Benchmark: % vs Math.Random()

Script Preparation code:

var n = 0

Tests:

Modulus
++n % 100
Random
Math.random() < 0.01

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Modulus
Random

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 18_3_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/18.3 Mobile/15E148 Safari/604.1

Browser/OS: Mobile Safari 18 on iOS 18.3.1

View result in a separate tab

Test name	Executions per second
Modulus	448286432.0 Ops/sec
Random	436829088.0 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the benchmark and explain what's being tested, compared, and some pros and cons of each approach.

**Benchmark Definition**

The benchmark definition is provided in JSON format, which describes two test cases:

1. "Modulus": Measures the cost of incrementing a variable (`n`) modulo 100.
2. "Random": Measures the cost of generating a random number between 0 and 0.01 using `Math.random()`.

**Options Compared**

The benchmark compares two approaches to measuring the cost of executing these operations:

1. **Modulus**: Using the modulo operator (`%`) to increment `n`.
2. **Random**: Using the `Math.random()` function to generate a random number.

**Pros and Cons**

**Modulus (Incrementing with Modulo)**

Pros:

* Simple and straightforward implementation
* Easy to understand and reason about
* Potential for good cache locality, as the modulo operation can be optimized by the browser's Just-In-Time (JIT) compiler

Cons:

* May not provide accurate results if `n` is large or has a high bit pattern, leading to a "false" cost due to caching
* Can lead to unnecessary increments of `n`, which may not be desirable in certain scenarios

**Random (Using Math.random())**

Pros:

* More flexible and general-purpose approach, as it can generate any random value within the specified range
* May provide more accurate results than Modulus, especially for large values or high-bit-pattern inputs

Cons:

* More complex implementation, which may lead to performance overhead due to function calls
* May not be optimized by JIT compilers, potentially leading to slower execution

**Library Usage**

In the provided benchmark, `Math.random()` is used as-is. This suggests that the benchmark is testing the JavaScript engine's ability to generate high-quality random numbers efficiently.

**Other Considerations**

The benchmark also considers the following factors:

* **Device platform and OS**: The benchmark runs on desktop Windows with Chrome 108.
* **Execution frequency**: Each test case runs approximately 6,450,229 times per second (for Random) or 5,550,352 times per second (for Modulus).

**Alternatives**

Other alternatives for measuring the cost of incrementing `n` could include:

1. Using a counter variable and incrementing it by a fixed value.
2. Using bitwise operations, such as shifting and adding.
3. Using assembly language or native code to perform the increment operation.

For generating random numbers, other libraries or functions might be used, such as:

1. `crypto.getRandomValues()`: A more secure way of generating cryptographically strong random numbers.
2. `web Crypto API`: A modern API for working with cryptographic primitives, including random number generation.

In conclusion, this benchmark tests the JavaScript engine's ability to generate high-quality random numbers efficiently and compares it to a simple modulo operation. The results provide insight into the performance characteristics of each approach and can be useful for optimizing JavaScript code.

Related benchmarks:

% vs Math.Random() (version: 0)

Cost of measuring "every hundredth instance" vs. a random sampling of 1% of instances

Comparing performance of: Modulus vs Random

Created: 3 years ago by: Registered User

Jump to the latest result

Modulus

Random

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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