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b7c5481c55
This is the release of the CUDA 13.3 samples, which include additions for CUDA Tile C++, and updated CCCL and Python samples.
45 lines
2.4 KiB
Markdown
45 lines
2.4 KiB
Markdown
# libcuxxRandom - libcu++ Random Distributions
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## Description
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This sample demonstrates the random-number facilities added to libcu++ in CCCL. `<cuda/std/random>` now offers host- and device-compatible implementations of the standard C++ distributions (uniform, normal, Poisson, Bernoulli, and more) and backports the C++26 `cuda::std::philox4x32` / `philox4x64` engines. `<cuda/random>` adds `cuda::pcg64` as an NVIDIA extension (the same generator NumPy uses by default). A kernel draws samples on each thread and the host computes empirical statistics, comparing them to the theoretical mean / variance / probability.
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## Key Concepts
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CCCL 3.3, libcu++ Random, PCG, Philox, Device-Side PRNG
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## Supported SM Architectures
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[SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) [SM 10.0 ](https://developer.nvidia.com/cuda-gpus) [SM 11.0 ](https://developer.nvidia.com/cuda-gpus) [SM 12.0 ](https://developer.nvidia.com/cuda-gpus)
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## Supported OSes
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Linux, Windows
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## Supported CPU Architecture
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x86_64, aarch64
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## CUDA APIs involved
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### [CCCL libcu++](https://nvidia.github.io/cccl/libcudacxx/)
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cuda::pcg64, cuda::std::philox4x32, cuda::std::uniform_real_distribution, cuda::std::normal_distribution, cuda::std::poisson_distribution, cuda::std::bernoulli_distribution
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### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
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cudaMalloc, cudaFree, cudaMemcpy, cudaDeviceSynchronize, cudaGetDeviceProperties
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## Dependencies needed to build/run
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[CCCL 3.3+](https://github.com/NVIDIA/cccl). Fetched automatically via CPM at configure time (pinned to `v3.3.3`). Override with `-DCCCL_SOURCE_DIR=/path/to/cccl` to use a local checkout.
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## Prerequisites
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Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
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Make sure the dependencies mentioned in [Dependencies]() section above are installed.
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## References (for more details)
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[CCCL 3.3 release notes](https://github.com/NVIDIA/cccl/releases), [cuda::pcg64 header](https://github.com/NVIDIA/cccl/blob/main/libcudacxx/include/cuda/__random/pcg_engine.h), [NumPy PCG64](https://numpy.org/doc/stable/reference/random/bit_generators/pcg64.html)
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