Benchmark: sudoku21 - MeasureThat.net

Script Preparation code:

var data =  [[".",".",".",".",".",".",".",".","."], 
 ["4",".",".",".",".",".",".",".","."], 
 [".",".",".",".",".",".","6",".","."], 
 [".",".",".","3","8",".",".",".","."], 
 [".","5",".",".",".","6",".",".","1"], 
 ["8",".",".",".",".",".",".","6","."], 
 [".",".",".",".",".",".",".",".","."], 
 [".",".","7",".","9",".",".",".","."], 
 [".",".",".","6",".",".",".",".","."]]

Tests:

shao
function sudoku2(grid) { var empty = "."; var chunk = []; for (var i = 0; i < grid.length; i++) { for (var j = 0; j < grid.length; j++) { if (j % 3 === 0 && !checkChunk(i, j)) { return false; } if (checkRowValid(grid[i], grid[i][j], j)) { return false; } if (checkColumnValid(grid, i, j)) { return false; } } } return true; function checkChunk(rowIndex, colIndex) { var counter = rowIndex % 3; while (counter < 3 && (rowIndex + counter) < grid.length) { chunk.push(grid[rowIndex + counter].slice(colIndex, colIndex + 3)); counter++; } chunk = [].concat.apply([], chunk); var isChunkValid = chunk.every(function (item, i) { if (item === empty) { return true; } return chunk.indexOf(item, i + 1) <= 1; }); chunk = []; return isChunkValid; } function checkRowValid(row, item, rowIndex) { return item !== empty && row.indexOf(item, rowIndex + 1) !== -1; } function checkColumnValid(grid, rowIndex, colIndex) { var entries = 0; for (var row = 0; row < grid.length; row++) { if (grid[row][colIndex] !== empty && grid[rowIndex][colIndex] === grid[row][colIndex]) { entries++; } } return entries > 1; } } sudoku2(data);
thre
function sudoku2(grid) { for(var x = 0; x < grid.length; x++) { var checkRow = {}; var checkColumn = {}; var checkSquare = {}; var offsetX = Math.floor(x/3) * 3; var offsetY = (x % 3) * 3; for(var y = 0; y < grid.length; y++) { var squareX = Math.floor(y/3) + offsetX; var squareY = (y % 3) + offsetY; if (isBroken(checkRow, grid[x][y]) || isBroken(checkColumn, grid[y][x]) || isBroken(checkSquare, grid[squareX][squareY])) { return false; } } } function isBroken(check, value) { return (check[value] && value !== '.') || !(check[value] = true); } return true; }; sudoku2(data);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
shao
thre

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

I'll break down the benchmark and its components for you.

**Benchmark Overview**

The provided benchmark is a JavaScript test case that measures the performance of Sudoku grid validation. The goal is to determine whether a given 9x9 Sudoku grid is valid or not.

**Options Compared**

There are two distinct approaches compared in this benchmark:

1. **Approach 1: "shao"**
This approach uses a more traditional and explicit method for validating the Sudoku grid. It:
	* Iterates through each row, column, and sub-grid (3x3 squares) to check for validity.
	* Uses separate objects (`checkRow`, `checkColumn`, and `checkSquare`) to store the values of each cell in the current row, column, and sub-grid, respectively.
	* Checks if a value is already present in its corresponding row, column, or sub-grid using an "isBroken" function. If it's not, but should be (i.e., it's a valid Sudoku number), the approach returns false.

2. **Approach 2: "thre"**
This approach uses a more concise and optimized method for validating the Sudoku grid. It:
	* Uses a single loop to iterate through each row and cell.
	* Creates separate objects (`checkRow`, `checkColumn`, and `checkSquare`) to store the values of each cell in the current row, column, and sub-grid, respectively.
	* Uses an "isBroken" function to check if a value is already present in its corresponding row, column, or sub-grid. If it's not, but should be (i.e., it's a valid Sudoku number), the approach returns false.

**Pros and Cons of Each Approach**

**Approach 1: "shao"**

Pros:

* More explicit and intuitive for understanding the validation logic.
* May be more suitable for developers who prefer a more traditional approach to problem-solving.

Cons:

* More verbose code, which can increase compilation and runtime overhead.
* May require more memory allocation and garbage collection due to the use of separate objects for each row, column, and sub-grid.

**Approach 2: "thre"**

Pros:

* More concise and compact code, which can reduce compilation and runtime overhead.
* May be more suitable for developers who prefer a more optimized and efficient approach to problem-solving.

Cons:

* Less explicit and less intuitive than the traditional approach.
* May require more complex logic to understand the validation rules and edge cases.

**Other Considerations**

* **Memory Allocation**: Both approaches allocate memory for each row, column, and sub-grid. However, Approach 2 uses a more compact representation of the Sudoku grid using separate objects, which may reduce memory allocation overhead.
* **Cache Performance**: The compact representation used in Approach 2 may improve cache performance due to its smaller size and more sequential access pattern.

**Benchmark Results**

The provided benchmark results show that:

* "shao" approach performs better (44648.5390625 executions per second) compared to the "thre" approach (31373.638671875 executions per second).

However, it's essential to note that the results may depend on various factors such as system architecture, compiler optimizations, and interpreter behavior.

Related benchmarks:

sudoku21 (version: 0)

Comparing performance of: shao vs thre

Created: 8 years ago by: Guest

Jump to the latest result

shao

thre

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):