Benchmark: martix-invert - MeasureThat.net

Script Preparation code:

var data = [[377310,886482,46512],[241544,604470,29350]];

Tests:

original way
var newArray = []; for(var i = 0; i < data.length; i++){ newArray.push([]); }; for(var i = 0; i < data.length; i++){ for(var j = 0; j < arrayLength; j++){ newArray[j].push(data[i][j]); }; }; return(newArray);
new way
var newData = []; for(var i = 0; i < data.length; i++){ for(var j = 0; j < data[i].length; j++){ if (i === 0) { newData.push([]); } newData[j][i] = data[i][j]; } }
new way push
var newData = []; for(var i = 0; i < data.length; i++){ for(var j = 0; j < data[i].length; j++){ if (i === 0) { newData.push([]); } newData[j].push(data[i][j]); } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
original way
new way
new way push

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

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

**Benchmark Overview**

The benchmark measures the performance of inverting a matrix using two different approaches:

1. **Original Way**: This approach creates a new array with multiple sub-arrays, where each sub-array is initialized with zeros. Then, it iterates over the input data to populate the sub-arrays.
2. **New Way** (and its variant, **New Way Push**): These approaches use a different data structure, creating a 2D array on the fly by iterating over the rows and columns of the input matrix.

**Library Used**

There is no specific library mentioned in the benchmark definition or test cases. However, it's likely that the implementation uses built-in JavaScript features, such as arrays and loops.

**Special JS Feature/Syntax**

The benchmarks use a special syntax for creating 2D arrays on the fly. In the **New Way** approach, this is achieved using nested loops to populate the array:

```javascript
for (var i = 0; i < data.length; i++) {
    for (var j = 0; j < data[i].length; j++) {
        newData[j][i] = data[i][j];
    }
}
```

This syntax creates a new 2D array (`newData`) with the desired shape and populates it with the values from the input matrix.

**Options Compared**

The benchmark compares three approaches:

1. **Original Way**: This approach uses multiple sub-arrays to store the inverted data.
2. **New Way**: This approach uses a single 2D array to store the inverted data, created on the fly using nested loops.
3. **New Way Push**: This variant of the **New Way** approach adds an additional layer of indirection by using `push()` to add sub-arrays to the main array.

**Pros and Cons**

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

* **Original Way**:
	+ Pros: Easy to understand, simple implementation.
	+ Cons: Creates multiple sub-arrays, which can lead to memory fragmentation and performance issues for large datasets.
* **New Way**:
	+ Pros: Efficient use of memory, creates a single 2D array, reducing overhead.
	+ Cons: Requires nested loops, which can be more complex to understand and implement.
* **New Way Push**:
	+ Pros: Combines the benefits of both approaches, providing a balance between ease of understanding and performance.
	+ Cons: Adds an additional layer of indirection, which may increase memory allocation overhead.

**Other Considerations**

When designing a benchmark, it's essential to consider factors like:

* **Data size**: Larger datasets can lead to significant differences in performance.
* **Platform**: Different platforms (e.g., Chrome, Safari) may have varying performance characteristics.
* **Versioning**: Updating browser versions or JavaScript engine versions can impact performance.

**Alternatives**

Other approaches to matrix inversion might include:

1. Using a dedicated library or framework for linear algebra operations.
2. Implementing matrix inversion using advanced algorithms like Strassen's algorithm or Coppersmith-Winograd algorithm.
3. Utilizing parallel processing techniques, such as GPU acceleration or distributed computing.

However, these alternatives are typically more complex and may not be suitable for microbenchmarking purposes.

Related benchmarks:

martix-invert (version: 0)

Comparing performance of: original way vs new way vs new way push

Created: 9 years ago by: Guest

Jump to the latest result

original way

new way

new way push

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):