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map-reduce-loop
(version: 0)
Comparing performance of:
map-reduce vs loop
Created:
5 years ago
by:
Guest
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Script Preparation code:
window.cols = Array.from({length: 20}).map(() => ({ style: { width: '123px' }}))
Tests:
map-reduce
const sum = cols .map(c => parseFloat(c.style.width)) .reduce((a, b) => a + b); console.log(sum)
loop
let sum = 0 for (const c of cols) { sum += parseFloat(c.style.width) } console.log(sum)
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
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Test case name
Result
map-reduce
loop
Fastest:
N/A
Slowest:
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's dive into the world of JavaScript microbenchmarks and analyze the provided benchmark. **What is tested?** The benchmark tests two approaches to calculate the sum of the widths of elements in an array: 1. `map-reduce-loop`: This approach uses the `map()` method to create a new array with transformed values, followed by the `reduce()` method to calculate the sum. 2. `loop`: This approach uses a traditional `for` loop to iterate over the array and add up the widths. **Options compared** The two approaches have different performance characteristics: * `map-reduce-loop`: + Pros: More concise and expressive code, leverages modern JavaScript features like `map()` and `reduce()`. + Cons: May incur higher overhead due to the creation of a new array and function calls. * `loop`: + Pros: Avoids the overhead of creating a new array and can be faster for small arrays or in situations where memory is limited. + Cons: Less concise and less expressive code, may not take advantage of modern JavaScript features. **Other considerations** * **Array iteration**: Both approaches iterate over the array, but `map-reduce-loop` creates an intermediate array, which can lead to higher memory usage. In contrast, `loop` only uses a single variable `c` to access each element in the loop. * **JavaScript engine optimizations**: The performance difference between the two approaches may be mitigated by the JavaScript engine's optimization techniques, such as inlining and caching. **Library and special features** In this benchmark, there is no specific library used. However, it does utilize modern JavaScript features like: * `Array.from()`: Creates a new array from an iterable object. * `map()` and `reduce()`: Array methods for transforming and aggregating data. No special JavaScript features or syntax are explicitly used in the benchmark code. **Alternatives** Other alternatives to consider when writing performance-critical JavaScript code include: * **NativeArray**: For large arrays, using a native array (like an `Uint8Array` or `Float64Array`) can be faster than working with JavaScript arrays. * **Web Workers**: If the computation is CPU-intensive, using web workers can offload the work to a separate thread, improving performance and responsiveness. * ** Just-In-Time (JIT) compilation**: Some modern browsers, like V8 in Chrome, have JIT compilers that can optimize JavaScript code at runtime. When writing performance-critical code, consider profiling tools, such as Chrome DevTools' Profiler or Node.js's built-in `performance` module, to identify bottlenecks and optimize accordingly.
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