SimpleOpenCLSamples
ArrayFire
SimpleOpenCLSamples | ArrayFire | |
---|---|---|
7 | 6 | |
76 | 4,413 | |
- | 0.5% | |
7.0 | 7.1 | |
18 days ago | 29 days ago | |
C++ | C++ | |
MIT License | BSD 3-clause "New" or "Revised" License |
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SimpleOpenCLSamples
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In the next 5 years, what do you think can push OpenCL adoption?
https://github.com/bashbaug/SimpleOpenCLSamples/tree/main/samples/usm - some USM samples, including several linked list samples.
- OpenCL 3.0.12 Released With Command Buffers Mutable Dispatch Extension and Enhanced Layers Support
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Why are there lack of opencl tutorial?
Selfishly, my own SimpleOpenCLSamples repo: https://github.com/bashbaug/SimpleOpenCLSamples
- Cant get OpenCL to run
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Profiling OpenCL code
We use the OpenCL Intercept Layer extensively. It's open-source, vendor-independent, and cross-platform. I wrote a tutorial to demonstrate common usages, if you want to see what it can do.
- I want to learn OpenCL but don't know where to start
- Any materials or samples on opencl 3.0?
ArrayFire
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Learn WebGPU
Loads of people have stated why easy GPU interfaces are difficult to create, but we solve many difficult things all the time.
Ultimately I think CPUs are just satisfactory for the vast vast majority of workloads. Servers rarely come with any GPUs to speak of. The ecosystem around GPUs is unattractive. CPUs have SIMD instructions that can help. There are so many reasons not to use GPUs. By the time anyone seriously considers using GPUs they're, in my imagination, typically seriously starved for performance, and looking to control as much of the execution details as possible. GPU programmers don't want an automagic solution.
So I think the demand for easy GPU interfaces is just very weak, and therefore no effort has taken off. The amount of work needed to make it as easy to use as CPUs is massive, and the only reason anyone would even attempt to take this on is to lock you in to expensive hardware (see CUDA).
For a practical suggestion, have you taken a look at https://arrayfire.com/ ? It can run on both CUDA and OpenCL, and it has C++, Rust and Python bindings.
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seeking C++ library for neural net inference, with cross platform GPU support
What about Arrayfire. https://github.com/arrayfire/arrayfire
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[D] Deep Learning Framework for C++.
Low-overhead — not our goal, but Flashlight is on par with or outperforming most other ML/DL frameworks with its ArrayFire reference tensor implementation, especially on nonstandard setups where framework overhead matters
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[D] Neural Networks using a generic GPU framework
Looking for frameworks with Julia + OpenCL I found array fire. It seems quite good, bonus points for rust bindings. I will keep looking for more, Julia completely fell off my radar.
- Windows 11 va bloquer les bidouilles qui facilitent l'emploi d'un navigateur alternatif à Edge
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Arrayfire progressive performance decline?
Your Problem may be the lazy evaluation, see this issue: https://github.com/arrayfire/arrayfire/issues/1709
What are some alternatives?
OpenCL-Guide - A guide to help developers get up and running quickly with the OpenCL programming framework
Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
FluidX3D - The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs via OpenCL.
Boost.Compute - A C++ GPU Computing Library for OpenCL
VexCL - VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP
opencl-intercept-layer - Intercept Layer for Debugging and Analyzing OpenCL Applications
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
nvidia-opencl-examples
CUB - THIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE.
OpenCL-Getting-Started - A small "getting started" tutorial for OpenCL. See http://www.eriksmistad.no/getting-started-with-opencl-and-gpu-computing/ for more info
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System