Benchmark: Object passing vs shared array context.

Script Preparation code:

var ctx = new Array(2);

function addObject(obj) {
  return obj.a + obj.b;
}

function addCtx() {
  return ctx[0] + ctx[1];
}

Tests:

Using Object
addObject({ a: Math.random(), b: Math.random() });
Using shared context
ctx[0] = Math.random(); ctx[1] = Math.random(); addCtx();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Using Object
Using shared context

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 7 months ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36

Browser/OS: Chrome 123 on Linux

View result in a separate tab

Test name	Executions per second
Using Object	2753099.8 Ops/sec
Using shared context	1626143.8 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and its various components.

**Benchmark Definition**

The benchmark is designed to compare the performance of two approaches: passing an object versus using a shared array context in JavaScript.

The script preparation code defines two functions:

* `addObject(obj)`: takes an object as input, adds the values of its properties (`a` and `b`) together, and returns the result. The object is created with random values.
* `addCtx()`: takes no arguments, but uses a shared array context to add the first two elements of the `ctx` array together.

**Options Compared**

The benchmark compares the performance of:

1. **Object passing**: `addObject(obj)` function calls are executed separately, where each call creates a new object instance with random values.
2. **Shared array context**: `addCtx()` function calls reuse the same shared array context to calculate the sum.

**Pros and Cons**

### Object Passing

Pros:

* Easier to understand and debug, as each function call operates independently.
* No shared state or potential concurrency issues.

Cons:

* Potential performance overhead due to object creation and garbage collection.
* May lead to slower execution times due to the number of object instances created.

### Shared Array Context

Pros:

* Can provide better performance, especially in hot loops, by reusing existing memory.
* Reduces garbage collection overhead.

Cons:

* More complex to understand and debug, as it involves shared state and potential concurrency issues.
* May introduce bugs or unexpected behavior if not used carefully.

**Library/Functionality Used**

In this benchmark, the `Array` class is used to create a shared array context. The `ctx` variable is initialized with two elements before being reused in the `addCtx()` function calls.

No external libraries are required for this benchmark.

**Special JS Feature/Syntax**

None of the code snippets provided use any special JavaScript features or syntax beyond basic arithmetic operations and object creation.

**Other Alternatives**

If you wanted to explore other alternatives, here are a few options:

1. **Using a closure**: Instead of using a shared array context, you could create a closure that captures the `ctx` variable and returns a new function with its own scope.
2. **Using an immutable data structure**: You could use an immutable data structure like a `Map` or `Set` instead of a shared array to pass around values between functions.
3. **Using a caching mechanism**: If performance is critical, you could consider using a caching mechanism to store the results of expensive function calls and reuse them when possible.

Keep in mind that these alternatives may introduce additional complexity or trade-offs in terms of performance, readability, or maintainability.

Related benchmarks:

Object passing vs shared array context. (version: 0)

Comparing performance of: Using Object vs Using shared context

Created: 3 years ago by: Registered User

Jump to the latest result

Using Object

Using shared context

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):