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Ringbuffer array vs Float64Array
(version: 0)
Comparing performance of:
array i/o vs typedArray i/o
Created:
5 years ago
by:
Guest
Go to the latest result
Script Preparation code:
var a = [...Array(10000)].map(_ => Math.random()); var ta = (new Float64Array(10000)).map(_ => Math.random());
Tests:
array i/o
for (let i = 0; i < 10000; ++i) a[i] = a[i] + 1;
typedArray i/o
for (let i = 0; i < 10000; ++i) ta[i] = ta[i] + 1;
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
array i/o
typedArray i/o
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
one year ago
)
User agent:
Mozilla/5.0 (X11; Linux x86_64; rv:131.0) Gecko/20100101 Firefox/131.0
Browser/OS:
Firefox 131 on Linux
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Embed Benchmark Result
<iframe src="https://measurethat.net/Embed?id=561981" width="100%" height="500px"></iframe>
typedArray i/o
154K/s
array i/o
102K/s
View exact numbers
Test name
Executions per second
✓
typedArray i/o
154,312 Ops/sec
array i/o
101,940 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the benchmark and explain what's being tested. **Benchmark Overview** The benchmark compares the performance of two data structures: an array and a Float64Array (a typed array). The goal is to measure how efficient each data structure is for incrementing its elements. **Options Compared** There are two options being compared: 1. **Array**: An untyped array, which is a collection of values stored in a contiguous block of memory. 2. **Float64Array**: A typed array that stores 64-bit floating-point numbers. It's designed to provide better performance and efficiency than an untyped array for numerical computations. **Pros and Cons** * **Array**: + Pros: Easy to use, no need to specify the type of data being stored. + Cons: Can be slower and less efficient than typed arrays due to the overhead of JavaScript's dynamic typing. * **Float64Array**: + Pros: Provides better performance and efficiency for numerical computations, as it can take advantage of the CPU's floating-point instructions. + Cons: Requires specifying the type of data being stored, which can be error-prone if not done correctly. **Library and Purpose** There is no specific library mentioned in this benchmark. However, JavaScript provides its own typed array implementations, such as Float64Array, Uint8Array, and others. **Special JS Feature or Syntax** None are mentioned explicitly in the provided code snippet. However, it's worth noting that JavaScript has some advanced features like async/await, Promises, and Web Workers that can affect benchmark performance. **Other Alternatives** If you want to compare other data structures or algorithms, here are a few alternatives: * **ArrayBuffer**: A binary buffer that can be used with typed arrays. It's similar to a Float64Array but provides more flexibility. * **Typed Array View**: A way to create a view of an existing array, allowing it to be treated as a typed array. * **NativeArray**: Some JavaScript engines provide native arrays, which are optimized for performance and can be used for benchmarking. When choosing an alternative, consider the specific use case and the requirements of your project. For example, if you're working with numerical computations, a Float64Array might still be the best choice. However, if you're working with binary data or need more control over memory allocation, an ArrayBuffer or Typed Array View might be more suitable. In summary, this benchmark is designed to compare the performance of two basic data structures: arrays and typed arrays. By using these alternatives, developers can gain a better understanding of how different data structures impact performance in JavaScript applications.
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