Benchmark: countNeighbours - MeasureThat.net

Script Preparation code:

var row = 1;
var col = 2;
var data = [[1, 0, 0, 1, 0],
            [0, 1, 0, 0, 0],
            [0, 0, 1, 0, 1],
            [1, 0, 0, 0, 0],
            [0, 0, 1, 0, 0]];

Tests:

Reduce
return data.reduce((r, v, i) => i>row+1 || i<row-1 ? r : v.reduce((c, v, i) => i>col+1 || i<col-1 ? c : c + v, r), 0) - data[row][col];
getCell
function getCellValue(r,c) { return (data[r]&&data[r][c])?data[r][c]:0; }; return getCellValue(row-1,col-1)+getCellValue(row-1,col)+getCellValue(row-1,col+1)+getCellValue(row,col-1)+getCellValue(row,col+1)+getCellValue(row+1,col-1)+getCellValue(row+1,col)+getCellValue(row+1,col+1);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce
getCell

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 8 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36

Browser/OS: Chrome 53 on Mac OS X 10.11.3

View result in a separate tab

Test name	Executions per second
Reduce	252072.8 Ops/sec
getCell	317707.5 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition Json**

The benchmark is designed to measure the performance of two different approaches to calculate the sum of neighboring cell values in a 5x5 grid data structure.

**Options Compared**

There are two main options compared:

1. **Reduce**: This approach uses the `reduce()` method, which iterates over the array and accumulates the sum of neighboring cell values.
2. **getCell**: This approach uses a custom function, `getCellValue()`, to individually retrieve each cell value from the grid data structure and then sums them up.

**Pros and Cons**

**Reduce Approach:**

Pros:

* Efficient use of memory, as it only accesses the current row's neighbors.
* Can be implemented using standard array methods, making it easy to understand and maintain.

Cons:

* May have performance issues if the grid is very large or the data structure is not optimized for random access.
* Requires iterating over the entire grid, which can lead to slow performance on certain hardware configurations.

**getCell Approach:**

Pros:

* Can be faster for very large grids, as it only accesses each cell individually.
* Allows for more control over how the grid data structure is accessed and processed.

Cons:

* Requires a custom function, `getCellValue()`, which can add complexity to the implementation.
* May have performance issues if the grid data structure is not optimized for random access or if there are many edge cases.

**Library:**

In this benchmark, there is no explicit library being used. However, it's possible that the JavaScript engine being tested (e.g., V8 in Chrome) provides some built-in optimizations or features that could impact the performance of these approaches.

**Special JS Features/Syntax:**

There are no special JavaScript features or syntax being used in this benchmark. The `reduce()` method is a standard array method, and the custom function, `getCellValue()`, uses basic JavaScript syntax.

**Other Alternatives**

Some alternative approaches that could be considered for calculating the sum of neighboring cell values include:

* Using a spatial data structure, such as a quadtree or k-d tree, to quickly access nearby cells.
* Implementing a custom optimization technique, such as parallel processing or caching, to speed up performance.

These alternatives would require additional implementation and testing, but could potentially offer better performance or scalability for large grids.

Related benchmarks:

countNeighbours (version: 0)

Comparing performance of: Reduce vs getCell

Created: 8 years ago by: Guest

Jump to the latest result

Reduce

getCell

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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