Benchmark: JS BigInt big number performance assignment

Script Preparation code:

var bigInt1 = 1000000n
var bigInt2 = 50n
var number1 = 1000000
var number2 = 50

Tests:

BigInt
a = bigInt1 b = bigInt2 c = a c = b
Number
a = number1 b = number2 c = a c = b

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
BigInt
Number

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Android 12; Mobile; rv:136.0) Gecko/136.0 Firefox/136.0

Browser/OS: Firefox Mobile 136 on Android

View result in a separate tab

Test name	Executions per second
BigInt	119454032.0 Ops/sec
Number	140222160.0 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks.

**Benchmark Purpose**

The provided JSON represents a benchmark that compares the performance of two data types in JavaScript: `Number` and `BigInt`. Specifically, it tests how these data types handle large numbers.

**Options Compared**

Two options are compared:

1. **Number**: This is the traditional JavaScript number type, which uses 32-bit or 64-bit floating-point arithmetic depending on the platform.
2. **BigInt**: This is a relatively new addition to JavaScript (introduced in ECMAScript 2020) that provides support for arbitrarily large integers.

**Pros and Cons**

**Number**:

Pros:

* Faster execution speed due to less memory allocation and pointer manipulation required
* Smaller code size, making it more suitable for embedded or resource-constrained environments

Cons:

* Limited range (typically ± 2^53 - 1) and precision (usually around 17-18 decimal digits), which can lead to rounding errors or overflow issues with very large numbers.
* May exhibit slower performance with large inputs due to the limitations mentioned above.

**BigInt**:

Pros:

* Supports arbitrary-precision arithmetic, allowing for truly large numbers without worrying about overflow or rounding errors
* Improved precision and range compared to traditional number types

Cons:

* Generally slower execution speed due to more memory allocation, pointer manipulation, and parsing required.
* Larger code size, which may be a concern in resource-constrained environments.

**Library Usage**

None of the provided benchmark cases use any external libraries. The script preparation code only includes JavaScript syntax elements, with no third-party dependencies or imports.

**Special JS Features/Syntax**

There are no special JavaScript features or syntax used in these benchmarks that would require specific knowledge to understand. The tests focus solely on comparing the performance of `Number` and `BigInt` data types.

**Alternatives**

If you're looking for alternatives, here are a few:

1. **Benchmarking libraries**: Tools like BenchmarkJS, Microbenchmark, or jsperf can help you write more robust and efficient benchmarks.
2. **Different test cases**: You could create additional tests to explore other aspects of JavaScript performance, such as:
	* Comparing the performance of various algorithms (e.g., sorting, searching) on different data types.
	* Examining the impact of memory allocation or garbage collection on performance.
3. **Benchmarks for specific use cases**: Create benchmarks tailored to specific industries or applications, like:
	* Cryptography benchmarks for evaluating cryptographic library performance.
	* Gaming benchmarks for optimizing game performance.

Keep in mind that each benchmark has its own unique requirements and considerations, so it's essential to tailor your approach to the specific problem you're trying to solve.

Related benchmarks:

JS BigInt big number performance assignment (version: 0)

Compare Number vs BigInt for big numbers

Comparing performance of: BigInt vs Number

Created: 7 months ago by: Guest

Jump to the latest result

BigInt

Number

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):