Benchmark: object fill test + reverse noop + while

Tests:

for loop
const object = {}; for (let i=0; i<1440; i++) { object[i] = { foo: 'bar', count: 0, users: new Set(), }; }
array fill map
const object = {}; new Array(1440).fill().map((_, n) => object[n] = { foo: 'bar', count: 0, users: new Set(), });
for loop (reverse noop)
const object = {}; for (let i=1440; i>=0; --i) { object[i] = { foo: 'bar', count: 0, users: new Set(), }; }
while
const object = {}; let i = 1440; while (--i) { object[i] = { foo: 'bar', count: 0, users: new Set(), }; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for loop
array fill map
for loop (reverse noop)
while

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (X11; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/117.0

Browser/OS: Firefox 117 on Linux

View result in a separate tab

Test name	Executions per second
for loop	1609.1 Ops/sec
array fill map	1373.5 Ops/sec
for loop (reverse noop)	1292.0 Ops/sec
while	1295.2 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

LLMs can make mistakes. Check important info.

Let's dive into the details of the benchmark and explain what's being tested, the options being compared, their pros and cons, and other considerations.

**Benchmark Definition**

The benchmark is defined by a JSON object with four properties:

* `Name`: The name of the benchmark, which is "object fill test + reverse noop + while".
* `Description`: An empty string, indicating that there's no description provided.
* `Script Preparation Code`: An empty string, meaning that no code needs to be executed before running the benchmark.
* `Html Preparation Code`: Also an empty string, suggesting that no HTML preparation is required.

**Individual Test Cases**

The benchmark consists of four test cases:

1. **"for loop"`**: This test case involves creating an object using a traditional for loop, filling it with properties, and measuring its performance.
2. **"array fill map"`**: In this test case, the `Array.prototype.fill()` method is used to create an array, which is then mapped to fill the same number of objects as the original loop.
3. **"for loop (reverse noop)"`: This test case is similar to the first one, but uses a for loop with decrementing iterations (`i>=0; --i`) and measures its performance.
4. **"while"`**: The last test case uses a while loop to create objects, measuring its performance.

**Library Usage**

None of the test cases use any external libraries. They rely solely on JavaScript's built-in features for their execution.

**Special JS Features/Syntax**

The following special JS feature is used:

* `Array.prototype.fill()`: This method was introduced in ECMAScript 2015 (ES6) and allows you to fill an array with a given value.
* `Set`: The `Set` data structure is a built-in JavaScript object that stores unique values. It's used in all test cases to store user IDs.

**Options Being Compared**

The four test cases are being compared in terms of performance, specifically the number of executions per second (ExecutionsPerSecond). This metric allows us to determine which approach is faster.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

1. **"for loop"`**: Traditional for loops are widely supported and efficient. However, they can be verbose and might not perform well with large datasets.
	* Pros: Wide compatibility, easy to understand
	* Cons: Verbosity, potential performance issues with large datasets
2. **"array fill map"`**: Using `Array.prototype.fill()` and the `map()` method provides a concise and expressive way to create arrays. However, it might incur overhead due to the use of intermediate data structures.
	* Pros: Concise, expressive, and efficient in some cases
	* Cons: Potential performance issues with large datasets, usage of intermediate data structures
3. **"for loop (reverse noop)"**: This approach is similar to the first one but uses decrementing iterations. It might provide better cache locality, which can lead to performance improvements.
	* Pros: Potential performance benefits due to improved cache locality
	* Cons: Less intuitive for some developers, potential issues with overflow handling
4. **"while"`**: While loops are simple and efficient. However, they can be less intuitive than traditional for loops and might not provide the same level of readability.
	* Pros: Simple, efficient
	* Cons: Less intuitive, potential readability issues

**Other Considerations**

When interpreting these results, it's essential to consider factors like:

* Cache locality: The impact of the loop control flow on cache behavior can affect performance. The "for loop (reverse noop)" approach might provide better cache locality due to decrementing iterations.
* Intermediate data structures: The use of intermediate data structures like arrays and `Set` objects in some test cases might incur overhead or have unforeseen consequences on performance.
* Platform-specific issues: Some platforms or browsers might exhibit different performance characteristics for each loop control flow.

**Alternatives**

If you're interested in exploring alternative approaches, consider the following:

* **Using a more efficient data structure**: Instead of creating an object with many properties, you could use a more efficient data structure like a `Map` or a sparse array.
* **Parallelizing the benchmark**: You can use Web Workers or other parallelization techniques to measure performance on multi-core processors.
* **Optimizing the loop control flow**: Depending on your specific use case, you might find that an alternative loop control flow (e.g., using `for...of` loops) provides better performance.

Related benchmarks:

object fill test + reverse noop + while (version: 0)

Comparing performance of: for loop vs array fill map vs for loop (reverse noop) vs while

Created: 3 years ago by: Guest

Jump to the latest result

for loop

array fill map

for loop (reverse noop)

while

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):