Benchmark: Pre-array or function calls

Script Preparation code:


var xOffset = 0;
var xScale = 1;
var i = 0, l = 1000000;

function getX( index ) {
  return xOffset + index * xScale;
}

var j = 0;
var tot = 0;

Tests:

test1
for( ;j < 10; j ++ ) { for( i = 0; i < l; i ++ ) { tot += getX( i ); } }
test2
for( ;i < l; i ++ ) { arr[ i ] = xOffset + i * xScale; } for( ;j < 10; j ++ ) { for( i = 0; i < l; i ++ ) { tot += arr[ i ]; } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
test1
test2

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.

Let's dive into the explanation of the provided benchmark.

**Benchmark Definition**
The benchmark definition is represented by two JSON objects: one for the script preparation code and another for the HTML preparation code. The first object contains variables `xOffset`, `xScale`, and `i` (initialized to 0, 1,000,000, respectively). It also defines a function `getX(index)` that returns the value of `xOffset + index * xScale`. The second object is empty, which means no additional setup or initialization code is required for the benchmark.

**Script Preparation Code**
The script preparation code contains two variables: `j` (initialized to 0) and `tot` (initialized to 0). It defines a function `getX(index)` as mentioned in the benchmark definition. The purpose of this function is to calculate a value based on the input index, but its actual implementation is not relevant for this benchmark.

**Test Cases**
There are two test cases:

1. **test1**: This test case uses a traditional loop structure with nested loops to iterate over `i` and `j`. It calls the `getX(i)` function inside the inner loop and accumulates the results in the `tot` variable.
2. **test2**: This test case also uses a traditional loop structure with nested loops, but it calculates the values directly without calling an external function (`getX(i)`). Instead, it stores the intermediate results in an array (`arr[i]`) and then sums them up inside the inner loop.

**Options Compared**
The two test cases differ in how they calculate the `tot` variable:

* **test1**: Uses a function call to calculate each value and accumulates the results.
* **test2**: Calculates values directly without using an external function, storing intermediate results in an array.

**Pros and Cons**

* **test1**:
	+ Pros: Simpler code, easier to read and understand. The function call is just a single operation, making it more efficient from a CPU perspective.
	+ Cons: Additional function call overhead might affect performance.
* **test2**:
	+ Pros: Avoids the function call overhead, which can be beneficial for performance-critical applications.
	+ Cons: More complex code, harder to read and understand. The array storage and indexing operations add overhead.

**Library/Functionality**
None of the test cases explicitly use a library or built-in JavaScript functionality that requires special consideration.

**Special JS Feature/Syntax**
None of the test cases use any special JavaScript features or syntax that require additional explanation.

**Other Considerations**

* **Cache Locality**: The two test cases have different cache locality characteristics. `test1` uses a function call, which can lead to more cache misses due to the extra operation. `test2`, on the other hand, calculates values directly, potentially improving cache locality.
* **Branch Prediction**: The two test cases have different branch prediction patterns. `test1` has a single branch (the loop), while `test2` has multiple branches (the loop and array indexing). This can affect branch prediction accuracy.

**Alternative Approaches**

* **Using Built-in Functions**: Instead of calculating values directly, the test cases could use built-in functions like `Array.prototype.reduce()` or `Math.max()` to simplify the code.
* **Avoiding Function Calls**: The test cases could avoid function calls altogether by using a more iterative approach, such as using a loop with multiple assignments.
* **Parallelization**: For performance-critical applications, parallelizing the test cases using techniques like multi-threading or concurrent execution could be beneficial.

Related benchmarks:

Pre-array or function calls (version: 0)

Comparing performance of: test1 vs test2

Created: 9 years ago by: Guest

Jump to the latest result

test1

test2

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):