Benchmark: Function execution time check for memoization

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.4/lodash.min.js"></script>

Tests:

Without memoizastion
const getSettingValues = config => config.values; const showPrimaryProfile = config => { const enabledChannels = _.union(getSettingValues(config), ['a']); return _.includes(enabledChannels, ['b']); } _.times(1000, () => { showPrimaryProfile({values: ['z']}); })
With Memoization
const getSettingValues = config => config.values; const getEnabledChannels = _.memoize(config => _.union(config, ['a'])) const showPrimaryProfile = config => { const enabledChannels = getEnabledChannels(getSettingValues(config)); return _.includes(enabledChannels, ['b']); } _.times(1000, () => { showPrimaryProfile({values: ['z']}); })

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Without memoizastion
With Memoization

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's break down what's being tested on MeasureThat.net.

The provided JSON represents two benchmark test cases for measuring the execution time of JavaScript functions with memoization.

**Memoization**: Memoization is an optimization technique where the result of a function is cached and reused when the same inputs occur again. This can significantly speed up performance-critical code by avoiding redundant calculations.

**Options being compared:**

1. **Without memoization**: The first test case measures the execution time of the `showPrimaryProfile` function without using memoization.
2. **With memoization**: The second test case measures the execution time of the same `showPrimaryProfile` function, but with memoization enabled through the use of the `_memoize` function from the Lodash library.

**Pros and cons:**

* **Without memoization**: This approach does not cache results, which means that each call to `showPrimaryProfile` will recalculate the entire expression, leading to slow performance. However, this approach is simple to implement and understand.
* **With memoization**: This approach caches results, which can significantly speed up performance-critical code. However, memoization also introduces additional overhead for caching and updating cache entries.

**Lodash library:**

The Lodash library is a popular JavaScript utility library that provides various functions for tasks such as array manipulation, object iteration, and more. In this benchmark, the `_memoize` function is used to enable memoization in the `showPrimaryProfile` function.

* **Purpose of _memoize**: The `_memoize` function takes a function as an argument and returns a new function that caches the result of the original function. When the same inputs are passed again, the cached result is returned instead of recalculating it.

**Special JS feature or syntax:**

There is no special JavaScript feature or syntax being tested in this benchmark. The code uses standard JavaScript syntax and libraries (Lodash) to demonstrate memoization.

Other alternatives:

* If memoization were not an option, alternative optimizations could include:
	+ Caching results using a hash table or object.
	+ Using a just-in-time (JIT) compiler to optimize performance-critical code.
	+ Breaking down complex functions into smaller, more manageable pieces.
* If the `showPrimaryProfile` function were rewritten without memoization, other approaches might include:
	+ Recalculating the entire expression on each call.
	+ Using a different data structure (e.g., a tree or graph) to represent the calculation.

Overall, this benchmark demonstrates the benefits of memoization in improving performance-critical code execution times.

Related benchmarks:

Function execution time check for memoization (version: 0)

Comparing performance of: Without memoizastion vs With Memoization

Created: 6 years ago by: Guest

Jump to the latest result

Without memoizastion

With Memoization

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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