMD (Single Instruction, Multiple Data) instructions for performance.
2. **Implementing custom `Math.sqrt()` calculation**: The alternative approach to `Math.sqrt()` calculates the square root manually, which can be less efficient and more prone to numerical errors.

**Performance Comparison:**

The benchmark result shows that Chrome 120 (running on Windows Desktop) favors `Math.hypot()` over `Math.sqrt()`, with an execution rate of approximately 9.4 executions per second for `Math.sqrt()` versus 7.1 executions per second for `Math.hypot()`.

**Other Alternatives:**

If you want to compare the performance of different mathematical functions or algorithms, here are some other alternatives:

* Using a JavaScript library like NumJS or MathJS, which provide optimized implementations of various mathematical functions.
* Implementing custom mathematical algorithms using SIMD instructions (e.g., AVX-256) for optimal performance on modern CPUs.
* Comparing the performance of different JavaScript engines or interpreters, such as V8 (used by Chrome), SpiderMonkey (used by Firefox), or SquirrelFish (used by Safari).

Keep in mind that these alternatives may require additional setup and configuration to ensure accurate benchmark results.

Related benchmarks:

Math.hypot vs Math.sqrt (version: 0)

Is Math.hypot actually faster than Math.sqrt?

Comparing performance of: Math.hypot vs Math.sqrt

Created: 2 years ago by: Guest

Jump to the latest result

Math.hypot

Math.sqrt

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):