BlockingCollection
ArrayFire
| BlockingCollection | ArrayFire | |
|---|---|---|
| 1 | 6 | |
| 58 | 4,413 | |
| - | 0.7% | |
| 0.0 | 7.1 | |
| over 4 years ago | 30 days ago | |
| C++ | C++ | |
| GNU General Public License v3.0 only | BSD 3-clause "New" or "Revised" License |
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BlockingCollection
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What are some candidate libraries for inter-thread communication like message boxes or event systems?
I wasn't aware of copper; that looks pretty slick if you're able to pivot to that paradigm of thread interaction. I had played around with BlockingCollection at some point in the past.
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.
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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?
moodycamel - A fast multi-producer, multi-consumer lock-free concurrent queue for C++11
Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System
Boost.Compute - A C++ GPU Computing Library for OpenCL
eXtended Template Library - eXtended Template Library
VexCL - VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP
libmill - Go-style concurrency in C
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
readerwriterqueue - A fast single-producer, single-consumer lock-free queue for C++
CUB - THIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE.
cupla - The project alpaka has moved to https://github.com/alpaka-group/cupla