Benchmark: repeated Math.random() vs crypto.getRandomValues()

Script Preparation code:

var v = new Uint32Array(16384);

Tests:

Math.random()
for(let i = 0; i < 16384; ++i) v[i] = 4294967296 * Math.random();
crypto.getRandomValues()
crypto.getRandomValues(v);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Math.random()
crypto.getRandomValues()

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one month ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36

Browser/OS: Chrome 130 on Windows

View result in a separate tab

Test name	Executions per second
Math.random()	172.2 Ops/sec
crypto.getRandomValues()	26528.7 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down what is being tested in the provided JSON benchmark.

**What is being tested:**

The benchmark measures the performance difference between two approaches to generate random numbers:

1. **Math.random()**: This is a built-in JavaScript function that generates a random number between 0 (inclusive) and 1 (exclusive). It's used extensively in web development for generating pseudo-random numbers.
2. **crypto.getRandomValues()**: This is a Web Cryptography API function that generates cryptographically secure pseudo-random numbers.

**Options compared:**

The benchmark compares the performance of these two approaches using the same input data:

* For each approach, 16,384 (16384) random values are generated and stored in a `Uint32Array` called `v`.
* The number of executions per second is measured for each approach over multiple runs.

**Pros and cons:**

1. **Math.random()**: This approach has the advantage of being extremely simple to implement and understand, as it's a built-in JavaScript function. However, its performance can be poor when generating large numbers of random values in sequence, as it relies on an algorithm that is not designed for high-performance use.
2. **crypto.getRandomValues()**: This approach provides cryptographically secure pseudo-random numbers, which are suitable for applications requiring high security, such as encryption and random number generation. However, its implementation can be more complex than `Math.random()`, especially when dealing with different platforms.

**Other considerations:**

* The benchmark assumes that the input data (16,384 values) is large enough to demonstrate a significant performance difference between the two approaches.
* The use of a `Uint32Array` as the output buffer ensures that the generated random numbers are uniformly distributed and have the same range.
* The benchmark doesn't account for potential differences in performance due to platform-specific optimizations or caching.

**Library/libraries:**

None explicitly mentioned, but the Web Cryptography API (`crypto`) is used to generate cryptographically secure pseudo-random numbers.

**Special JS feature/syntax:**

Not applicable in this simple benchmark.

Now that we've broken down what's being tested, let's talk about alternative approaches:

1. **other algorithms**: Other random number generation algorithms, such as the Mersenne Twister or the Linear Congruential Generator, could be used instead of `Math.random()` and `crypto.getRandomValues()`.
2. **Non-Web Cryptography API methods**: For generating cryptographically secure pseudo-random numbers, other APIs like the Windows API (in .NET) or the OpenSSL library (in C++) could be used.
3. **Library alternatives**: Other libraries, such as the `random-js` library, could be used to generate random numbers instead of relying on built-in JavaScript functions.

Keep in mind that these alternative approaches would likely have different pros and cons compared to the original benchmark.

Related benchmarks:

repeated Math.random() vs crypto.getRandomValues() (version: 0)

Comparing performance of: Math.random() vs crypto.getRandomValues()

Created: 4 years ago by: Registered User

Jump to the latest result

Math.random()

crypto.getRandomValues()

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):