Benchmark: Caching with loop conditions

Script Preparation code:

var arr = [...Array(1000).keys()];

Tests:

With cache
let sum = 0; for (let i = 0, arrLen=arr.length; i < arrLen; i++){ sum += arr[i]; }
Without cache
let sum = 0; for (let i = 0; i < arr.length; i++){ sum += arr[i]; }
While loop (as performance reference)
let sum = 0; let i = arr.length; while(i--){ sum += arr[i] }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
With cache
Without cache
While loop (as performance reference)

Fastest: N/A

Slowest: N/A

Latest run results:

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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 its test cases.

**Benchmark Definition**

The provided benchmark definition defines a JavaScript microbenchmark that compares the performance of three different approaches to calculate the sum of an array:

1. **With cache**: This approach uses a variable `arrLen` to store the length of the array, which is assigned directly from the `arr.length` property. This allows the loop to continue as long as `i < arrLen`.
2. **Without cache**: This approach does not use a separate variable for the array length and instead checks the condition within the loop itself.
3. **While loop (as performance reference)**: This is a reference benchmark that uses a while loop with decrementing `i` to calculate the sum.

**Options Compared**

The benchmark compares the performance of two approaches:

1. **With cache**: Uses a separate variable for the array length, allowing the loop to continue as long as `i < arrLen`.
2. **Without cache**: Does not use a separate variable for the array length and checks the condition within the loop itself.

**Pros and Cons**

* **With cache**:
	+ Pros: More efficient since the array length is only calculated once and can be reused.
	+ Cons: May lead to less accurate results if the array length changes frequently or unexpectedly.
* **Without cache**:
	+ Pros: Less dependent on the initial value of `arr.length` and may provide more accurate results.
	+ Cons: More computationally expensive since the array length is recalculated within the loop.

**Other Considerations**

The benchmark also includes a reference benchmark using a while loop, which can serve as a performance baseline. However, this approach has its own set of pros and cons:

* **While loop (as performance reference)**:
	+ Pros: Can provide an accurate performance baseline for the two compared approaches.
	+ Cons: May not be representative of real-world scenarios where array lengths are dynamic.

**Libraries Used**

None of the provided benchmark definitions use a specific JavaScript library. However, it's worth noting that some libraries like `lodash` or `underscore` may provide similar functionality to what is demonstrated in this benchmark (e.g., iteration helpers).

**Special JS Features or Syntax**

None of the test cases explicitly utilize special JavaScript features or syntax beyond basic ES6 syntax.

**Alternatives**

Other alternatives for measuring performance in JavaScript microbenchmarks include:

1. **V8 Benchmark**: A set of benchmarks provided by Google that targets specific aspects of V8, such as garbage collection and loop unrolling.
2. **JavaScriptPerf**: A benchmarking library specifically designed for JavaScript performance testing.
3. **Benchmark.js**: A lightweight benchmarking library that allows for easy creation of microbenchmarks.

Keep in mind that each alternative has its strengths and weaknesses, and the choice of which one to use depends on specific requirements and goals.

Related benchmarks:

Caching with loop conditions (version: 0)

Comparing performance of with and without caching array length.

Comparing performance of: With cache vs Without cache vs While loop (as performance reference)

Created: 3 years ago by: Guest

Jump to the latest result

With cache

Without cache

While loop (as performance reference)

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):