Cupy vs cuda The figure shows CuPy speedup over NumPy. If just using cuda::memcpy_async with barrier, it looks the same with Synchronous Copy? Nov 1, 2024 · Thanks for your explainaton. x (11. You can confirm the GPU usage of CuPy. cu, cupy_matvec. gesvdj. py, and numba_matvec. Python calls to torch functions will return after queuing the operation, so the majority of the GPU work doesn't hold up the Python code. CuPy, a GPU-accelerated drop-in replacement for Numpy -- and the GPU-accelerated features available in Numba. RawKernel What do I lose by writing Cuda in Python vs. The code is given below: import numpy as np import numba CuPy has been available for years and has always worked great. Most operations perform well on a GPU using CuPy out of the box. Feb 6, 2024 · Generally CuPy is on the GPU, and in fact in the docs for this method, it mentions that it calls cuSOLVER (cupy. The Euclidean distance formula is vectorized to leverage GPU parallelism. Installing CuPy from Conda-Forge # Conda is a cross-language, cross-platform package management solution widely used in scientific computing and other fields. The ball was in Nvidia’s court, and they let OpenAI and Meta take control of the software stack. vscuda README VS Code extension for CUDA support. It's a very popular and well-supported library with a syntax that's similar to numpy. Sep 5, 2022 · General Usage cuda, broadcasting, cuarrays, tensors, mapslices Lincoln_Hannah September 5, 2022, 5:35am 1 Trying to apply a function to slices of a CuArray. Kernels' execution are async in PyTorch, while there are some gaps between kernels' execution in Cupy. Reference below link rapidsai/cucim#329 (comment) if initiated cuda_GpuMat with CuPy array pointer, the result is not as expected, it seems Aug 18, 2025 · CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. Jun 13, 2022 · CuPy for an array-oriented calculation on a GPU, which nevertheless has to create intermediate arrays CuPy with a custom cp. jl would compare with one of bigger Python GPU libraries CuPy. linalg. Oct 2, 2025 · CUDA C++ Best Practices Guide 1. TensorFlow - Open Source Software Library for Machine Intelligence. py. The idea that this is a drop in replacement for numpy (e. Aug 18, 2025 · This is a CuPy wheel (precompiled binary) package for CUDA 12. 2 (older) - Last updated October 9, 2025 - Send Feedback CuPy always raises cupy. This class can be used to define a custom kernel using raw CUDA source. Speed up specific operations by integrating custom CUDA kernels using CuPy or Numba. When I run this myself for a 64-bit double matrix using cuSOLVER directly, with cusolverDnDgesvd, I get about 5 iterations per second. Alternatively, for both Linux (x86_64, ppc64le, aarch64-sbsa) and Windows once the CUDA driver is correctly set up, you can also install CuPy from the conda-forge 01 :: CuPy and Numba on the GPU NumPy can be used for array math on the CPU. If you have another version of CUDA, or want to build from source, refer to the Installation Guide for instructions. This is a CuPy wheel (precompiled binary) package for CUDA 13. CuPy provides a ndarray, sparse matrices, and the associated routines for GPU devices, all having the same API as NumPy and SciPy: Nov 27, 2024 · Introduction to CuPy CuPy is a GPU array library that implements a subset of the NumPy and SciPy interfaces. 0 that now offers support for the ROCm stack for GPU-accelerated computing. scipy. If you wish to input a PyTorch tensor into OMEGA forward/backward projection operator, you need to use CUDA (and the tensor HAS to be a CUDA device tensor). The Dask CUDA project contains some convenience CLI and Python utilities to automate this process. Currently there is an experimental metapackage on PyPI. In summary, CuPy provides a high-level Python interface for programming GPU-accelerated computations using CUDA. Learn how to use CuPy and Numba's CUDA extensions in conjunction for amazingly fast Sep 15, 2016 · With pyCUDA you will be writing the CUDA kernels using C++, and it's CUDA, so there shouldn't be a difference in performance of running that code. In this process, I need to use SciPy routines along with Numba. Thanks to CuPy, people conversant with NumPy can very conveniently harvest the compute power of GPUs without writing code in GPU programming languages such as CUDA, OpenCL, and HIP. - in CuPy column denotes that CuPy implementation is not provided yet. This improves NCCL compatibility on mixed-library environments. 2 - 11. This fundamental distinction creates significant performance variations: May 15, 2019 · I know that with other libraries that allow one to use python with the GPU, you have to specify that you're using cuda, otherwise the functions will work but not use cuda, like numba. This is a true reflection of the peak floating point throughput of a compute GPU and a modern x86-64 CPU The key here is asynchronous execution - unless you are constantly copying data to and from the GPU, PyTorch operations only queue work for the GPU. You need to install CUDA Toolkit 12. See cupy. While CuPy deals with all device-related code instead of you, the computations are still Apr 7, 2025 · CuPy vs. Context Initialization # It may take several seconds when calling a CuPy function for the first time in a process. Mar 17, 2023 · Is it possible to access CuPy array/memory directly within cuda_GpuMat to support CudaArrayInterface? Implemented a wrapper of CudaArrayInterface works fine to move GpuMat memory directly to CuPy. We demonstrate registering an Xarray backend that reads data from a Zarr store directly to GPU memory as CuPy arrays using the new kvikIO library and GPU Direct Storage technology. Array operations are very amenable to execution on a massively parallel GPU. MemoryPool and cupy. Which of the 4 has the most linalg support and support for custom functions (The algo has a lot of fancy indexing, comparisons, sorting, filtering)? Aug 27, 2020 · Mostly all examples of Numba, CuPy and etc available online are simple array additions, showing the speedup from going to cpu singles core/thread to a gpu. x) cupy-rocm-5-0 Installing CuPy Uninstalling CuPy Upgrading CuPy Reinstalling CuPy Using CuPy inside Docker FAQ Using CuPy on AMD GPU (experimental) User Guide Basics of CuPy User-Defined Kernels Accessing CUDA Functionalities Fast Fourier Transform with CuPy Memory Management Performance Best Practices Interoperability Differences between CuPy and NumPy API Comparison Table # Here is a list of NumPy / SciPy APIs and its corresponding CuPy implementations. I captured the Unix time computing n, m = 8192, 8192 one thousand times. CuPy utilizes CUDA Toolkit libraries including cuBLAS, cuRAND, cuSOLVER, cuSPARSE, cuFFT, cuDNN and NCCL to make full use of the GPU architecture. RawKernel # class cupy. By leveraging CuPy and Numba, cuSignal achieves significant performance gains over CPU-based signal processing, particularly for large signal sizes, with speedups evident in operations like Apr 11, 2025 · GPU details of a Kaggle Notebook with GPU P100 acceleration. The block_dim line is commented out in the Taichi demonstrations. I also know of Jax and CuPy but haven't used either. float32) are aliases of NumPy scalar values and are allocated in CPU memory. 使用 cuPy，您可以利用 NVIDIA GPU 的并行处理功能以大规模并行方式执行数组操作和数学计算。通过与 CUDA 无缝集成，cuPy 使您能够编写 GPU 加速代码，而无需进行大量修改。示例：数组平方让我们比较一下 cuPy 与 NumPy 如何加速简单的数组平方运算： We'll introduce CuPy, describing the advantages of the library and how it is cleanly exposing in Python multiple CUDA state-of-the art libraries such as cuTENSOR or cuDNN. Device. CuPy provides GPU accelerated computing with Python. However, while performing tests using a CuPy array vs a NumPy array, results have shown that using a CuPy array is not beneficial for speeding up visualization of the image. If these types were returned, it would be required to synchronize between GPU and CPU. This gives us near instant access to the best of both libraries. numba, cupy, CUDA python, and pycuda are some of the available approaches to tap into CUDA acceleration from Python. cfg file? Unfortunately there is no perfect solution yet but we are getting there. Sorry, this file is invalid so it cannot be displayed. , filtering, FFT, or statistical summaries), use CuPy to process data on the GPU, then pass the result to Matplotlib for plotting. Most of your code stays the Installing CuPy Uninstalling CuPy Upgrading CuPy Reinstalling CuPy Using CuPy inside Docker FAQ Using CuPy on AMD GPU (experimental) User Guide Basics of CuPy User-Defined Kernels Accessing CUDA Functionalities Fast Fourier Transform with CuPy Memory Management Performance Best Practices Interoperability Differences between CuPy and NumPy API Jul 27, 2025 · Unlock massive speed-ups for NumPy with GPU acceleration using cuPy and Dask. !pip install cupy-cuda12x # For version 11 # !pip install cupy-cuda11x This isn’t different from installing NumPy, is it? That’s what a drop-in replacement means. That ecosystem built its own tools because of Nvidia’s failure with their proprietary tools, and now Nvidia’s moat will be permanently weakened. And commands documentations mostly lack g Oct 25, 2022 · Notably, the CUDA/CUB and CuPy implementations achieve impressive performance, both exceeding 90% of the peak bandwidth of the hardware. 5. Limiting GPU Memory Usage # You can hard-limit the amount of GPU memory that can be allocated by using CUPY_GPU_MEMORY_LIMIT environment variable (see Environment variables for details). The instance of this class defines a CUDA kernel which can be invoked by the Installing CuPy Uninstalling CuPy Upgrading CuPy Reinstalling CuPy Using CuPy inside Docker FAQ Using CuPy on AMD GPU (experimental) User Guide Basics of CuPy User-Defined Kernels Accessing CUDA Functionalities Fast Fourier Transform with CuPy Memory Management Performance Best Practices Interoperability Differences between CuPy and NumPy API May 24, 2023 · Results: CuPy clearly outperforms Numpy As you can see here, CuPy outperforms Numpy by a big margin. However, they are now used to do a wide range of computations too. cuda. Nov 1, 2023 · Image by Author What is CuPy? CuPy is a Python library that is compatible with NumPy and SciPy arrays, designed for GPU-accelerated computing. May 24, 2023 · Exploring GPU-Accelerated Numerical Computing: A Look into cuPy and Numba Introduction In the realm of numerical computing, harnessing the immense power of GPUs can significantly boost performance … May 29, 2024 · Accelerated Python: CuPy Faster Matrix Operations on GPUs This blog post is part of the series Accelerated Python. Using CuPy on the GPU can result in over a 100x speedup for array processing I know of Numba from its jit functionality. Jul 24, 2020 · For careful timing of a kernel-only execution in cupy or numba, I would suggest the method I indicate below: use device-resident arrays, and be careful to use cuda. The results show that CUDA C, as expected, has the fastest performance and highest energy efficiency, while Numba offers comparable performance when data movement is minimal. This is because the CUDA driver creates a CUDA context during the first CUDA API call in CUDA applications. That's it! You can now code in CUDA without having to look up the documentation everytime. LogicError: cuFuncSetBlockShape failed: invalid resource handle Do you know how I could fix it? Here is a simplified code to reproduce the error: import numpy as np import cupy as cp from scipy. Jan 30, 2025 · The AI/ML Engineer's starter guide to GPU Programming #1 Programming on GPUs from scratch by implementing CUDA Kernels in C++, CuPy Python and OpenAI Triton. ndarray The concept of current device host-device and device-device array transfer Basics of cupy. use() or cudaSetDevice()) will be reactivated when exiting a device context manager. matmul. Apr 10, 2021 · My guess would be that some time is spent on data transfer, to the GPU, and while I don't include . RawKernel(str code, str name, tuple options= (), str backend='nvrtc', bool translate_cucomplex=False, *, bool enable_cooperative_groups=False, bool jitify=False) [source] # User-defined custom kernel. ndarray s. Here is the Julia code I was benchmarking using CUDA using CUDA. Mar 16, 2023 · I've edited the global configuration options in PyQtGraph and set useCupy and useOpenGL to True with pyqtgraph. In the following code, cp is an abbreviation of cupy, following the standard convention of abbreviating numpy as np: This document compares Just-In-Time (JIT) CUDA development with CuPy to Ahead-Of-Time (AOT) development using CUDA/C++ and CMake. Optimizing Your GPU Code: Key Tips Minimize Data Mar 20, 2024 · We compared Numba and CuPy to each other and our CUDA C implementation. Oct 13, 2024 · For example, if we switch between CPU and GPU calculations frequently, using CuPy may actually reduce efficiency. This allows you to perform array-related tasks using GPU acceleration, which results in faster processing of larger arrays. TensorFlow vs Aug 31, 2022 · Hello, I’m getting started with C++ CUDA and running the SingleAsianOption Cuda Sample on Visual Studio and it runs in around 140 ms on my NVIDIA GeForce GTX 1650. C or C++? : r/CUDA r/CUDA Current search is within r/CUDA Remove r/CUDA filter and expand search to all of Reddit Aug 18, 2025 · CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. We can now install CuPy on this notebook environment. The difference between CuPy and this may be due to it using some other algorithm, e. Contribute to nv-legate/cupynumeric development by creating an account on GitHub. Both do their best to keep you at the Array-level abstraction until you actually need to start writing kernels yourself and even then, it's pretty simple. Basics of elementwise kernels # An elementwise kernel can be defined by the ElementwiseKernel class. The SciPy library is based on NumPy and provides a rich set on functionalities for scientific computing. NVIDIA cuPyNumeric layers on top of Legate, like many other libraries. Y If I want to publish a package, what should I write in the setup. Includes syntax highlighting, code help and auto code completion. Since P100 runs on CUDA version 12. Features Syntax Jun 22, 2022 · pip install cupy builds from source (sdist) pip install cupy-cudaXY installs the prebuilt wheel for CUDA X. x) cupy-cuda12x (for CUDA 12. For their roles in the CUDA programming model, please refer to CUDA Programming Guide. It is accelerated with the CUDA platform from NVIDIA and also uses CUDA-related libraries, including cuBLAS, cuDNN, cuRAND, cuSOLVER, cuSPARSE, and NCCL, to make full use of the GPU architecture. The guide helps developers identify performance bottlenecks, leverage GPU architecture effectively, and apply profiling tools to fine Using numpy, cupy, and numba to compare convolution implementations. AOT This document compares Just-In-Time (JIT) CUDA development with CuPy to Ahead-Of-Time (AOT) development using CUDA/C++ and CMake. CuPy – NumPy-like API accelerated with CUDA ¶ This is the CuPy documentation. Jan 2, 2023 · CuPy’s eigensolver is built on top of NVIDIA’s CUDA Toolkit and implements the Jacobi eigenvalue algorithm to find the eigenvalues and eigenvectors of Hermitian matrices. CuPy 1 is an open-source library with NumPy syntax that increases speed by doing matrix operations on NVIDIA GPUs. More recently, Nvidia released the official CUDA Python, which will surely enrich the ecosystem. Is there a simple explanation? Thanks! Sep 3, 2018 · Multi-threading version of PyTorch is slower than "for" version. After installing, select Cuda as your language in the bottom right corner of your IDE. This gives us near-instant access to the best of both libraries. cloud Most recently, CuPy, an open-source array library with Python, has expanded its traditional GPU support with the introduction of version 9. PyCUDA provides even more fine-grained control of the CUDA API. Still, the high performance of these libraries is provided by the underling C-implementations. Sep 4, 2022 · CuPy offers both high level functions which rely on CUDA under the hood, low-level CUDA support for integrating kernels written in C, and JIT-able Python functions (similar to Numba). See full list on unum. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU. For the API reference please see Streams and events. This is a CuPy wheel (precompiled binary) package for CUDA 12. x x86_64 / aarch64 pip install cupy Oct 23, 2022 · I am working on a simulation whose bottleneck is lots of FFT-based convolutions performed on the GPU. It uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL to make full use of the GPU architecture. NumPy / CuPy APIs # Module-Level # Jan 18, 2024 · We are happy to announce that CuPy v13 is now available. device) <CUDA Device 0> Note: It’s Abstract CuPy 1 is an open-source library with NumPy syntax that increases speed by doing matrix operations on NVIDIA GPUs. g. The CuPy [14] package provides a similar set of functions, but these functions are implemented for GPUs using CUDA. Installation Search for VSCuda in Visual Studio Code Extensions Marketplace. _driver. Basics of CuPy User-Defined Kernels Accessing CUDA Functionalities Fast Fourier Transform with CuPy Memory Management Performance Best Practices Interoperability Differences between CuPy and NumPy API Compatibility Policy Aug 18, 2025 · CuPy : NumPy & SciPy for GPU CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. CuPy utilizes CUDA, an NVIDIA parallel computing platform, to accelerate numerical computations on GPUs. But there will be a difference in the performance of the code you write in Python to setup or use the results of the pyCUDA kernel vs the one you write in C. CuPy [0] for Python and CUDA. User-Defined Kernels # CuPy provides easy ways to define three types of CUDA kernels: elementwise kernels, reduction kernels and raw kernels. Python can c Mar 5, 2021 · cuSignal is a library that GPU-accelerates the popular SciPy Signal library using CuPy and custom Numba CUDA kernels, making it suitable for signal processing applications that require real-time response. While CuPy offers ease of implementation, it performs slower for compute-heavy tasks. CuPy 对于这种复杂的运算则只能通过编写 CUDA 代码来实现。在这种没有现成的运算库的情况下，我们需要真正的高性能编程语言来实现这个公式。 Mar 12, 2025 · CuPy executes computations on NVIDIA GPUs using CUDA cores, while NumPy operates on CPU cores. For example, you can run the It is accelerated with the CUDA platform from NVIDIA and also uses CUDA-related libraries, including cuBLAS, cuDNN, cuRAND, cuSOLVER, cuSPARSE, and NCCL, to make full use of the GPU architecture. Mar 30, 2023 · Press enter or click to view image in full size CuPy v12 added official support for these latest NVIDIA GPU platforms. C or C++? : r/CUDA r/CUDA Current search is within r/CUDA Remove r/CUDA filter and expand search to all of Reddit Feb 6, 2024 · Generally CuPy is on the GPU, and in fact in the docs for this method, it mentions that it calls cuSOLVER (cupy. on('cuda') in torch measures, cupy does the tensor movements inside cupy. Why and When to Use JIT vs. Apr 24, 2024 · I’m looking to utilize CUDA to speed up simulation code in a Python environment. CuPy provides a NumPy-like interface for array Oct 3, 2020 · Contents Introduction C++ OpenCV CUDA Introduction OpenCV GpuMat and Libtorch OpenCV GpuMat and TensorFlow OpenCV GpuMat and tensorrt Python OpenCV CUDA Intro CUDA Array Interface Integration with CuPy Integration with Numba Integration with PyCUDA Integration with deep learning frameworks PyTorch TensorFlow Practical Notes Blocking vs. Fast Fourier Transform with CuPy # CuPy covers the full Fast Fourier Transform (FFT) functionalities provided in NumPy (cupy. This is a CuPy wheel (precompiled binary) package for CUDA 11. . The article is about the next wave of Python-oriented JIT toolchains, that will allow writing actual GPU kernels in a Pythonic-style instead of calling an existing precompiled GEMM implementation in CuPy (like in that snippet) or even JIT-ing CUDA C++ kernels from a Python source, that has also been available for years: https Overview # CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. That moves the bottleneck from Python to CUDA, which is why they perform so similarly. CUDA 11. e. jp CuPy is a NumPy/SciPy compatible Array library from Preferred Networks, for GPU-accelerated computing with Python. GPU hardware is designed for data parallelism, where high throughputs are achieved when the GPU is Accessing CUDA Functionalities # Streams and Events # In this section we discuss basic usages for CUDA streams and events. I try to use conda to install cupy and pip to install sp Jul 20, 2017 · Hi Guys, I am trying to implement a frame difference kernel on CUDA. Device Behavior # The CUDA current device (set via cupy. CuPy - A NumPy-compatible matrix library accelerated by CUDA. org called cupy-wheel for downstream to depend on. What do I lose by writing Cuda in Python vs. synchronize() to guard the timing region. Overview The CUDA C++ Best Practices Guide provides practical guidelines for writing high-performance CUDA applications. I would appreciate any suggestions on how to address these issues. setConfigOptions (). vectorize, but for GPUs) Numba's CUDA backend, which is effectively like cp. If I run a naive Tensorflow or Cupy code on Google colab I get the same result in around 2 ms. In [1]: print(b. Different GPU kernels are executed by default streams in PyTorch. CuPy v12 # Change in cupy. Jun 7, 2022 · This blog and the questions that follow it may be of interest. x. Kernel Compilation # CuPy uses on-the-fly kernel synthesis. I was surprised to see that CUDA. Is there any way to do this? Aug 12, 2021 · The cupy dot call (which uses a highly optimized GPU BLAS GEMM) hits about 4000 GFLOP/s average, i. about 50 times faster than numpy run on the host. I would have expected the C++ CUDA code to run much faster than Tensorflow or Cupy. Then how to install CuPy? First, go to this link and download the CUDA toolkit. In this video, I have walked through the installation process and the basics of CUPY. - randompast/python-convolution-comparisons Jul 22, 2021 · Hi all, I’m trying to do some operations on pyCuda and Cupy. PinnedMemoryPool for details. But I am still confused that it seems to me memcpy_async should be used with pipeline so that the latency can be overlapped with computation. Update of Docker Images # CuPy official Docker images (see Installation for details) are now updated to use CUDA 12. What is the difference of performance between Cuda C/C++ and CuPy (python wrapper of CUDA)? if I need to do operations on array size 1 million which one will be good in terms of scalability and Overview # CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. , `import cupy as np`) is quite nice, though I've gotten similar benefit out of using `pytorch` for this purpose. Tried broadcasting, mapslices and various Tensor packages. 62 seconds # Slightly slower than CuPy (custom implementation vs optimized CuPy kernel) Why this works: PyTorch tensors live on the GPU by default if device='cuda' is specified. Legate democratizes computing by making it possible for all programmers to leverage the power of large clusters of CPUs and GPUs by running the same code that runs Aug 18, 2025 · CuPy : NumPy & SciPy for GPU CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. With cuPyNumeric you can write code productively in Python, using the familiar NumPy API, and have your program scale with no code changes from single-CPU computers to multi-node-multi-GPU clusters. signal import butter Nov 17, 2021 · There are also libraries and frameworks that have CUDA support, such as TensorFlow or CuPy, so you can get the advantages of GPU processing without having to learn CUDA-specific coding. Work in Progress # GPU computing is a quickly moving field today and as a result the information in this page is likely to go out of date quickly. Apr 22, 2022 · The very first call to CuPy is slower because it takes time to initialize a GPU and create a CUDA context. I wanted to see how FFT’s from CUDA. NVIDIA CUDA 12 is the latest CUDA major release in many years, with Mar 8, 2024 · While part 1 focused on the usage of the new NVIDIA cuTENSOR 2. It offers ease of use, compatibility with multiple GPU architectures, portability, and support for a wide range of CUDA libraries. NumPy: Same Code, 10x Faster with GPUs If you are a heavy user of Numpy and are lucky enough to have access to a system with an Nvidia GPU, you have a relatively easy way to supercharge CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. This package (cupy) is a source distribution. x to use these packages. Reference below link rapidsai/cucim#329 (comment) if initiated cuda_GpuMat with CuPy array pointer, the result is not as expected, it seems Installing CuPy from Conda-Forge # Conda is a cross-language, cross-platform package management solution widely used in scientific computing and other fields. CuPy’s interface is highly compatible with NumPy; in most cases it can be Be aware of these overheads when benchmarking CuPy code. Is the same t Nov 1, 2024 · Thanks for your explainaton. Separately, both are working fine, but when I try to use pyCuda after Cupy, I got the following error: pycuda. Alternatively, for both Linux (x86_64, ppc64le, aarch64-sbsa) and Windows once the CUDA driver is correctly set up, you can also install CuPy from the conda-forge GPUs (Graphics Processing Units) are optimised for numerical operations, while CPUs (central processing units) perform general computation. Introduction Matrix operations are fundamental in fields like data science Installing CuPy from Conda-Forge # Conda is a cross-language, cross-platform package management solution widely used in scientific computing and other fields. 1 GPU-Accelerated Data Preprocessing with CuPy CuPy is a NumPy drop-in replacement that uses CUDA to accelerate array operations. svd — CuPy 13. At the same time, CUDA toolkit was installed successfully. It covers optimization strategies across memory usage, parallel execution, and instruction-level efficiency. Multi-threading version of Cupy is faster than "for" version. Understanding these different approaches will help readers appreciate CuPy's strengths and decide when each method is most appropriate for their GPU programming tasks. Hence, the term General Purpose GPU (GPGPU). cupy. 2. I would like to know if it is possible to combine Numba’s loop parallelization with the usage of SciPy and CuPy functions. That is because CuPy scalar values (e. 3 days ago · Here’s how: 4. Mar 19, 2021 · In this tutorial, we show how the CUDA Array and DLPack interfaces allow us to share our data between cuDF and CuPy in microseconds. fft). Originally, GPUs handled computer graphics. fft) and a subset in SciPy (cupyx. jl FFT’s were slower than CuPy for moderately sized arrays. In addition to those high-level APIs that can be used as is, CuPy provides additional features to access advanced routines that cuFFT offers for NVIDIA GPUs, control better the performance and behavior of the FFT routines Time to learn: 30 minutes Introduction to CuPy ¶ CuPy is an open-source GPU-accelerated array library for Python that is compatible with NumPy/SciPy. Learn how to scale array workflows efficiently with modern tools. CUDA Python simplifies the CuPy build and allows for a faster and smaller memory footprint when importing the CuPy Python module. nccl, instead of import cupy. However, the AMD-GPU compatibility for CuPy is quite an attractive feature. jl [1] for Julia are both excellent ways to interface with GPU that don't require you to get into the nitty gritty of CUDA. It is also used by spaCy for GPU processing. FWIW there are other python/CUDA methodologies. Maximum throughput is achieved when you are computing the same If you want numpy-like gpu array, the Chainer team is actively maintaining CuPy. If just using cuda::memcpy_async with barrier, it looks the same with Synchronous Copy? In CUDA, CuPy and PyTorch are supported, though the latter also uses CuPy internally. On the other hand, PyTorch leverages Torch, a scientific computing framework, which provides GPU acceleration through CUDA. If you want to use scalar values, cast the returned arrays explicitly. x) cupy-cuda11x (for CUDA 11. CuPy provides high-level Python APIs Stream and Event for creating streams and events, respectively. What it does is it detects the I performed element-wise multiplication using Torch with GPU support and Numpy using the functions below and found that Numpy loops faster than Torch which shouldn't be the case, I doubt. 0. What’s the advantage of this over things like CuPy or Numba? Tutorial: CUDA programming in Python with numba and cupy PyTorch for Deep Learning & Machine Learning – Full Course NVIDIA cuPyNumeric # cuPyNumeric is a library that aims to provide a distributed and accelerated drop-in replacement for NumPy built on top of the Legate framework. This new major release contains the effort of over 270 pull requests, including… Legate Legate is an abstraction layer that runs on top of the CUDA® runtime system, together providing scalable implementations of popular domain-specific APIs. CuPy speeds up some operations more than 100X. ndarray # CuPy is a GPU array backend that implements a subset of NumPy interface. We will not go into the CUDA programming model too much in this tutorial, but the most important thing to remember is that the GPU hardware is designed for data parallelism. Tutorial: CUDA programming in Python with numba and cupy Richard Sutton – Father of RL thinks LLMs are a dead end CuPy A NumPy compatible Library for the GPU - Sean Farley Feb 1, 2024 · CUPY is a Numpy-like array implementation for NVIDIA CUDA. 0 documentation). See their high-performance computing advantages, and use CuPy and hipDF in a detailed example of an investment portfolio allocation optimization using the Markowitz model. However, when I try to accomplish the same task by cudaMemcpy of frame buffer into Unified Memory ( memory allocated using cudaMallocManaged )the time taken by the kernel is more Oct 15, 2025 · GPU vs CPU performance examples using OpenCV CUDA and CuPy - mirzafahad/opencv-cupy-cuda-benchmarks Aug 18, 2025 · CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. I installed cupy by instructions, but nothing worked. Why GPUs? Originally (80s-90s) built for graphics, called Video Graphics Arrays/ Adapters (VGAs) In 2007, Nvidia introduces CUDA to facilitate general-purpose application development May 6, 2025 · Learn how to deploy CuPy and hipDF on AMD GPUs. For most users, use of pre-build wheel distributions are recommended: cupy-cuda13x (for CUDA 13. We encourage interested readers to check out Dask’s Blog which has more timely updates on ongoing work. However, I still have lingering questions that haven’t been resolved: Writing code using Python-style expressions in a Python Aug 1, 2019 · In this tutorial, we show how the CUDA Array and DLPack interfaces allow us to share our data between cuDF and CuPy in microseconds. Different GPU kernsls are executed by separate streams in Cupy. In this documentation, we describe how to define and call each kernels. You need to install CUDA Toolkit 13. RawKernel (like @nb. block_size is set to 32 in cuda_matvec. CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. Jan 30, 2018 · CUDA device memory copies: cudaMemcpyDeviceToDevice vs copy kernel Asked 11 years, 8 months ago Modified 5 years, 11 months ago Viewed 15k times NumPy compatible GPU library for fast computation in Python Preferred Networks Crissman Loomis crissman@preferred. CUFFT using BenchmarkTools A Mar 19, 2021 · RAPIDS is designed to create seamless connections between GPU PyData libraries, allowing for easy interoperability between libraries like cuDF and CuPy. We welcome contributions for these functions. CuPy acts as a drop-in replacement to run existing NumPy/SciPy code on NVIDIA CUDA or AMD ROCm platforms. 2 ~ 11. I obtain my frame using NVBuffer which I cudaMemcpy into Zero Copy Memory ( memory allocated using cudaHostAlloc ) and use it to do the operation. The above pip install instruction is compatible with conda environments. This allows direct-to-GPU reads and GPU-native analytics on existing pipelines 🎉 😱 🤯 🥳. CompileException # If CuPy raises a CompileException for almost everything, it is possible that CuPy cannot detect CUDA installed on your system correctly. Async (Non-Blocking) Calls Copy Data from Host to Device CuPy now loads NCCL shared library at the time of import cupy. Taichi CUDA is faster, trailing behind cuda_matvec. CuPyを使う CuPyのインストール方法 CUDA SDKをインストールする必要ならcuDNN・NCCLをインストール Jan 16, 2023 · The 1,000-foot summary is that the default software stack for machine learning models will no longer be Nvidia’s closed-source CUDA. 2+) x86_64 / aarch64 pip install cupy-cuda11x CUDA 12. compiler. By replacing NumPy with CuPy syntax, you can run your code on NVIDIA CUDA or AMD ROCm platforms. Data copies and kernel launches Mar 17, 2023 · Is it possible to access CuPy array/memory directly within cuda_GpuMat to support CudaArrayInterface? Implemented a wrapper of CudaArrayInterface works fine to move GpuMat memory directly to CuPy. From my search, the ability to write CUDA code with a syntax similar to Python using CuPy and Numba’s CUDA seems appealing, and I am currently proceeding with coding in this manner. Taichi's performance is comparable to the highly optimized CUB and CuPy versions and outruns Thrust at all data sizes by a large margin. CuPy provides a ndarray, sparse matrices, and the associated routines for GPU devices, all having the same API as NumPy and SciPy: CuPy - It is an open-source matrix library accelerated with NVIDIA CUDA. 6, we can run the following code to install CuPy. The kernel is compiled at an invocation of the __call__() method, which is cached for each Jun 9, 2023 · I am trying to learn GPU acceleration using Numba and CuPy for my research work. I want t However CuPy counterparts return zero-dimensional cupy. 0 CUDA math library, this post introduces a variety of usage modes beyond that, specifically usage from Python and Julia. CUDA Python can interoperate with most or all of them. CuPy uses NVIDIA CUDA to run operations on the GPU, which can provide significant performance improvements for numerical computations compared to running on the CPU, especially at larger data sizes. GPU hardware is designed for data parallelism, where high throughputs are achieved when the GPU is Oct 9, 2025 · CUDA Runtime API (PDF) - v13. However, it failed with moving from CuPy array to GpuMat. Sep 14, 2022 · I have an old GPU GTX 870m. 8. The CUDA Array and DLPack interfaces enable data sharing between cuDF and CuPy in microseconds, giving users near-instant access to the strengths of both libraries. Interoperability between cuDF and CuPy # This notebook provides introductory examples of how you can use cuDF and CuPy together to take advantage of CuPy array functionality (such as advanced linear algebra operations). Discover the CuPy advantages and how they can use it to experience performance gains in their NumPy codes without any major changes. May 9, 2024 · The frameworks Numba CUDA, Taichi Vulkan, and Taichi OpenGL perform similarly. NumPy and SciPy on Multi-Node Multi-GPU systems. x) cupy-rocm-5-0 1 day ago · Output (on RTX 3060): PyTorch GPU Time: 0. CuPy supports Nvidia CUDA GPU platform, and Basics of CuPy # In this section, you will learn about the following things: Basics of cupy. For OpenCL, you must move the data through the host (NumPy) first if you want to utilize PyTorch. jp Shunta Saito shunta@preferred. If your workflow involves heavy data manipulation (e. Website | Install | Tutorial | Examples | Documentation | API Reference | Forum CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.