[[Category:Programming languages]]
[[Category:GPU Programming languages]]
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620 bytes (81 words) - 16:46, 31 January 2022
The main benefit over OpenCL is that your GPU code is compiled at the same time as your C++ code and is intercompatible.
* [https://rocm.github.io/languages.html A table to transition between CUDA, OpenCL, ROCm]
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687 bytes (116 words) - 16:47, 31 January 2022
...at <code>nvidia-smi</code> lists the maximum CUDA version supported by the GPU driver, not the installed version of CUDA.<br>
...d in each conda environment, independently of the version supported by the GPU driver.
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3 KB (406 words) - 16:15, 23 April 2024
[[Category:Programming languages]]
[[Category:GPU Programming languages]]
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3 KB (424 words) - 16:46, 31 January 2022
<div class="main-card-header">Programming</div>
==Programming==
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7 KB (704 words) - 16:47, 12 December 2022
<div class="main-card-header">Programming</div>
==Programming==
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8 KB (717 words) - 14:12, 3 April 2024
If you're using an NVIDIA GPU, install the [https://developer.nvidia.com/cuda-toolkit CUDA Toolkit].
See https://www.eriksmistad.no/getting-started-with-opencl-and-gpu-computing/
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11 KB (1,602 words) - 16:47, 31 January 2022
The idea of branchless programming is to avoid <code>if</code> and <code>for</code> statements.
These are micro optimizations but they could be useful for speeding up GPU shaders in some cases.
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3 KB (402 words) - 19:58, 16 November 2023
Similar to [[C (programming language) | C]], the arguments are <code>int argc</code> and <code>char *ar
==Functional Programming==
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26 KB (3,897 words) - 19:36, 17 April 2024
[https://www.programiz.com/python-programming/string-interpolation Reference]<br>
A lot of functional programming can be done with lists<br>
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19 KB (2,580 words) - 19:39, 18 April 2023
