Benchmark: memoizer - MeasureThat.net

Tests:

Memoizer

function memoizer(func) {
  const cache = {};
  return function() {
    const key = JSON.stringify(arguments);

    if (cache[key] !== undefined) {
      return cache[key];
    }
    const result = func(...arguments);
    cache[key] = result;
    return result;
  };
}

const fibonacci = n => {
  if (n <= 1) return 1;
  return fibonacci(n - 1) + fibonacci(n - 2);
};

const cachedFibonacci = memoizer(fibonacci);

const getCachedFibonacci = (limit = 1) => {
  const arr = [];
  for (let i = 0; i <= limit; i++) {
    arr.push(cachedFibonacci(i));
  }
  return arr;
};

getCachedFibonacci(30)

​x
 
function memoizer(func) {  const cache = {};  return function() {    const key = JSON.stringify(arguments);​    if (cache[key] !== undefined) {      return cache[key];    }    const result = func(...arguments);    cache[key] = result;    return result;  };}​const fibonacci = n => {  if (n <= 1) return 1;  return fibonacci(n - 1) + fibonacci(n - 2);};​const cachedFibonacci = memoizer(fibonacci);​const getCachedFibonacci = (limit = 1) => {  const arr = [];  for (let i = 0; i <= limit; i++) {    arr.push(cachedFibonacci(i));  }  return arr;};​getCachedFibonacci(30)

No Memoizer

const fibonacci = n => {
    if (n <= 1) return 1;
    return fibonacci(n - 1) + fibonacci(n - 2);
};

const getFibonacci = (limit = 1) => {
    const arr = [];
    for (let i = 0; i <= limit; i++) {
        arr.push(fibonacci(i));
    }
    return arr;
};
getFibonacci(30)

 
const fibonacci = n => {    if (n <= 1) return 1;    return fibonacci(n - 1) + fibonacci(n - 2);};​const getFibonacci = (limit = 1) => {    const arr = [];    for (let i = 0; i <= limit; i++) {        arr.push(fibonacci(i));    }    return arr;};getFibonacci(30)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Experimental features:

Memory measurements supported only in Chrome.
For precise memory measurements Chrome must be launched with --enable-precise-memory-info flag.
More information: Monitoring JavaScript Memory

Test case name	Result
Memoizer
No Memoizer

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/86.0.4240.75 Safari/537.36

Browser/OS: Chrome 86 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Memoizer	17.2 Ops/sec
No Memoizer	17.2 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

Let's break down the provided benchmark and its results.

Benchmark Overview

The benchmark tests two different approaches to caching the Fibonacci sequence in JavaScript:

With Memoization: The memoizer function wraps another function (fibonacci) with caching capabilities, storing previously computed values in a cache object.
Without Memoization: A simple implementation of the Fibonacci sequence without any caching.

Comparison

The two approaches are compared in terms of their performance on different browsers and devices.

Memoization: The memoizer function caches the results of expensive function calls, making it more efficient for repeated computations. This approach is particularly beneficial when dealing with recursive functions like the Fibonacci sequence.
Without Memoization: Without caching, each call to the fibonacci function computes the result from scratch, leading to a significant increase in execution time.

Pros and Cons

Memoization: Pros:

Improved performance for repeated computations
Reduced overhead due to caching

Cons:

Additional memory usage due to cache storage
Potential issues with cache invalidation or expiration

Without Memoization: Pros:

No additional memory usage
Simplified codebase

Cons:

Poor performance for repeated computations
Increased execution time

Library and Special JS Features

There is no explicit library mentioned in the benchmark, but it's worth noting that the memoizer function uses a simple object caching mechanism.

As for special JavaScript features, there are none explicitly mentioned or used in this benchmark. However, if you were to modify the code, you could explore using modern JavaScript features like:

Generators: To implement the Fibonacci sequence more efficiently.
Async/Await: To make the code more readable and easier to maintain.

Alternative Approaches

Other alternatives for caching the Fibonacci sequence might include:

Using a dedicated caching library (e.g., LRU Cache)
Implementing a more advanced cache invalidation strategy
Utilizing parallel processing or multi-threading techniques

Keep in mind that these alternatives would depend on specific performance requirements and constraints.

Benchmark Preparation Code

The provided benchmark preparation code is minimal, as it only defines the two test cases:

memoizer function with caching
Simple Fibonacci sequence without caching

This simplicity allows for a clean comparison between the two approaches.

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and its results.

**Benchmark Overview**

The benchmark tests two different approaches to caching the Fibonacci sequence in JavaScript:

1. **With Memoization**: The `memoizer` function wraps another function (`fibonacci`) with caching capabilities, storing previously computed values in a cache object.
2. **Without Memoization**: A simple implementation of the Fibonacci sequence without any caching.

**Comparison**

The two approaches are compared in terms of their performance on different browsers and devices.

* **Memoization**: The `memoizer` function caches the results of expensive function calls, making it more efficient for repeated computations. This approach is particularly beneficial when dealing with recursive functions like the Fibonacci sequence.
* **Without Memoization**: Without caching, each call to the `fibonacci` function computes the result from scratch, leading to a significant increase in execution time.

**Pros and Cons**

**Memoization:**
Pros:

* Improved performance for repeated computations
* Reduced overhead due to caching

Cons:

* Additional memory usage due to cache storage
* Potential issues with cache invalidation or expiration

**Without Memoization:**
Pros:

* No additional memory usage
* Simplified codebase

Cons:

* Poor performance for repeated computations
* Increased execution time

**Library and Special JS Features**

There is no explicit library mentioned in the benchmark, but it's worth noting that the `memoizer` function uses a simple object caching mechanism.

As for special JavaScript features, there are none explicitly mentioned or used in this benchmark. However, if you were to modify the code, you could explore using modern JavaScript features like:

* **Generators**: To implement the Fibonacci sequence more efficiently.
* **Async/Await**: To make the code more readable and easier to maintain.

**Alternative Approaches**

Other alternatives for caching the Fibonacci sequence might include:

* Using a dedicated caching library (e.g., LRU Cache)
* Implementing a more advanced cache invalidation strategy
* Utilizing parallel processing or multi-threading techniques

Keep in mind that these alternatives would depend on specific performance requirements and constraints.

**Benchmark Preparation Code**

The provided benchmark preparation code is minimal, as it only defines the two test cases:

1. `memoizer` function with caching
2. Simple Fibonacci sequence without caching

This simplicity allows for a clean comparison between the two approaches.

Related benchmarks:

memoizer (version: 0)

Test memoizer function

Comparing performance of: Memoizer vs No Memoizer

Created: 4 years ago by: Guest

Jump to the latest result

Memoizer

No Memoizer

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):