Benchmark: matrix test - MeasureThat.net

Script Preparation code:

function addObj(out, a, b) {
    out[0] = a[0] + b[0];
    out[1] = a[1] + b[1];
    out[2] = a[2] + b[2];
    return out;
};

function addArr(out, oo, a, b, ao,bo) {
    out[0+oo] = a[0+ao] + b[0+bo];
    out[1+oo] = a[1+ao] + b[1+bo];
    out[2+oo] = a[2+ao] + b[2+bo];
};

function addArr2(out, a, b, oo, ao,bo) {
    out[0+(oo || 0)] = a[0+(ao || 0)] + b[0+(bo || 0)];
    out[1+(oo || 0)] = a[1+(ao || 0)] + b[1+(bo || 0)];
    out[2+ (oo || 0)] = a[2+(ao || 0)] + b[2+(bo || 0)];
};

Tests:

vec3 Arr
var A = new Float32Array(300); var B = new Float32Array(300); var O = new Float32Array(300); for (var i = 0;i < 10000;++i) { for (var j=0;j< 100;++j) { addArr(O,j*3,A,B,j*3,j*3); } }
vec3 obj
var A = []; var B = []; var O = []; for (var x=0;x<100;++x) { A.push(new Float32Array(3)); B.push(new Float32Array(3)); O.push(new Float32Array(3)); } for (var i = 0;i < 10000;++i) { for (var j=0;j< 100;++j) { addObj(O[j],A[j],B[j]); } }
vec3 arr2
var A = new Float32Array(300); var B = new Float32Array(300); var O = new Float32Array(300); for (var i = 0;i < 10000;++i) { for (var j=0;j< 100;++j) { addArr2(O,A,B,j * 3,j*3,j*3); } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
vec3 Arr
vec3 obj
vec3 arr2

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Definition JSON**

The provided JSON represents a benchmark definition, which is a set of instructions that outline how to create and execute the benchmark. In this case, we have three test cases: `vec3 Arr`, `vec3 obj`, and `vec3 arr2`. Each test case has its own script preparation code, which defines functions for adding elements to arrays (`addObj` and `addArr`) or using a default function for adding elements (`addArr2`).

**Options Compared**

The three test cases compare different approaches for adding elements to arrays:

1. `vec3 Arr`: Uses the `push` method to add new arrays to an array, followed by calling `addObj` on each element.
2. `vec3 obj`: Creates a new array with initial values using `new Float32Array(3)`, and then pushes this array into another array. Finally, it calls `addObj` on each element of the nested array.
3. `vec3 arr2`: Uses the default function for adding elements to arrays (`addArr2`) to add elements directly to an existing array.

**Pros and Cons**

* **Push method (vec3 Arr)**:
	+ Pros: Efficient use of memory, as no new arrays need to be created.
	+ Cons: May incur overhead due to frequent calls to `push`.
* **Array creation with initial values (vec3 obj)**:
	+ Pros: Can take advantage of browser optimizations for creating arrays with initial values.
	+ Cons: Requires more memory allocation and may incur performance overhead due to the creation of intermediate arrays.
* **Default function for adding elements (vec3 arr2)**:
	+ Pros: Eliminates the need for explicit array additions, which can be faster and more efficient.
	+ Cons: May not work as expected if the default function is not optimized or does not handle edge cases correctly.

**Other Considerations**

* **Array performance**: JavaScript arrays are a fundamental data structure in modern web applications. Understanding how to optimize array operations is crucial for achieving good performance.
* **Browser variations**: MeasureThat.net tests different browsers, which can affect the results due to varying implementations and optimizations.

**Library Used**

None of the test cases explicitly use any external libraries beyond the built-in `Float32Array` type.

**Special JS Features or Syntax**

None of the test cases rely on special JavaScript features or syntax beyond the standard language specification.

Related benchmarks:

matrix test (version: 0)

test patterns for matrix perf

Comparing performance of: vec3 Arr vs vec3 obj vs vec3 arr2

Created: 9 years ago by: Guest

Jump to the latest result

vec3 Arr

vec3 obj

vec3 arr2

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):