Benchmark: fastest vs slowest is backwards

Tests:

should be slower
const result = [] const ary = [1,2,3,4,5,6,7,8,9,10] for (let i = 0; i < 1000; i++) { result.push(ary[i%10] * 2) }
should be faster
const result = []; const ary = [1,2,3,4,5,6,7,8,9,10] for (let i = 0; i < 10; i++) { result.push(ary[i%10] * 2) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
should be slower
should be faster

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 29 days ago):

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark definition is an empty object, which means that the user hasn't specified any specific test cases or scripts to run. The only information provided is a brief name for the benchmark ("fastest vs slowest is backwards") and a description (which is null).

**Individual Test Cases**

There are two individual test cases:

1. **"should be slower"**
	* Script: `const result = []; const ary = [1,2,3,4,5,6,7,8,9,10]; for (let i = 0; i < 1000; i++) { result.push(ary[i%10] * 2); }`
	* This script creates an empty array `result` and then loops through the `ary` array 1000 times, pushing the result of `(ary[i%10] * 2)` to the `result` array.
2. **"should be faster"**
	* Script: `const result = []; const ary = [1,2,3,4,5,6,7,8,9,10]; for (let i = 0; i < 10; i++) { result.push(ary[i%10] * 2); }`
	* This script is similar to the first one, but it loops through the `ary` array only 10 times.

**Options being compared**

In this benchmark, two options are being compared:

1. **1000 iterations**: The first test case uses a large number of iterations (1000) in its loop.
2. **10 iterations**: The second test case uses a much smaller number of iterations (10) in its loop.

**Pros and Cons of each approach**

* **Using 1000 iterations**:
	+ Pros: Can help to identify performance issues with the script, especially if it's using many resources or performing complex calculations.
	+ Cons: May not be representative of real-world usage patterns, where iterations are often limited by other constraints (e.g., user input, network latency).
* **Using 10 iterations**:
	+ Pros: Can provide a more realistic representation of real-world usage patterns and help identify performance issues that arise from frequent updates to the data.
	+ Cons: May not be able to detect performance issues related to large numbers of iterations.

**Library or Framework**

There is no explicit mention of any libraries or frameworks being used in these test cases. However, the use of `const` and arrow functions (`=>`) suggests that the code may be written in a modern JavaScript style.

**Special JS feature or syntax**

There are no special JavaScript features or syntaxes mentioned in these test cases.

**Other considerations**

When interpreting benchmark results, it's essential to consider factors beyond just raw execution speed. For example:

* **Memory usage**: How much memory does the script consume during its execution?
* **Cache performance**: Does the script benefit from caching frequently accessed data?
* **Hardware constraints**: Are there any hardware-related limitations that could impact performance (e.g., CPU architecture, available memory)?

**Alternatives**

Other alternatives for benchmarking JavaScript code include:

1. **Benchmarking frameworks**: Tools like Benchmark.js or Benchmark SU provide a more structured approach to benchmarking and can help ensure consistency across different test environments.
2. **WebAssembly**: WebAssembly is a binary format that allows JavaScript code to be compiled to efficient machine code, which can improve performance in certain contexts.
3. **Just-In-Time (JIT) compilers**: Some browsers use JIT compilers to optimize JavaScript code at runtime, which can provide better performance than traditional interpretation.

Latest run results:

Run details: (Test run date: 5 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36

Browser/OS: Chrome 73 on Mac OS X 10.13.6

View result in a separate tab

Test name	Executions per second
should be slower	169748.5 Ops/sec
should be faster	23717238.0 Ops/sec

Related benchmarks:

fastest vs slowest is backwards (version: 0)

Comparing performance of: should be slower vs should be faster

Created: 5 years ago by: Guest

Jump to the latest result

should be slower

should be faster

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 29 days ago):