Benchmark: for loop vs Array.map

Script Preparation code:

var values = [];
for (var i = 0; i < 256; i++) {
  values[i] = [];
  for (var j = 0; j < 256; j++) {
    values[i][j] = Math.random();
  }
}

Tests:

for-loop
for (var i = 0; i < 256; i++) { for (var j = 0; j < 256; j++) { values[i][j] = values[i][j] < Math.random() ? 1 : 0; } }
Array.map
values = values.map(function(arr) { return arr.map(function(val) { return val < Math.random() ? 1 : 0; }); });

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for-loop
Array.map

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 28 days ago):

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested, compared options, pros and cons of each approach, and other considerations.

**Benchmark Definition**

The benchmark definition consists of two scripts: `for loop` and `Array.map`. Both scripts generate a 2D array of size 256x256 using random values. The main difference between the two scripts is how they manipulate the generated array:

1. **For Loop**: The script uses nested for loops to iterate over each element in the array and assign either 0 or 1 based on a condition (random value comparison). This approach requires explicit iteration and indexing.
2. **Array.map**: The script uses the `map()` method to create a new array with the same structure as the original, but with values assigned based on the same condition. The `map()` method is a higher-order function that takes a callback function as an argument.

**Options Compared**

The benchmark compares two options:

1. **For Loop**: An iterative approach using nested for loops.
2. **Array.map**: A functional programming approach using the `map()` method.

**Pros and Cons of Each Approach**

**For Loop:**

Pros:

* Easy to understand and implement
* No memory allocation required
* Can be optimized for specific use cases

Cons:

* Inefficient for large datasets due to nested iterations
* May lead to slower performance due to indexing lookups

**Array.map:**

Pros:

* High-level abstraction, reducing boilerplate code
* Efficient for large datasets since it uses built-in iteration
* Often faster than explicit loops due to optimized implementations

Cons:

* Can be less intuitive for developers unfamiliar with functional programming concepts
* May lead to higher memory allocation due to creating a new array

**Other Considerations**

* **Scalability**: The benchmark is designed to test performance on large datasets. Both approaches should perform well, but the `map()` method might have an advantage due to its optimized implementation.
* **Memory Allocation**: Since both scripts create a 2D array of size 256x256, memory allocation will be similar for both approaches. However, the `for loop` approach may incur additional overhead due to indexing lookups.
* **Browser Implementation**: The benchmark uses Chrome 58 as the test browser. Other browsers might have different implementations or optimizations that could affect performance.

**Libraries and Special JS Features**

The benchmark does not use any libraries or special JavaScript features beyond standard syntax and built-in functions like `Math.random()` and `Array.map()`. If used, these are already included in the benchmark by default.

Now, let's look at some alternative approaches:

* **V8 Compiler**: Some benchmarks might compare performance between different V8 compiler versions or configurations. This could reveal differences in optimization strategies or implementation details.
* **Different JavaScript Engines**: Benchmarks like this one can also be used to compare performance across other JavaScript engines, such as SpiderMonkey (Firefox) or JavaScriptCore (Safari).
* **Other Data Structures**: Alternative data structures, like linked lists or queues, might be compared for their performance in different scenarios.

Keep in mind that the specific approach and options chosen will depend on the benchmark's requirements and goals.

Latest run results:

Run details: (Test run date: 6 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.96 Safari/537.36

Browser/OS: Chrome 58 on Windows

View result in a separate tab

Test name	Executions per second
for-loop	29.3 Ops/sec
Array.map	35.4 Ops/sec

Related benchmarks:

for loop vs Array.map (version: 0)

Comparing performance of: for-loop vs Array.map

Created: 6 years ago by: Guest

Jump to the latest result

for-loop

Array.map

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 28 days ago):