Benchmark: array vs float32array without conversion 2

Script Preparation code:

function getRandomInt(max) {
  return Math.floor(Math.random() * Math.floor(max));
}
var a = [...Array(10000)].map(_ => Math.random(1000000));
var ta = (new Float32Array(10000)).map(_ => Math.random(1000000));

Tests:

array sort
a.sort();
typedArray sort
ta.sort();
array i/o
for (let i = 0; i < 10000; ++i) { a[i] = a[i] + 1; }
typedArray i/o
for (let i = 0; i < 10000; ++i) { ta[i] = ta[i] + 1; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
array sort
typedArray sort
array i/o
typedArray i/o

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark JSON and explain what's being tested, compared, and the pros/cons of different approaches.

**Benchmark Definition JSON**

The `Script Preparation Code` section defines two test cases:

1. Creating an array (`a`) with 10,000 random integers between 0 and 1,000,000.
2. Creating a Float32Array (`ta`) with 10,000 random floats between 0 and 1,000,000.

The `Html Preparation Code` section is empty, indicating that no HTML setup or preparation is required for this benchmark.

**Individual Test Cases**

There are four test cases:

1. **Array Sort**: Sorting the array `a` using the built-in `sort()` method.
2. **TypedArray Sort**: Sorting the Float32Array `ta` using the built-in `sort()` method.
3. **Array I/O**: Modifying each element of the array `a` by adding 1 to it, with a total of 10,000 iterations.
4. **TypedArray I/O**: Modifying each element of the Float32Array `ta` by adding 1 to it, with a total of 10,000 iterations.

**Comparison and Analysis**

The benchmark compares the performance of arrays and Float32Arrays when performing sorting and input/output operations. The goal is to determine which data structure is faster in terms of execution time.

**Pros and Cons:**

* **Arrays**: Fast for small to medium-sized datasets but can be slower for larger datasets due to cache inefficiencies.
	+ Pros: Easy to use, widely supported, and suitable for most use cases.
	+ Cons: May suffer from cache thrashing and performance degradation with large datasets.
* **Float32Arrays**: Designed for high-performance numerical computations on the GPU. They provide better cache locality and parallelization opportunities compared to regular arrays.
	+ Pros: Optimized for performance in numerical computations, better cache locality, and can take advantage of parallel processing.
	+ Cons: May require additional setup and configuration, and may not be suitable for all use cases.

**Library Usage**

There is no explicit library usage mentioned in the benchmark definition. However, it's likely that the `Float32Array` class is part of the Web API (specifically, the `TypedArray` interface).

**Special JS Feature or Syntax**

None are explicitly mentioned in the provided code snippets.

**Other Alternatives**

For more precise and detailed benchmarks, you can explore using:

* Benchmarking libraries like ` benchmark.js`, ` js-benchmark`, or ` benchmark- library`.
* Testing frameworks like ` Jest` or ` Mocha`, which provide built-in support for benchmarking.
* Performance analysis tools like ` Chrome DevTools` or ` Firefox Developer Tools`, which offer a range of performance metrics and optimization techniques.

Keep in mind that the choice of alternative will depend on your specific requirements, project goals, and personal preferences.

Related benchmarks:

array vs float32array without conversion 2 (version: 0)

Comparing performance of: array sort vs typedArray sort vs array i/o vs typedArray i/o

Created: 5 years ago by: Guest

Jump to the latest result

array sort

typedArray sort

array i/o

typedArray i/o

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):