Benchmark: asmjs vs simple - MeasureThat.net

Script Preparation code:

var arr = [];
var size = 1024;
for(let i = 0; i < size; i++) {
  arr.push(Math.random());
}

var min = Math.min.apply(null, arr);
var max = Math.max.apply(null, arr);
var diff = max - min;

var normalize = (function(global, env, buffer) {
 "use asm";

 function _my_add(d1, d2, d3) {
  d1 = +d1;
  d2 = +d2;
  d3 = +d3;

  return (d1 - d2) / d3;
 }

 return {
  _normalize: _my_add
 };
})(this, null, null)._normalize;

Tests:

asmjs
for(let i = 0; i < size; i++) { const a = normalize(arr[i], min, diff); }
simple
for(let i = 0; i < size; i++) { const a = (arr[i] - min) / diff; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
asmjs
simple

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's dive into the provided benchmarking JSON.

**Benchmark Purpose and Options**

The primary goal of this benchmark is to compare the performance of two different approaches for normalizing an array of random numbers: the "asmjs" approach, which uses assembly-like instructions (ASMJS), and the "simple" approach, which performs a straightforward division operation.

There are two options being compared:

1. **ASMJS Approach**: This method uses a custom function `_my_add` to perform the normalization operation. The function takes three arguments: `d1`, `d2`, and `d3`. It first converts these values to integers using the unary plus operator (`+`) and then returns their difference divided by `d3`. The ASMJS instructions are used to optimize performance.
2. **Simple Approach**: This method uses a simple division operation to perform the normalization: `(arr[i] - min) / diff`.

**Pros and Cons of Each Approach**

1. **ASMJS Approach**:
	* Pros:
		+ Optimized for performance using assembly-like instructions (ASMJS).
		+ May offer better performance due to direct access to hardware resources.
	* Cons:
		+ Less readable and maintainable code, as it relies on low-level details of the ASMJS syntax.
		+ Requires careful tuning to optimize performance.
2. **Simple Approach**:
	* Pros:
		+ More readable and maintainable code, as it uses a familiar division operation.
		+ Easier to understand and debug.
	* Cons:
		+ May be slower due to the overhead of the JavaScript interpreter.

**Library Used**

The `normalize` function in the benchmark definition code uses an anonymous function expression with three arguments: `global`, `env`, and `buffer`. This is a way of creating a self-invoking function that has access to its own scope and can use variables from the surrounding context. The purpose of this library is to provide a flexible way to normalize the array values.

**Special JS Feature**

The benchmark definition code uses the `"use asm"` directive, which allows the use of assembly-like instructions in modern JavaScript engines (e.g., V8). This feature was introduced in ECMAScript 2015 (ES6) as part of the "asm.js" module. The `use asm` directive enables the use of ASMJS syntax and compilation to machine code.

**Other Alternatives**

If you need alternative approaches for normalizing arrays, you can consider:

1. Using a library like `lodash` or `ramda`, which provide a range of mathematical functions, including normalization.
2. Implementing a custom normalization function using standard JavaScript arithmetic operations (e.g., multiplication and division).
3. Using a different optimization technique, such as SIMD (Single Instruction, Multiple Data) instructions.

For the ASMJS approach, you could consider:

1. Using a library like `asm.js-optimizer` to optimize the performance of your code.
2. Implementing custom ASMJS instructions using a JavaScript engine that supports it (e.g., V8).

Related benchmarks:

asmjs vs simple (version: 0)

Comparing performance of: asmjs vs simple

Created: 5 years ago by: Guest

Jump to the latest result

asmjs

simple

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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