Benchmark: Closure overhead vs function overhead vs inline

Script Preparation code:

var NUM_ITERATIONS = 1000000

var state = [987654321, 987654321, 987654321, 987654321];


var baz_inner = (state) => {
    state[0] = ((state[0] & 0xFFFFFFFE) << 18) ^ (((state[0] << 6) ^ state[0]) >> 13);
    state[1] = ((state[1] & 0xFFFFFFF8) << 2) ^ (((state[1] << 2) ^ state[1]) >> 27);
    state[2] = ((state[2] & 0xFFFFFFF0) << 7) ^ (((state[2] << 13) ^ state[2]) >> 21);
    state[3] = ((state[3] & 0xFFFFFF80) << 13) ^ (((state[3] << 3) ^ state[3]) >> 12);
}

Tests:

Inline
state = [987654321, 987654321, 987654321, 987654321]; for (let i = 0; i < NUM_ITERATIONS; i++) { state[0] = ((state[0] & 0xFFFFFFFE) << 18) ^ (((state[0] << 6) ^ state[0]) >> 13); state[1] = ((state[1] & 0xFFFFFFF8) << 2) ^ (((state[1] << 2) ^ state[1]) >> 27); state[2] = ((state[2] & 0xFFFFFFF0) << 7) ^ (((state[2] << 13) ^ state[2]) >> 21); state[3] = ((state[3] & 0xFFFFFF80) << 13) ^ (((state[3] << 3) ^ state[3]) >> 12); }
IIFE
state = [987654321, 987654321, 987654321, 987654321]; for (let i = 0; i < NUM_ITERATIONS; i++) { (() => { state[0] = ((state[0] & 0xFFFFFFFE) << 18) ^ (((state[0] << 6) ^ state[0]) >> 13); state[1] = ((state[1] & 0xFFFFFFF8) << 2) ^ (((state[1] << 2) ^ state[1]) >> 27); state[2] = ((state[2] & 0xFFFFFFF0) << 7) ^ (((state[2] << 13) ^ state[2]) >> 21); state[3] = ((state[3] & 0xFFFFFF80) << 13) ^ (((state[3] << 3) ^ state[3]) >> 12); })(); }
External function
state = [987654321, 987654321, 987654321, 987654321]; for (let i = 0; i < NUM_ITERATIONS; i++) { baz_inner(state); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Inline
IIFE
External function

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's break down the provided JSON and explain what is being tested.

**Benchmark Overview**

The benchmark compares the performance of three approaches:

1. **Inline**: The code is inlined, meaning it's directly inserted into the loop without any function calls or declarations.
2. **IIFE (Immediately Invoked Function Expression)**: A function is defined immediately and then called without any arguments. This creates a new scope and executes the code inside the IIFE as soon as it's created.
3. **External function**: The `baz_inner` function is declared separately from the loop, and its name is passed to the loop as an argument.

**Pros and Cons of each approach**

1. **Inline**:
	* Pros: Reduced overhead due to fewer function calls and no need for scope creation.
	* Cons: Increased code size, potential impact on cache locality, and difficulty in maintaining separate scopes for different variables.
2. **IIFE**:
	* Pros: Encapsulates the code within its own scope, reducing interference with outer scopes, and allows for easier management of side effects.
	* Cons: Creates a new scope, which can lead to increased overhead due to the creation and garbage collection of the new scope.
3. **External function**:
	* Pros: Separates the code from the loop, making it easier to maintain and debug. Also, reduces the impact of scope creation and garbage collection.
	* Cons: Requires an additional function declaration, which can increase code size.

**Library usage**

None of the provided benchmarks explicitly use any external libraries or frameworks that would affect their performance.

**Special JS features**

No special JavaScript features are used in this benchmark. The focus is solely on the three approaches to measuring performance differences.

**Alternatives**

Other alternatives for benchmarking and measuring performance differences include:

* **Function call overhead only**: Measure the performance difference between a function call with no arguments versus one with an argument.
* **Closure optimization techniques**: Explore different closure optimization techniques, such as tail call optimization or recursive function calls with memoization.
* **Parallel execution**: Run multiple instances of each benchmark in parallel to measure speedup benefits of multi-threading or concurrent processing.

These alternatives can provide a more comprehensive understanding of performance differences and potential optimizations for specific use cases.

Related benchmarks:

Closure overhead vs function overhead vs inline (version: 0)

Runs a pseudo random number generator in a loop. To determine overhead from IIFE in a loop versus function call overhead

Comparing performance of: Inline vs IIFE vs External function

Created: 3 years ago by: Guest

Jump to the latest result

Inline

IIFE

External function

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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