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Simple benchmark
(version: 0)
Comparing performance of:
32 vs Max vs 8
Created:
2 years ago
by:
Guest
Go to the latest result
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>
Run tests (3)
Previous results
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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
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Embed Benchmark Result
<iframe src="https://measurethat.net/Embed?id=517526" width="100%" height="500px"></iframe>
32
152K/s
8
151K/s
Max
2K/s
View exact numbers
Test name
Executions per second
✓
32
152,091 Ops/sec
8
150,992 Ops/sec
Max
2,002 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
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.
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