Benchmark: Fill an MxN 2D nested array with random numbers

Script Preparation code:

var M = 1000,
    N = 500;

Tests:

a
const arr = []; for(let i = 0; i < M; i += 1) { arr[i] = []; for(let j = 0; j < N; j += 1) arr[i][j] = Math.random() * 2 - 1; } return arr;
b
const arr = Array(M).fill(0).map(a => Array(N).fill(0).map(b => Math.random() * 2 - 1)); return arr;
c
const arr = Array.from({ length: M }, () => Array.from({ length: N }, () => Math.random() * 2 - 1)); return arr;
d
const arr = [...Array(M)].map(a => [...Array(N)].map(b => Math.random() * 2 - 1));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
a
b
c
d

Fastest: N/A

Slowest: N/A

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

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Let's break down the provided benchmark and explain what's being tested.

**Benchmark Definition**

The benchmark is designed to measure the performance of creating and populating a 2D nested array with random numbers. The input parameters are:

* `M`: The number of rows in the 2D array.
* `N`: The number of columns in the 2D array.

**Options Compared**

There are four different approaches compared:

1. **Manual Loop**: The first approach uses a manual loop to create each row and column:
```javascript
for (let i = 0; i < M; i += 1) {
  arr[i] = [];
  for (let j = 0; j < N; j += 1)
    arr[i][j] = Math.random() * 2 - 1;
}
```
This approach requires explicit loop control and can be less efficient due to the overhead of loop management.

2. **Array.fill() + map()**: The second approach uses `Array.fill()` to create an array of size `M` and then maps over it using another `map()` function to create a 2D array:
```javascript
const arr = Array(M).fill(0).map(a => Array(N).fill(0).map(b => Math.random() * 2 - 1));
```
This approach uses the optimized `Array.fill()` and `map()` functions, which are generally faster than manual loops.

3. **Array.from() + map()**: The third approach uses `Array.from()` to create an array from a constructor function that generates each row, and then maps over it using another `map()` function:
```javascript
const arr = Array.from({ length: M }, () => Array.from({ length: N }, () => Math.random() * 2 - 1));
```
This approach uses the optimized `Array.from()` function and map functions.

4. **Spread Operator + map()**: The fourth approach uses the spread operator (`[...]`) to create a new array by spreading an existing array, and then maps over it using another `map()` function:
```javascript
const arr = [...Array(M)].map(a => [...Array(N)].map(b => Math.random() * 2 - 1));
```
This approach uses the optimized spread operator.

**Pros and Cons**

Here are some pros and cons of each approach:

* Manual Loop:
	+ Pros: Easy to understand, can be useful for debugging.
	+ Cons: Less efficient due to loop overhead.
* Array.fill() + map():
	+ Pros: Optimized functions reduce overhead, generally faster.
	+ Cons: May not work as expected if array is not initialized with zeros.
* Array.from() + map():
	+ Pros: Optimized function reduces overhead, flexible syntax.
	+ Cons: May require more memory due to the constructor function used in `Array.from()`.
* Spread Operator + map():
	+ Pros: Concise syntax, can be efficient due to optimized spread operator.
	+ Cons: May not work as expected if array is not initialized with zeros.

**Other Considerations**

When comparing these approaches, consider factors such as:

* Memory usage: How much memory does each approach require?
* Cache locality: How well do the arrays line up in cache, affecting access patterns?
* Parallelism: Can the approaches be easily parallelized or optimized for concurrent execution?

**Libraries and Special Features**

None of the provided benchmark cases use any external libraries. However, it's worth noting that some JavaScript engines (like V8) have built-in optimizations for certain array operations.

**Special JS Feature**

There are no special JS features mentioned in the benchmark case.

Related benchmarks:

Fill an MxN 2D nested array with random numbers (version: 0)

Comparing performance of: a vs b vs c vs d

Created: 2 years ago by: Guest

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a

b

c

d

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):