Benchmark: map-reduce-loop - MeasureThat.net

Script Preparation code:

window.cols = Array.from({length: 20}).map(() => ({ style: { width: '123px' }}))

Tests:

map-reduce
const sum = cols .map(c => parseFloat(c.style.width)) .reduce((a, b) => a + b); console.log(sum)
loop
let sum = 0 for (const c of cols) { sum += parseFloat(c.style.width) } console.log(sum)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map-reduce
loop

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks and analyze the provided benchmark.

**What is tested?**

The benchmark tests two approaches to calculate the sum of the widths of elements in an array:

1. `map-reduce-loop`: This approach uses the `map()` method to create a new array with transformed values, followed by the `reduce()` method to calculate the sum.
2. `loop`: This approach uses a traditional `for` loop to iterate over the array and add up the widths.

**Options compared**

The two approaches have different performance characteristics:

* `map-reduce-loop`:
	+ Pros: More concise and expressive code, leverages modern JavaScript features like `map()` and `reduce()`.
	+ Cons: May incur higher overhead due to the creation of a new array and function calls.
* `loop`: 
	+ Pros: Avoids the overhead of creating a new array and can be faster for small arrays or in situations where memory is limited.
	+ Cons: Less concise and less expressive code, may not take advantage of modern JavaScript features.

**Other considerations**

* **Array iteration**: Both approaches iterate over the array, but `map-reduce-loop` creates an intermediate array, which can lead to higher memory usage. In contrast, `loop` only uses a single variable `c` to access each element in the loop.
* **JavaScript engine optimizations**: The performance difference between the two approaches may be mitigated by the JavaScript engine's optimization techniques, such as inlining and caching.

**Library and special features**

In this benchmark, there is no specific library used. However, it does utilize modern JavaScript features like:

* `Array.from()`: Creates a new array from an iterable object.
* `map()` and `reduce()`: Array methods for transforming and aggregating data.

No special JavaScript features or syntax are explicitly used in the benchmark code.

**Alternatives**

Other alternatives to consider when writing performance-critical JavaScript code include:

* **NativeArray**: For large arrays, using a native array (like an `Uint8Array` or `Float64Array`) can be faster than working with JavaScript arrays.
* **Web Workers**: If the computation is CPU-intensive, using web workers can offload the work to a separate thread, improving performance and responsiveness.
* ** Just-In-Time (JIT) compilation**: Some modern browsers, like V8 in Chrome, have JIT compilers that can optimize JavaScript code at runtime.

When writing performance-critical code, consider profiling tools, such as Chrome DevTools' Profiler or Node.js's built-in `performance` module, to identify bottlenecks and optimize accordingly.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Linux; Android 10; SM-A505FN) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.198 Mobile Safari/537.36

Browser/OS: Chrome Mobile 86 on Android

View result in a separate tab

Test name	Executions per second
map-reduce	16166.5 Ops/sec
loop	16766.1 Ops/sec

Related benchmarks:

map-reduce-loop (version: 0)

Comparing performance of: map-reduce vs loop

Created: 3 years ago by: Guest

Jump to the latest result

map-reduce

loop

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):