Benchmark: Test benchmark # 2

HTML Preparation code:

<div>yo</div>

Script Preparation code:

var i = 0;

Tests:

Test case #1
i++;
Test case #2
i--;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Test case #1
Test case #2

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 7 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36

Browser/OS: Chrome 124 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Test case #1	158971488.0 Ops/sec
Test case #2	152910208.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the Benchmark Definition and test cases to understand what's being tested.

**Benchmark Definition**

The provided JSON defines a benchmark with two main components:

1. **Script Preparation Code**: `var i = 0;` - This code is executed once before running the actual benchmark. It sets an initial value of `i` to 0.
2. **Html Preparation Code**: `<div>yo</div>` - This code is used to generate a simple HTML element that will be displayed in the benchmark results page.

**Test Cases**

The benchmark consists of two individual test cases, each defined by:

1. **Benchmark Definition**: A JavaScript statement or expression to be executed repeatedly.
2. **Test Name**: A unique identifier for each test case.

In this case, the two test cases are:

* `i++;` (incrementing a variable)
* `i--;` ( decrementing a variable)

**Options Comparison**

When running benchmarks like these, options such as compiler optimizations, caching, or loop unrolling can impact performance. Here's a brief overview of the pros and cons of each approach:

1. **No optimization**:
	* Pros: Simple, easy to understand, and reproduceable results.
	* Cons: May not be representative of real-world scenarios or performance-critical code.
2. **Loop unrolling**:
	* Pros: Can lead to significant performance improvements in certain cases.
	* Cons: Requires careful tuning and may introduce additional complexity.
3. **Cache-based optimization**:
	* Pros: Can provide better cache locality and reduce memory accesses.
	* Cons: May not be relevant for all types of workloads or architectures.

In the case of the provided benchmark, loop unrolling is unlikely to have a significant impact, as the increment/decrement operations are simple and don't benefit from optimization.

**Library Usage**

There is no apparent library usage in this benchmark. The script preparation code and html preparation code are basic JavaScript snippets that don't rely on any external libraries.

**Special JS Features or Syntax**

The provided benchmark doesn't use any special JavaScript features or syntax. However, if we were to consider other scenarios, some examples of special features or syntax might include:

* `const`/`let` declarations
* Arrow functions
* Class expressions
* Promises and async/await

These features are used extensively in modern JavaScript code and can have a significant impact on performance.

**Other Alternatives**

If you were to create your own benchmark, some alternatives to consider include:

1. **GPU-based benchmarks**: Measure the performance of GPU-accelerated computations or compute-intensive algorithms.
2. **WebAssembly benchmarks**: Test the performance of WebAssembly (WASM) modules and their interaction with JavaScript engines.
3. **Thread-level benchmarks**: Compare the performance of different threading models, such as parallel loops or async/await.

When designing a benchmark, it's essential to carefully consider the scope, relevance, and representativeness of the tests to ensure accurate and meaningful results.

Related benchmarks:

Test benchmark # 2 (version: 0)

New version of MeasureThat.net

Comparing performance of: Test case #1 vs Test case #2

Created: 2025 years ago by: Registered User

Jump to the latest result

Test case #1

Test case #2

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):