Benchmark: multie params vs object

Script Preparation code:

function sum(a,b){return a + b}
function sumObj(o){return o.a + o.b}

Tests:

sum multi params
for (let i = 0; i < 100000; i++){ sum(Math.random(), Math.random()) }
sum single param
for (let j = 0; j < 100000; j++){ sumObj({ a: Math.random(), b: Math.random() }) }
sum random param o
for (let k = 0; k < 100000; k++){ if (k % 2 === 0) { sumObj({ a: Math.random(), b: Math.random() }) } else { sumObj({ b: Math.random(), a: Math.random() }) } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
sum multi params
sum single param
sum random param o

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Safari/537.36

Browser/OS: Chrome 121 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
sum multi params	67.9 Ops/sec
sum single param	68.1 Ops/sec
sum random param o	68.2 Ops/sec

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

LLMs can make mistakes. Check important info.

I'd be happy to explain the provided JSON benchmark definition and its test cases.

**Benchmark Definition**

The JSON defines a benchmark called "multie params vs object" with two scripts for preparation: `sum(a,b)` and `sumObj(o)`. The HTML preparation code is empty, indicating that no additional HTML is needed for this benchmark. The script definitions are simple functions that take two arguments (`a` and `b` or an object `o`) and return their sum.

**Options Compared**

The benchmark compares the performance of three different approaches:

1. **Multi-argument function**: The first test case, "sum multi params", uses a traditional function with multiple arguments (`function sum(a,b){return a + b}`). This approach passes two random numbers as arguments to the `sum` function.
2. **Object-based function**: The second test case, "sum single param", uses an object-based function (`function sumObj(o){return o.a + o.b}`). This approach creates an object with two properties (`a` and `b`) and passes it to the `sumObj` function.
3. **Conditional object creation**: The third test case, "sum random param o", uses a conditional statement to create an object randomly (`if (k % 2 === 0) {\tsumObj({ a: Math.random(), b: Math.random() })\n} else {\sumObj({ b: Math.random(), a: Math.random() })}`). This approach creates an object with random values for `a` and `b`, but only if the current iteration is even. If it's odd, the object creation is delayed until the next iteration.

**Pros and Cons of Each Approach**

1. **Multi-argument function**: Pros:
	* Simple and easy to understand
	* Fast execution since no overhead from object creation or lookups
Cons:
	* Passing arguments can be slower due to argument passing mechanisms
2. **Object-based function**: Pros:
	* Can take advantage of object caching and lookups for faster access
Cons:
	* Object creation can introduce additional overhead
3. **Conditional object creation**: Pros:
	* Can leverage the benefits of object caching and lookups while avoiding unnecessary object creation
Cons:
	* Conditional statements can introduce additional overhead due to branching

**Library and Special JS Features**

None of the test cases use any libraries, but they do utilize some special JavaScript features:

1. **`Math.random()`**: A built-in function that returns a random number between 0 (inclusive) and 1 (exclusive).
2. **`Object creation with properties`**: The `sumObj` function creates an object with two properties (`a` and `b`) using the syntax `{ a: Math.random(), b: Math.random() }`.

**Other Alternatives**

To measure this benchmark, you can consider alternative approaches such as:

1. Using a profiling tool like V8 Inspector (Chrome DevTools) or Webpack's built-in profiler.
2. Implementing a custom microbenchmarking framework in JavaScript, similar to what MeasureThat.net provides.

Keep in mind that the choice of approach depends on your specific use case and requirements.

Related benchmarks:

multie params vs object (version: 0)

Comparing performance of: sum multi params vs sum single param vs sum random param o

Created: 2 years ago by: Guest

Jump to the latest result

sum multi params

sum single param

sum random param o

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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