Benchmark: Maping numeric vs f32 vs f64 with add

Maping numeric vs f32 vs f64 with add (version: 0)

compare the map speed of an array of random true/false values compared to an ArrayBuffer with a uInt8 view of 0 or 1. The results are that the typed array is much much faster. There are two possible reasons I can think of for this. 1) JavaScript boolean type size of 4Bytes where as an element of a uInt8 array is actually 1Byte. 2) The ArrayBuffer is allocating contiguous memory resulting in a better cache hit ratio.

Comparing performance of: numeric array vs Float64Array vs Float32Array

Created: one year ago by: Guest

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Script Preparation code:

const size = 1024;

var buffer64 = new ArrayBuffer(size*8);
var buffer32 = new ArrayBuffer(size*4);
var f64View = new Float32Array(buffer64, 0, size);
var f32View = new Float32Array(buffer32, 0, size);

var numericArray = [];
for (let i=0; i<size; i++) {
  const someVal = Math.random();
  numericArray[i] = someVal;
  f64View[i] = someVal;
  f32View[i] = someVal;
}

​x
 
const size = 1024;​var buffer64 = new ArrayBuffer(size*8);var buffer32 = new ArrayBuffer(size*4);var f64View = new Float32Array(buffer64, 0, size);var f32View = new Float32Array(buffer32, 0, size);​var numericArray = [];for (let i=0; i<size; i++) {  const someVal = Math.random();  numericArray[i] = someVal;  f64View[i] = someVal;  f32View[i] = someVal;}​​​​

Tests:

numeric array
const resultArray = numericArray.map(s => { return (s +1); });
const resultArray = numericArray.map(s => {
return (s +1);
});
Float64Array
const resultArray = f64View.map(s => { return (s +1); });
const resultArray = f64View.map(s => {
return (s +1);
});
Float32Array
const resultArray = f32View.map(s => { return (s +1); });
const resultArray = f32View.map(s => {
return (s +1);
});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Experimental features:

Memory measurements supported only in Chrome.
For precise memory measurements Chrome must be launched with --enable-precise-memory-info flag.
More information: Monitoring JavaScript Memory

Test case name	Result
numeric array
Float64Array
Float32Array

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (X11; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/118.0

Browser/OS: Firefox 118 on Linux

View result in a separate tab

Test name	Executions per second
numeric array	143253.5 Ops/sec
Float64Array	165020.0 Ops/sec
Float32Array	161873.8 Ops/sec

Autogenerated LLM Summary (model llama3.1:latest, generated 6 months ago):

Let's dive into the explanation.

Benchmark Definition

The provided JSON represents a benchmark test case, which is designed to compare the performance of different data types in JavaScript. The test case is called "Maping numeric vs f32 vs f64 with add" and its description explains that it compares the speed of mapping an array of random boolean values (using Math.random()) versus using ArrayBuffer views with Float32Array and Float64Array.

Options Being Compared

The benchmark tests three options:

Numeric Array: An array of random boolean values created using Math.random() and stored in a regular JavaScript array (numericArray).
Float64Array: A view on an ArrayBuffer with 8-byte floats (64-bit) created using new Float64Array(buffer64, 0, size) and populated with the same random values as the numeric array.
Float32Array: A view on an ArrayBuffer with 4-byte floats (32-bit) created using new Float32Array(buffer32, 0, size) and populated with the same random values as the numeric array.

Pros/Cons of Each Approach

Numeric Array:
- Pros: Easy to create and manipulate arrays in JavaScript.
- Cons: May be slower due to JavaScript's dynamic typing and garbage collection overhead.
Float64Array:
- Pros: Can store 8-byte floats, which can be useful for numerical computations that require high precision.
- Cons: Creates an ArrayBuffer view with a larger memory footprint than the numeric array.
Float32Array:
- Pros: Similar to Float64Array but stores 4-byte floats, which can reduce memory usage.
- Cons: May lose some precision in numerical computations compared to Float64Array.

Other Considerations

When choosing between these options, consider the following factors:

Precision: If high precision is required, use Float64Array. For less precise computations, Float32Array might be sufficient and more memory-efficient.
Performance: The benchmark results show that using an ArrayBuffer view with a typed array (either Float32Array or Float64Array) is significantly faster than the numeric array approach.
Memory Usage: If memory usage is a concern, consider using Float32Array instead of Float64Array.

Library Used

No specific library is mentioned in this test case.

Special JS Feature/Syntax Used

The test case uses JavaScript's built-in map() method to apply the addition operation (s + 1) to each element of the array. This is a common and efficient way to transform arrays in JavaScript.

Other Alternatives

If you need to perform numerical computations, consider using libraries like NumJS or mathjs, which provide optimized implementations for various mathematical operations. If memory usage is a concern, explore other data types like typed arrays (e.g., Int8Array, Uint16Array) or alternatives like WebAssembly, which can offer improved performance and memory efficiency.

I hope this explanation helps you understand the benchmark test case!

LLMs can make mistakes. Check important info.

Let's dive into the explanation.

**Benchmark Definition**

The provided JSON represents a benchmark test case, which is designed to compare the performance of different data types in JavaScript. The test case is called "Maping numeric vs f32 vs f64 with add" and its description explains that it compares the speed of mapping an array of random boolean values (using `Math.random()`) versus using ArrayBuffer views with `Float32Array` and `Float64Array`.

**Options Being Compared**

The benchmark tests three options:

1. **Numeric Array**: An array of random boolean values created using `Math.random()` and stored in a regular JavaScript array (`numericArray`).
2. **Float64Array**: A view on an ArrayBuffer with 8-byte floats (64-bit) created using `new Float64Array(buffer64, 0, size)` and populated with the same random values as the numeric array.
3. **Float32Array**: A view on an ArrayBuffer with 4-byte floats (32-bit) created using `new Float32Array(buffer32, 0, size)` and populated with the same random values as the numeric array.

**Pros/Cons of Each Approach**

1. **Numeric Array**:
	* Pros: Easy to create and manipulate arrays in JavaScript.
	* Cons: May be slower due to JavaScript's dynamic typing and garbage collection overhead.
2. **Float64Array**:
	* Pros: Can store 8-byte floats, which can be useful for numerical computations that require high precision.
	* Cons: Creates an ArrayBuffer view with a larger memory footprint than the numeric array.
3. **Float32Array**:
	* Pros: Similar to Float64Array but stores 4-byte floats, which can reduce memory usage.
	* Cons: May lose some precision in numerical computations compared to Float64Array.

**Other Considerations**

When choosing between these options, consider the following factors:

1. **Precision**: If high precision is required, use `Float64Array`. For less precise computations, `Float32Array` might be sufficient and more memory-efficient.
2. **Performance**: The benchmark results show that using an ArrayBuffer view with a typed array (either Float32Array or Float64Array) is significantly faster than the numeric array approach.
3. **Memory Usage**: If memory usage is a concern, consider using `Float32Array` instead of `Float64Array`.

**Library Used**

No specific library is mentioned in this test case.

**Special JS Feature/Syntax Used**

The test case uses JavaScript's built-in `map()` method to apply the addition operation (`s + 1`) to each element of the array. This is a common and efficient way to transform arrays in JavaScript.

**Other Alternatives**

If you need to perform numerical computations, consider using libraries like NumJS or mathjs, which provide optimized implementations for various mathematical operations. If memory usage is a concern, explore other data types like typed arrays (e.g., `Int8Array`, `Uint16Array`) or alternatives like WebAssembly, which can offer improved performance and memory efficiency.

I hope this explanation helps you understand the benchmark test case!

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