Benchmark: Simple benchmark

Tests:

32
crypto.getRandomValues(new Uint32Array(20))
Max
crypto.getRandomValues(new Uint32Array(15000))
8
crypto.getRandomValues(new Uint32Array(20))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
32
Max
8

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36

Browser/OS: Chrome 123 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
32	152090.9 Ops/sec
Max	2001.6 Ops/sec
8	150991.8 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into explaining the provided benchmark.

**What is tested on the provided JSON?**

The benchmark tests the performance of different approaches to generating random numbers using the `crypto` module in JavaScript. Specifically, it measures how many executions per second (i.e., how fast) each approach can produce a certain number of random 32-bit integers.

**Options compared:**

There are three test cases:

1. **"32"`**: Tests producing 20 random 32-bit integers using `crypto.getRandomValues(new Uint32Array(20))`.
2. **"Max"`**: Tests producing 15,000 random 32-bit integers using `crypto.getRandomValues(new Uint32Array(15000))`. This is likely a larger-than-usual batch size to see how the performance scales.
3. **"8"`**: Tests producing 20 random 32-bit integers using `crypto.getRandomValues(new Uint32Array(20))`, similar to the first test case.

**Pros and cons of each approach:**

1. **"32"`**: This is a small batch size, which might be sufficient for many use cases. However, it may not be representative of more complex scenarios that require larger batches.
2. **"Max"`**: This large batch size can help identify performance issues in high-traffic or data-intensive applications. However, it may also lead to slower performance due to the increased overhead of generating a large number of random numbers.
3. **"8"`**: This is an intermediate batch size that falls between the two above. It might be more representative of typical use cases but still has limitations compared to larger batches.

**Library and its purpose:**

The `crypto` module in JavaScript provides a way to perform various cryptographic functions, including generating random numbers. The `getRandomValues()` function is used to generate an array of pseudo-random numbers based on the operating system's entropy pool.

**Special JS feature or syntax:**

There are no specific features or syntax mentioned in the benchmark that require special handling.

**Other alternatives:**

In addition to using the `crypto` module, JavaScript provides other ways to generate random numbers:

1. **`Math.random()`**: A built-in function that generates a pseudo-random number between 0 (inclusive) and 1 (exclusive). However, this method is not suitable for cryptographic purposes due to its predictability.
2. **`Random` class**: Some browsers provide a `Random` class or API for generating random numbers. This might be used in some benchmarks as an alternative to the `crypto` module.

Keep in mind that these alternatives have different performance characteristics and may not be suitable for all use cases, especially those requiring high-quality randomness like cryptography.

Overall, this benchmark helps measure the performance of JavaScript engines' implementation of the `crypto.getRandomValues()` function, which is essential for applications relying on random number generation.

Related benchmarks:

Simple benchmark (version: 0)

Comparing performance of: 32 vs Max vs 8

Created: 2 years ago by: Guest

Jump to the latest result

32

Max

8

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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