Toggle navigation
MeasureThat
.net
Create a benchmark
Tools
Feedback
FAQ
Register
Log In
repeated Math.random() vs crypto.getRandomValues()
(version: 0)
Comparing performance of:
Math.random() vs crypto.getRandomValues()
Created:
5 years ago
by:
Registered User
Go to the latest result
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>
Run tests (2)
Previous results
Fork
Test case name
Result
Math.random()
crypto.getRandomValues()
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
one year 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
Embed
Embed Benchmark Result
<iframe src="https://measurethat.net/Embed?id=566799" width="100%" height="500px"></iframe>
crypto.getRandomValues()
27K/s
Math.random()
172/s
View exact numbers
Test name
Executions per second
✓
crypto.getRandomValues()
26,529 Ops/sec
Math.random()
172 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
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
Math.random vs crypto.getRandom
Math.random vs crypto.getRandom Alberto
crypto.getRandomValues vs Math.random()
Comments
Confirm delete:
Do you really want to delete benchmark?