Benchmark: inline func - MeasureThat.net

Script Preparation code:

var FN = val => val * 2 + 1;

Tests:

1
var i = 1000; while (i--) { var x = FN(i); }
inline
var i = 1000; while (i--) { var x = (val => val * 2 + 1)(i); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
1
inline

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36

Browser/OS: Chrome 126 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
1	12474.1 Ops/sec
inline	626666.7 Ops/sec

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

LLMs can make mistakes. Check important info.

Measuring the performance of JavaScript code is crucial for understanding how different approaches affect execution speed.

The provided JSON represents two benchmark definitions:

**Benchmark Definition 1: "inline func"**
This benchmark defines an anonymous function `FN` that takes one argument, multiplies it by 2, and adds 1. The script preparation code sets up this function as a variable `FN`. The HTML preparation code is empty.

In this benchmark, we're interested in the performance of calling the `FN` function with different inputs (in this case, a loop that decrements an initial value of 1000).

**Benchmark Definition 2: "inline"**
This benchmark is similar to the previous one but uses a named function instead of an anonymous one. The script preparation code defines a function `fn` and assigns it to the same variable name as before (`FN`). However, in this case, the HTML preparation code is different from the first benchmark.

**Library and Purpose:**
In both benchmarks, there is no explicit library mentioned. However, we can infer that the JavaScript engine or interpreter used by the test browser (Chrome 126) likely provides some form of function invocation optimization, such as just-in-time (JIT) compilation or caching.

**Special JS feature/Syntax:**
There are a few special aspects to note:

*   **Arrow functions**: In Benchmark Definition 2, the `FN` variable is defined using an arrow function (`val => val * 2 + 1`). This syntax is supported by modern JavaScript engines but can have performance implications due to the way it's evaluated (e.g., no explicit declaration of the function body).
*   **Named vs. anonymous functions**: Both benchmarks use named and anonymous functions, which can affect performance depending on how the engine or interpreter handles them.

**Options compared:**

We're comparing two approaches:

1.  Using an **anonymous function** (`val => val * 2 + 1`) versus a **named function** (`var FN = val => val * 2 + 1;`).
2.  Directly calling the `FN` variable using its name (`FN(i)`) versus passing it as a parameter to another anonymous function (`(val => val * 2 + 1)(i)`).

**Pros and Cons of each approach:**

1.  **Anonymous functions vs. named functions**: Using an anonymous function can be beneficial if the function is only used once or in a specific context, reducing memory allocation overhead. However, it may lead to slower performance due to the lack of JIT compilation.
2.  **Direct function call vs. parameter passing**: Passing `FN` as a parameter might offer better performance by allowing the engine or interpreter to optimize the invocation process. This approach also reduces the need for variable lookup.

**Other considerations:**

*   **Cache locality**: The way variables are stored in memory can impact cache locality and performance.
*   **Loop unrolling**: Optimizations like loop unrolling (increasing the number of iterations) can lead to better performance but require more complex code and potentially introduce new challenges.

**Alternatives:**
To further compare or explore different approaches, you could consider the following alternatives:

*   Using other programming languages or libraries that provide similar functionality.
*   Adding more benchmarks for different JavaScript features or engines (e.g., Node.js vs. browser-based engines).
*   Investigating specific optimization techniques, such as loop unrolling, caching, or function inlining.

Keep in mind that performance optimizations often involve trade-offs between code complexity, memory usage, and execution speed.

Related benchmarks:

inline func (version: 0)

Comparing performance of: 1 vs inline

Created: one year ago by: Registered User

Jump to the latest result

1

inline

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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