Benchmark: pre-increment vs post-increment in loops

Script Preparation code:

let i = 0;

Tests:

pre-increment
++i
post-increment
i++
addition assignment operator
i += 1

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
pre-increment
post-increment
addition assignment operator

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 8 months ago)

User agent: Mozilla/5.0 (Android 14; Mobile; rv:143.0) Gecko/143.0 Firefox/143.0

Browser/OS: Firefox Mobile 143 on Android

View result in a separate tab

Test name	Executions per second
pre-increment	247064576.0 Ops/sec
post-increment	221489008.0 Ops/sec
addition assignment operator	176094080.0 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark definition is a simple JavaScript statement that increments a variable `i` by 1. There are three different ways to increment a variable in JavaScript:

*   Pre-increment (`++i`)
*   Post-increment (`i++`)
*   Addition assignment operator (`i += 1`)

These approaches can have different performance implications due to how the JavaScript engine executes them.

**Options Compared**

The benchmark is comparing the execution speed of these three increment operators. Here's a brief overview of each:

*   **Pre-increment (`++i`)**: In this approach, the value of `i` is incremented first, and then the new value is used in the expression. This means that the compiler/interpreter can optimize the code by evaluating the post-increment part before incrementing `i`.
*   **Post-increment (`i++`)**: Here, the expression is evaluated first, and then `i` is incremented. Since the result of the expression is assigned to a new variable (or used in an operation), the compiler/interpreter needs to store the old value of `i` before incrementing it.
*   **Addition assignment operator (`i += 1`)**: In this case, the value of `i` is incremented directly using the addition operator. This approach avoids the need for storing and incrementing `i`, making it potentially faster.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

*   **Pre-increment (`++i`)**: Pros: Can be optimized by the compiler/interpreter. Cons: May have slightly higher overhead due to the need for storing and incrementing `i`.
*   **Post-increment (`i++`)**: Pros: Allows for more flexibility in code usage. Cons: Requires storing and incrementing `i`, which can introduce additional overhead.
*   **Addition assignment operator (`i += 1`)**: Pros: Potentially faster due to reduced overhead from incrementing `i`. Cons: May limit the flexibility of the code.

**Library Used**

None

**Special JS Feature/Syntax**

None (standard JavaScript syntax)

Now, let's discuss alternative approaches:

*   **Using a loop instead of direct increments**: Instead of using pre-increment/post-increment operators, you could use a traditional `for` loop to increment `i`.
*   **Using a different data structure**: Depending on your specific requirements, you might consider using an array or object with indexing to achieve the desired increment behavior.
*   **Profiling and optimization techniques**: You could apply various profiling tools and optimization techniques (e.g., compiler flags, caching) to further optimize the performance of this benchmark.

To run this benchmark in MeasureThat.net, simply fill in the `Script Preparation Code` field with the provided JavaScript code (`let i = 0;`) and set up your test cases according to the individual test case structure.

Related benchmarks:

pre-increment vs post-increment in loops (version: 1)

Comparing performance of: pre-increment vs post-increment vs addition assignment operator

Created: 7 years ago by: Registered User

Jump to the latest result

pre-increment

post-increment

addition assignment operator

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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