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High performance sorting library for javascript

License: MIT License

CMake 27.85% JavaScript 50.95% Cuda 21.20%

cuda javascript sorting thrust

accel_sort's Introduction

accel-sort

A high performance sorting library for Javascript. Get up to 70x speedup when sorting ints and floats

Prerequisites

NVIDIA GPU with CUDA Compute Capability (5.0 or higher)
Node.js (Version 12.x or higher)
CUDA Version 12.4 installed

Installation

Run npm install accel-sort

Building from source

Make sure you have nvcc installed on your system, then simply run

npm install

Usage

Simply import the AccelSort with the following:

const AccelSort = require("accel-sort");

Within AccelSort, pass it the specified array types and size to their dedicated functions:

sortIntegers:

let array = new Int32Array([3, 1, 2]);
let buffer = Buffer.from(array.buffer);
AccelSort.sortIntegers(buffer, array.length);

sortFloat:

let array = new Float32Array([5.8, -10.7, 1507.6563, 1.0001]);
let buffer = Buffer.from(array.buffer);
AccelSort.sortFloats(buffer, array.length);

After that, the original array object will be correctly sorted in place

Contributions

Contributions are welcome! Please submit a pull request or open an issue to discuss proposed changes or additions.

License

Distributed under the MIT License. See LICENSE for more information.

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accel_sort's Issues

Support custom comparator/passing objects through and giving a property to sort on

By having more flexibility in what type of data can be sorted, we can process full object arrays, copying their indices and the key value, then moving those elements to the sorted position

Create sorting kernel for ROCm (AMD)

The ability to create a sorting kernel for ROCm would be ideal and would result in a higher adoption

Pre-compile packages for targets

Having prebuilt packages available to download will be alot easier for getting started. Must target:

Linux
Windows
macOS (maybe?)

In addition to different architectures

arm64
amd64/x86_64

And if possible, targeting different CUDA versions would be ideal

Create WebGPU compute shader for sorting on clients that support it

Creating a bitonic/radix sort compute shader using WebGPU can be extremely beneficial for using this library in a web application. This is an unlikely case but could lead to a higher adoption

Use standard lib (stl) sorting when amount of elements is below overhead

First need to determine the common amount of elements that result in a slowdown when pushing to GPU memory, then sorting. Otherwise, we can just use the std::sort on the elements

Auto detect array types and use built in array types

User shouldn't have to specify the specific array type and use the dedicated method to sort

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