libGPGPU
ArrayFire
| libGPGPU | ArrayFire | |
|---|---|---|
| 4 | 6 | |
| 8 | 4,418 | |
| - | 0.8% | |
| 8.0 | 7.1 | |
| 5 months ago | about 1 month ago | |
| C++ | C++ | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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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.
libGPGPU
- Can anyone with Arc750 or 770 try this FMA benchmark written with OpenCL? I want to compare gigaflops.
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I made a fused-multiply-add test program that distributes work onto all GPUs, CPUs, iGPU of computer. As a result, it outputs the total combined GFLOPS value of the computer.
libGPGPU/fma_benchmark.rar at master · tugrul512bit/libGPGPU · GitHub
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AMD and Intel Battle for Windows 11 AI Acceleration Lead
16000x16000 mandelbrot generator with 50 iterations per pixel takes only 50 milliseconds using gt1030+iGpu+ryzen 7900 https://github.com/tugrul512bit/libGPGPU
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?
Boost.Compute - A C++ GPU Computing Library for OpenCL
Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
compute-runtime - Intel® Graphics Compute Runtime for oneAPI Level Zero and OpenCL™ Driver
VexCL - VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP
Cekirdekler - Multi-device OpenCL kernel load balancer and pipeliner API for C#. Uses shared-distributed memory model to keep GPUs updated fast while using same kernel on all devices(for simplicity).
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
EpicWarCL - C# fully OpenCL(C99)-accelerated game demo and benchmark, prealpha- stage abondonware.
CUB - THIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE.
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System
moderngpu - Patterns and behaviors for GPU computing