Easy Creation of GnuPlot Scripts from C++
ArrayFire
| Easy Creation of GnuPlot Scripts from C++ | ArrayFire | |
|---|---|---|
| - | 6 | |
| 46 | 4,404 | |
| - | 0.5% | |
| 0.7 | 7.8 | |
| about 1 year ago | 27 days ago | |
| C++ | C++ | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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Easy Creation of GnuPlot Scripts from C++
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Tracking mentions began in Dec 2020.
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?
NCCL - Optimized primitives for collective multi-GPU communication
Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
Boost.Compute - A C++ GPU Computing Library for OpenCL
RaftLib - The RaftLib C++ library, streaming/dataflow concurrency via C++ iostream-like operators
VexCL - VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP
HPX - The C++ Standard Library for Parallelism and Concurrency
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
libdill - Structured concurrency in C
CUB - THIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE.
libcds - A C++ library of Concurrent Data Structures
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System