Benchmark: powoaetuheu12321

Tests:

1.1**10
var x = Math.pow(1.1,10);
1.1**100
var x = Math.pow(1.1,100);
1.1**1000
var x = Math.pow(1.1,1000);
2**1000
var x = Math.pow(2,1000);
1.1**10000
var x = Math.pow(1.1,10000);
73**10000
var x = Math.pow(73,10000);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
1.1**10
1.1**100
1.1**1000
2**1000
1.1**10000
73**10000

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36

Browser/OS: Chrome 87 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
1.1**10	8457632.0 Ops/sec
1.1**100	8365982.5 Ops/sec
1.1**1000	8353663.0 Ops/sec
2**1000	8379389.0 Ops/sec
1.1**10000	8386998.0 Ops/sec
73**10000	8474376.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of MeasureThat.net and explore what's being tested on this provided JSON.

**What is being tested?**

MeasureThat.net is designed to test the performance of JavaScript engines in various mathematical operations, specifically exponentiation (`Math.pow()`). The benchmark consists of a series of individual test cases, each representing a specific exponentiation operation (e.g., `1.1**10`, `2**1000`, etc.). These test cases are intended to measure how efficiently different browsers and devices can execute these operations.

**Options compared**

The main options being compared in this benchmark are:

* Different bases for the exponentiation operation (e.g., 1.1, 2)
* Different exponents (10, 100, 1000, etc.)

These variations aim to identify performance differences between different browsers and devices under varying conditions.

**Pros and cons of each approach**

* Using different bases:
	+ Pros: Helps identify performance differences in handling different mathematical operations.
	+ Cons: May not be representative of real-world usage patterns, as many applications use more complex exponentiation formulas.
* Using different exponents:
	+ Pros: Allows for a wider range of test cases and helps identify performance differences at different scales.
	+ Cons: May introduce unnecessary variations that don't accurately reflect real-world scenarios.

**Library usage**

In the provided benchmark definition JSON, the `Math.pow()` function is used to perform exponentiation operations. The `Math` library is a built-in JavaScript library that provides various mathematical functions, including `pow()`. Its purpose is to provide a way to raise a number to a power.

**Special JS features or syntax**

There are no special JavaScript features or syntax mentioned in the provided benchmark definition JSON. However, it's worth noting that MeasureThat.net might use other libraries or tools for tasks like HTML preparation code or device platform detection, but these are not relevant to the specific benchmark being tested.

**Other alternatives**

If you're interested in exploring alternative approaches to benchmarking JavaScript performance, consider:

* WebPageTest: A comprehensive tool for measuring web page load times and performance across various browsers and devices.
* Browserbench: A benchmarking tool specifically designed for comparing browser performance.
* JSPerf: A popular benchmarking tool for testing JavaScript performance.

Keep in mind that each of these tools has its own strengths and weaknesses, and the choice ultimately depends on your specific use case and requirements.

Related benchmarks:

powoaetuheu12321 (version: 0)

Comparing performance of: 1.110 vs 1.1100 vs 1.11000 vs 21000 vs 1.110000 vs 7310000

Created: 4 years ago by: Guest

Jump to the latest result

1.1**10

1.1**100

1.1**1000

2**1000

1.1**10000

73**10000

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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

powoaetuheu12321 (version: 0)

Comparing performance of: 1.1**10 vs 1.1**100 vs 1.1**1000 vs 2**1000 vs 1.1**10000 vs 73**10000

Created: 4 years ago by: Guest

Jump to the latest result

1.1**10

1.1**100

1.1**1000

2**1000

1.1**10000

73**10000

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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

Comparing performance of: 1.110 vs 1.1100 vs 1.11000 vs 21000 vs 1.110000 vs 7310000