Benchmark: compose_check - MeasureThat.net

Tests:

add2
function add2(x){ return (x + 1) + 1; } let res = 0; for(let i = 0; i < 10000; i++){ res = add2(res) }
add1_twice
function add1(x){ return x + 1; } function add1_twice(x){ return add1(add1(x)); } let res = 0; for(let i = 0; i < 10000; i++){ res = add1_twice(res) }
add1_twice_2
function add1(x){ return x + 1; } function twice(fn){ return function(x){ return fn(fn(x)); }; } const add1_twice_2 = twice(add1); let res = 0; for(let i = 0; i < 10000; i++){ res = add1_twice_2(res) }
add2_arrow
const add2 = (x) => (x + 1) + 1; let res = 0; for(let i = 0; i < 10000; i++){ res = add2(res) }
add1_twice_arrow
const add1 = (x) => x + 1; const add1_twice = (x) => add1(add1(x)); let res = 0; for(let i = 0; i < 10000; i++){ res = add1_twice(res) }
add1_twice_2_arrow
const add1 = (x) => x + 1; const twice = (f) => (x) => f(f(x)); const add1_twice = twice(add1); let res = 0; for(let i = 0; i < 10000; i++){ res = add1_twice(res) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
add2
add1_twice
add1_twice_2
add2_arrow
add1_twice_arrow
add1_twice_2_arrow

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (X11; CrOS x86_64 13982.39.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.82 Safari/537.36

Browser/OS: Chrome 92 on Chrome OS 13982.39.0

View result in a separate tab

Test name	Executions per second
add2	158454.1 Ops/sec
add1_twice	81684.4 Ops/sec
add1_twice_2	106272.8 Ops/sec
add2_arrow	160030.5 Ops/sec
add1_twice_arrow	30064.9 Ops/sec
add1_twice_2_arrow	106196.9 Ops/sec

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

LLMs can make mistakes. Check important info.

Measuring the performance of JavaScript code is crucial for optimizing and fine-tuning our applications.

Let's break down the provided benchmark definition and test cases to understand what's being tested.

**Benchmark Definition**

The `compose_check` benchmark definition is empty, which means that it doesn't specify any particular function or logic to be executed.

**Test Cases**

There are five test cases:

1. **add2**: This test case defines a simple function `add2(x)` that returns `(x + 1) + 1`. The test executes this function in a loop, appending the result to another variable `res`, for a total of 10,000 iterations.
2. **add1_twice**: This test case defines two functions: `add1(x)` and `add1_twice(res)`. `add1(x)` simply returns `x + 1`, while `add1_twice(res)` calls `add1` twice in a row on the input `res`.
3. **add1_twice_2**: This test case defines two functions: `add1(x)` and `twice(fn)`. The `twice` function takes another function as an argument, creates a new function that calls itself with the original argument twice, and returns this new function.
4. **add2_arrow**: Similar to `add2`, but defined using arrow function syntax (`(x) => (x + 1) + 1;`). This is equivalent to a traditional function definition (`function add2(x){...}`)
5. **add1_twice_arrow**: Similar to `add1_twice`, but defined using arrow function syntax.

**Comparison**

The main difference between these test cases lies in how the functions are composed and executed:

* `add2` and `add2_arrow` execute a simple arithmetic operation in a loop.
* `add1_twice` executes two calls to `add1` on the input `res`.
* `add1_twice_2` uses a higher-order function (`twice`) to create a new function that calls itself twice.
* `add1_twice_arrow` uses arrow function syntax to define and call the `add1_twice` logic.

**Pros and Cons**

Here's a brief overview of each approach:

* **Simple loops**: These tests are straightforward and easy to understand, but they don't require any special consideration for function composition or higher-order functions.
* **Function composition**: Tests like `add1_twice_2` demonstrate how to create new functions by composing existing ones. This is a fundamental concept in functional programming.
* **Arrow function syntax**: Using arrow functions can simplify code and make it more concise, but it's essential to consider the nuances of this syntax when defining complex functions.

**Performance Results**

The performance results show that the `add2_arrow` test case is slightly faster than the traditional `add2` implementation. This might be due to the optimizations provided by the JavaScript engine for arrow function expressions. However, the differences are relatively small, and other factors like caching, optimization, and platform-specific behavior can affect the actual performance results.

**Conclusion**

In conclusion, this benchmark highlights the importance of understanding how functions are composed and executed in JavaScript. By examining different test cases and their approaches, we can gain insights into the trade-offs between simplicity, conciseness, and performance.

Related benchmarks:

compose_check (version: 0)

Comparing performance of: add2 vs add1_twice vs add1_twice_2 vs add2_arrow vs add1_twice_arrow vs add1_twice_2_arrow

Created: 3 years ago by: Guest

Jump to the latest result

add2

add1_twice

add1_twice_2

add2_arrow

add1_twice_arrow

add1_twice_2_arrow

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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