Benchmark: Pre-array or function calls

Script Preparation code:


var xOffset = 0;
var xScale = 1;
var i = 0, l = 10000000;

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:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON data to understand what is being tested in this JavaScript microbenchmark.

**Benchmark Definition**

The benchmark definition represents a simple loop-based calculation. It consists of two parts:

1. **Preparation Code**: This section initializes variables `xOffset`, `xScale`, and sets up an array `arr` with dimensions `l x 1`. The function `getX(index)` is defined to calculate the value at index `i` given `X` offset.
2. **Html Preparation Code**: This section is empty, indicating that no HTML-specific code needs to be executed before running the benchmark.

**Individual Test Cases**

There are two test cases:

**Test Case 1: "test1"**
```javascript
for( ;j < 10; j ++ ) {
  for( i = 0; i < l; i ++ ) {
    tot += getX( i );
  }
}
```
This test case uses the `getX(i)` function to calculate values and accumulates them in the variable `tot`.

**Test Case 2: "test2"**
```javascript
for( ;i < l; i ++ ) {
  arr[ i ] = xOffset + i * xScale;
}

for( ;j < 10; j ++ ) {
  for( i = 0; i < l; i ++ ) {
    tot += arr[ i ];
  }
}
```
This test case first initializes an array `arr` with dimensions `l x 1`, and then uses this array to calculate values and accumulates them in the variable `tot`.

**Comparison of Options**

The two test cases differ in how they use the calculated values:

* **Test Case 1 ("test1")**: Uses the `getX(i)` function directly.
* **Test Case 2 ("test2")**: Stores the calculated value in an array `arr` and then uses this array to accumulate the values.

**Pros and Cons of Different Approaches**

* **Direct Function Call (Test Case 1)**:
	+ Pros: More straightforward, avoids additional memory allocation for storing intermediate results.
	+ Cons: May lead to slower performance due to repeated function calls.
* **Array-Based Approach (Test Case 2)**:
	+ Pros: Can potentially be faster due to reduced function call overhead and improved cache locality.
	+ Cons: Requires additional memory allocation for the array, which can impact performance in certain scenarios.

**Library Usage**

There is no explicit library usage mentioned in the benchmark definition. However, it's worth noting that some JavaScript engines may optimize away unnecessary calculations or use SIMD instructions (Single Instruction, Multiple Data) to improve performance.

**Special JS Features/Syntax**

No special JavaScript features or syntax are used in this benchmark. The code only uses standard JavaScript constructs such as loops, variables, and function definitions.

**Other Alternatives**

For similar microbenchmarks, consider the following alternatives:

* Measuring the execution time of a single loop iteration instead of looping over multiple iterations.
* Using SIMD instructions to accelerate calculations for large datasets.
* Using parallel processing or concurrent programming techniques to take advantage of multi-core processors.
* Profiling JavaScript engine-specific optimizations, such as those related to Just-In-Time (JIT) compilation.

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):