Benchmark: Million loops - MeasureThat.net

Tests:

1 million
for (let i = 0; i < 1_000_000; ++i) { i + 1; }
10 millions
for (let i = 0; i < 10_000_000; ++i) { i + 1; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
1 million
10 millions

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):

LLMs can make mistakes. Check important info.

I'll break down the benchmark test case and explain what's being tested, compared, and other considerations.

**Benchmark Test Case**

The provided JSON represents a JavaScript microbenchmark that tests the performance of simple loops. The benchmark is defined by two individual test cases:

1. "Million loops" with 1 million iterations.
2. "10 millions" with 10 million iterations.

Each test case has a simple loop using `for` and increments the variable `i` by 1 for each iteration.

**Options Compared**

In this benchmark, we're comparing two options:

1. **Simple Loop**: The original loop implementation using `for`.
2. **No Optimization**: This is likely the baseline or default behavior of JavaScript engines, without any optimizations applied to the loop.
3. **Loop Unrolling**: This involves increasing the number of increments performed in each iteration, which can potentially improve performance by reducing the overhead of function calls and branching.

**Pros and Cons**

* **Simple Loop (Original)**:
	+ Pros: Easy to understand and maintain, doesn't introduce additional complexity or assumptions.
	+ Cons: May be slower due to the inherent nature of the loop increment operation.
* **No Optimization (Baseline)**:
	+ Pros: Serves as a baseline for comparison, represents typical execution behavior without any enhancements.
	+ Cons: Likely slower than optimized implementations.
* **Loop Unrolling**:
	+ Pros: Can potentially improve performance by reducing branch prediction errors and function call overhead.
	+ Cons: May increase code complexity, requires careful tuning to avoid negative effects on readability or maintainability.

**Library Usage**

In the provided benchmark code, there are no explicit libraries mentioned. However, JavaScript engines like V8 (used in Chrome) and SpiderMonkey (used in Firefox) have internal optimizations and features that might influence performance. These include:

* **Just-In-Time (JIT)** compilation: Converts interpreted code into optimized machine code at runtime.
* **Garbage Collection**: Manages memory allocation and deallocation, which can impact performance.

**Special JS Features or Syntax**

There's no explicit mention of any special JavaScript features or syntax in this benchmark. However, some engines like V8 have notable optimizations for certain aspects of the language, such as:

* **Tail Calls**: Allows for function calls to be optimized away, reducing stack overhead.
* **Constant Folding**: Evaluates constant expressions at compile-time to improve performance.

**Alternative Implementations**

For a similar benchmark, you could consider implementing other optimization techniques or alternatives, such as:

1. **Vectorized Loops**: Using libraries like NumJS or MathJS to perform operations on entire arrays or vectors, potentially improving performance by reducing branching and looping overhead.
2. **SIMD Instructions**: Utilizing SIMD (Single Instruction, Multiple Data) instructions on modern CPU architectures to execute the same operation on multiple data points simultaneously.
3. **Parallel Processing**: Dividing the loop into smaller, independent chunks and executing them concurrently using multi-threading or parallel computing frameworks.

Keep in mind that these alternatives might introduce additional complexity and may not be suitable for all scenarios or use cases.

Please note that this explanation is based on general knowledge of JavaScript performance optimization and benchmarking. If you're interested in exploring more advanced techniques, I'd be happy to help!

Latest run results:

Run details: (Test run date: 5 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:124.0) Gecko/20100101 Firefox/124.0

Browser/OS: Firefox 124 on Windows

View result in a separate tab

Test name	Executions per second
1 million	2401.5 Ops/sec
10 millions	238.7 Ops/sec

Related benchmarks:

Million loops (version: 0)

Comparing performance of: 1 million vs 10 millions

Created: 5 months ago by: Guest

Jump to the latest result

1 million

10 millions

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):