dpnp VS dpctl

Compare dpnp vs dpctl and see what are their differences.

dpnp

Data Parallel Extension for NumPy (by IntelPython)
Sycl Numpy Python3 Intel Mkl Oneapi gpu-acceleration GPU Dpcpp pstl
Source Code
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dpctl

Python SYCL bindings and SYCL-based Python Array API library (by IntelPython)
Oneapi dppy Sycl intel-xpu Intel Python gpu-computing GPU
Source Code
intelpython.github.io
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dpnp dpctl
1 1
91 94
- -
9.5 9.8
4 days ago 4 days ago
Python C++
BSD 2-clause "Simplified" License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

dpnp

Posts with mentions or reviews of dpnp. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning dpnp yet.
Tracking mentions began in Dec 2020.

dpctl

Posts with mentions or reviews of dpctl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-11-24.
  • Data Parallel Extensions for Python: near-native speed for scientific computing
    2 projects | news.ycombinator.com | 24 Nov 2023
    Considering how poorly it seems to support cuda as a backend [0], I wouldn't hold my breath about non intel vendor support (amd cpu or gpu). As for less common gpus, there really is no good support in any library. If you ever want to go down a fun rabbit hole, try to use the gpu in a raspberry pi for something. You'll eventually find one guy who reverse engineered the drivers to make a compiler but that's it.

    [0] https://github.com/IntelPython/dpctl/discussions/1124

What are some alternatives?

When comparing dpnp and dpctl you can also consider the following projects:

numpy-stl - Simple library to make working with STL files (and 3D objects in general) fast and easy.

dpbench - Benchmark suite to evaluate Data Parallel Extensions for Python

cupy - NumPy & SciPy for GPU

oneAPI-samples - Samples for Intel® oneAPI Toolkits

sparse_dot - Python wrapper for Intel Math Kernel Library (MKL) matrix multiplication

ParallelReductionsBenchmark - Thrust, CUB, TBB, AVX2, CUDA, OpenCL, OpenMP, SyCL - all it takes to sum a lot of numbers fast!

oneMKL - oneAPI Math Kernel Library (oneMKL) Interfaces

matchering - 🎚️ Open Source Audio Matching and Mastering

AdaptiveCpp - Implementation of SYCL and C++ standard parallelism for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!

eaminer - Heterogeneous Ethereum Miner with support for AMD, Intel and Nvidia GPUs using SYCL, OpenCL and CUDA backends

dpnp vs numpy-stl dpctl vs dpbench dpnp vs cupy dpctl vs oneAPI-samples dpnp vs sparse_dot dpctl vs ParallelReductionsBenchmark dpnp vs oneMKL dpctl vs oneMKL dpnp vs matchering dpctl vs AdaptiveCpp dpnp vs eaminer dpctl vs eaminer