python cuda numpy

CuPy supports various methods, indexing, data types, broadcasting and more. jetson-utils / python / examples / cuda-from-numpy.py / Jump to. Use Tensor.cpu() to copy the tensor to host memory first.” when I am calculating cosine-similarity in bert_1nn. 分类专栏: 深度学习环境配置 文章标签: gpu cuda python numpy. Follow edited Jun 6 '19 at 8:01. Code navigation not available for this commit ... ArgumentParser (description = 'Copy a test image from numpy to CUDA and save it to disk') parser. It translates Python functions into PTX code which execute on the CUDA hardware. Here is an image of writing a stencil computation that smoothes a 2d-image all from within a … Writing CUDA-Python¶. This didn’t happen when I run the code on CPU. CuPy is an open-source array library accelerated with NVIDIA CUDA. A NumPy-compatible array library accelerated by CUDA. The programming effort required can be as simple as adding a function decorator to instruct Numba to compile for the GPU. Code definitions. Peruse NumPy GPU acceleration for a pretty good overview and links to other Python/GPU libraries. It translates Python functions into PTX code which execute on the CUDA hardware. Python での 高速計算。 NumPy 互換 GPU 計算ライブラリ cupy ... GPU計算には、例えば NVIDIA が提供するライブラリの CUDA を呼び出して実行する必要があります。 しかしそのインターフェースは非常に低レベルで、なかなか素人が気軽に使えるものではありません。 1.1 list 转 numpy ndarray = np.array(list) 1.2 numpy 转 list list = ndarray.tolist() 2.1 list 转 torch. It was updated on September 19, 2017.]. To get started with Numba, the first step is to download and install the Anaconda Python distribution, a “completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing” that includes many popular packages (Numpy, Scipy, Matplotlib, iPython, etc) and “conda”, a powerful package manager. Occasionally it showed that the Python … Numpy.GPU是一个面向Numpy的Gpu加速库,基于Cuda。 注:您必须拥有一块NVIDIA的显卡(支持cuda)才能享受加速效果。 二、安装教程 Check out the hands-on DLI training course: NVIDIA websites use cookies to deliver and improve the website experience. 最后发布:2017-11-24 11:23:44 首次发布:2017-11-24 11:23:44. Numba exposes the CUDA programming model, just like in CUDA C/C++, but using pure python syntax, so that programmers can create custom, tuned parallel kernels without leaving the comforts and advantages of Python behind. Part 1 can be found here. 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, ... (torch.cuda.FloatTensor if torch.cuda.is_available() ... python-3.x numpy gpu pytorch  Share. Nov 19, 2017. How do I solve this error? Python libraries written in CUDA like CuPy and RAPIDS 2. User-Defined Kernels tutorial. Because the pre-built Windows libraries available for OpenCV 4.3.0 do not include the CUDA modules, or support for the Nvidia Video Codec […] 使用Python写CUDA程序有两种方式: Numba; PyCUDA; numbapro现在已经不推荐使用了,功能被拆分并分别被集成到accelerate和Numba了。. Numba’s CUDA JIT (available via decorator or function call) compiles CUDA Python functions at run time, specializing them for the types you use, and its CUDA Python API provides explicit control over data transfers and CUDA streams, among other features. jit def invert_color (img_in, img_out): """画像の色を反転させるカーネル関数""" x, y = cuda. CuPy uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL to make full use of the GPU architecture. Use this guide for easy steps to install CUDA. JAX: Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU. Read the original benchmark article Here is an image of writing a stencil computation that smoothes a 2d-image all from within a Jupyter Notebook: JAX: Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU. Improve this question. Numba.cuda.jit allows Python users to author, compile, and run CUDA code, written in Python, interactively without leaving a Python session. Based on Python programming language. Hardware and Software Setup. Pandas and/or Numba ok. Xarray: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization: Sparse CuPy speeds up some operations more than 100X. Hi, I try to run my code on teaching lab GPU and got this error: “can’t convert cuda:0 device type tensor to numpy. It was updated on September 19, 2017.] The data parallelism in array-oriented computing tasks is a natural fit for accelerators like GPUs. oat32) 6 a gpu =cuda.mem alloc(a.nbytes) 7cuda.memcpy htod(a gpu, a) [This is examples/demo.py in the PyCUDA distribution.] Check out the hands-on DLI training course: Fundamentals of Accelerated Computing with CUDA Python [Note, this post was originally published September 19, 2013. Compiled binaries are cached and reused in subsequent runs. Not exactly. Use this guide for easy steps to install CUDA. 使用Python写CUDA程序. Most operations perform well on a GPU using CuPy out of the box. Numba works by allowing you to specify type signatures for Python functions, which enables compilation at run time (this is “Just-in-Time”, or JIT compilation). Numba.cuda.jit allows Python users to author, compile, and run CUDA code, written in Python, interactively without leaving a Python session. Using the simulator; Supported features; GPU Reduction. Many consider that NumPy is the most powerful package in Python. CuPy : A NumPy-compatible array library accelerated by CUDA CuPy is an implementation of NumPy-compatible multi-dimensional array on CUDA. The jit decorator is applied to Python functions written in our Python dialect for CUDA.Numba interacts with the CUDA Driver API to load the PTX onto the CUDA device and execute. With that implementation, superior parallel speedup can be achieved due to the many CUDA cores GPUs have. pip python 3 Writing CUDA-Python¶. Please read the Access GPU CUDA, cuDNN and NCCL functionality are accessed in a Numpy-like way from CuPy.CuPy also allows use of the GPU in a more low-level fashion as well. CuPy is an implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy provides GPU accelerated computing with Python. This is a blog on optimizing the speed of Python. shape [0] and y < img_in. Perhaps most important, though, is the high productivity that a dynamically typed, interpreted language like Python enables. We will use the Google Colab platform, so you don't even need to own a GPU to run this tutorial. Numpy has been a gift to the Python community. $ python speed.py cpu 100000 Time: 0.0001056949986377731 $ python speed.py cuda 100000 Time: 0.11871792199963238 $ python speed.py cpu 11500000 Time: 0.013704434997634962 $ python speed.py cuda 11500000 Time: 0.47120747699955245 In the meantime I was monitoring the GPU using nvidia-smi. (c) Lison Bernet 2019 Introduction In this post, you will learn how to do accelerated, parallel computing on your GPU with CUDA, all in python! The CUDA JIT is a low-level entry point to the CUDA features in Numba. Numpy/CUDA/Python Project. array (Image. No definitions found in this file. The transition from NumPy should be one line. in cudakernel1[1024, 1024](array), is equivalent to launching a kernel with y and z dimensions equal to 1, e.g. Preferred Networks, Inc. & Preferred Infrastructure, Inc. | Design by Styleshout. Looking for more? CuPy uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL to make full use of the GPU architecture. CuPy : A NumPy-compatible array library accelerated by CUDA. $ pip3 install numpy Collecting numpy... suppress this warning, use --no-warn-script-location. CuPy automatically wraps and compiles it to make a CUDA binary. Python is a high-productivity dynamic programming language that is widely used in science, engineering, and data analytics applications. grid (2) if x < img_in. Python中 list, numpy.array, torch.Tensor 格式相互转化 - SiyuanChen - 博客园 首页 See our. SETUP CUDA PYTHON To run CUDA Python, you will need the CUDA Toolkit installed on a system with CUDA capable GPUs. Writing CUDA-Python¶. Its data is allocated on the current device, which will be explained later.. It translates Python functions into PTX code which execute on the CUDA hardware. Numba is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code. Successfully installed numpy-1.19.0. CuPy's interface is highly compatible with NumPy; in most cases it can be used as a drop-in replacement. Pytorch 中,如果直接从 cuda 中取数据,如 var_tensor.cuda().data.numpy(), import torch var_tensor = torch.FloatTensor(2,3) if torch.cuda.is_available(): # 判断 GPU 是否可用 var_tensor.cuda().data.numpy() 则会出现如下类似错误: TypeError: can't convert CUDA tensor to numpy. One of the strengths of the CUDA parallel computing platform is its breadth of available GPU-accelerated libraries. CUDA toolkit: 8.0 ~ Python: 3.5.1 ~ Numpy: 1.9 ~ 推奨 OS は Ubuntu 16.04/18.04, CentOS 7 で,Windows でも動作可能なようです.最近の macOS 搭載 PC は Radeon GPU なので CUDA が対応し … jupyter visualization python CUDA NumPy PyTorch video. CuPy, which has a NumPy interface for arrays allocated on the GPU. We’re improving the state of scalable GPU computing in Python. For example the following code generates a million uniformly distributed random numbers on the GPU using the “XORWOW” pseudorandom number generator. Part 1: From Math to Code . This is the second part of my series on accelerated computing with python: Part I : Make python fast with numba : accelerated python on the CPU If you don’t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. CuPy is an implementation of NumPy-compatible multi-dimensional array on CUDA. 例子 numba. CuPy can also be installed from source code. How do I solve this error? Network communication with UCX 5. It supports a subset of numpy.ndarray interface. Network communication with UCX 5. In this post I’ll introduce you to Numba, a Python compiler from Anaconda that can compile Python code for execution on CUDA-capable GPUs or multicore CPUs. Numba’s ability to dynamically compile code means that you don’t give up the flexibility of Python. The answer is of course that running native, compiled code is many times faster than running dynamic, interpreted code. Numpy support; Supported Atomic Operations. This disables a large number of NumPy APIs. The install script in the source code automatically detects installed versions of CUDA, cuDNN and NCCL in your environment. Numba provides Python developers with an easy entry into GPU-accelerated computing and a path for using increasingly sophisticated CUDA code with a minimum of new syntax and jargon. NumPy makes it easy to process vast amounts of data in a matrix format in an efficient way. As in other CUDA languages, we launch the kernel by inserting an “execution configuration” (CUDA-speak for the number of threads and blocks of threads to use to run the kernel) in brackets, between the function name and the argument list: mandel_kernel[griddim, blockdim](-2.0, 1.0, -1.0, 1.0, d_image, 20). Numba, which allows defining functions (in Python!) The CUDA JIT is a low-level entry point to the CUDA features in NumbaPro. You can get the full Jupyter Notebook for the Mandelbrot example on Github. It is accelerated with the CUDA platform from NVIDIA and also uses CUDA-related libraries, including cuBLAS, cuDNN, cuRAND, cuSOLVER, cuSPARSE, and … Writing CUDA-Python¶. Optionally, CUDA Python can provide Fundamental package for scientific computing with Python on conventional CPUs. Numba is designed for array-oriented computing tasks, much like the widely used NumPy library. It has good debugging and looks like a wrapper around CUDA kernels. But you should be able to come close. Notebook ready to run on the Google Colab platform ... import numpy as np a = np. 分类专栏: 深度学习环境配置 文章标签: gpu cuda python numpy. © Device Selection; The Device List; Examples. This package (cupy) is a source distribution. $ python speed.py cpu 100000 Time: 0.0001056949986377731 $ python speed.py cuda 100000 Time: 0.11871792199963238 $ python speed.py cpu 11500000 Time: 0.013704434997634962 $ python speed.py cuda … Broadly we cover briefly the following categories: 1. Three different implementations with numpy, cython and pycuda. Writing CUDA-Python¶. Raw modules. You can speedup your Python and NumPy codes using CuPy, which is an open-source matrix library accelerated with NVIDIA CUDA. On a server with an NVIDIA Tesla P100 GPU and an Intel Xeon E5-2698 v3 CPU, this CUDA Python Mandelbrot code runs nearly 1700 times faster than the pure Python version. For detailed instructions on installing CuPy, see the installation guide. Please read cuda编程部分基本和c++上是一致的 可参考c++版的: CUDA编程基本入门学习笔记 看懂上面链接之后就很好懂numba的python代码了 下面直接放代码了: from numba import cuda,vectorize import numpy as np import math from timeit import default_timer as timer def func_cpu(a,b,c,th): for y in range(a.shape[0]): f 3 import numpy 4 5 a =numpy.random.randn(4,4).astype(numpy. python和cuda交互:Pycuda安装(填坑) 国家二级退堂骨演奏家 回复 北漂客: 谢谢楼主,准备换装备了。 python和cuda交互:Pycuda安装(填坑) 北漂客 回复 国家二级退堂骨演奏家: 首先确认安装好cuda,cudnn 本人电脑: cuda9.0 cudnn7.3这些版本需要对应的 We’re going to dive right away into how to parse Numpy arrays in C and use CUDA to speed up our computations. Example; Random Number Generation. Turing T4 GPU block diagram Introduction In this post, you will learn how to write your own custom CUDA kernels to do accelerated, parallel computing on a GPU, in python with the help of numba and CUDA. The jit decorator is applied to Python functions written in our Python dialect for CUDA.Numba interacts with the CUDA Driver API to load the PTX onto the CUDA device and execute. But Python’s greatest strength can also be its greatest weakness: its flexibility and typeless, high-level syntax can result in poor performance for data- and computation-intensive programs. This is a blog on optimizing the speed of Python. High performance with CUDA. Here is the ... Use pip3 of Python to install NumPy. Many consider that NumPy is the most powerful package in Python. The CUDA JIT is a low-level entry point to the CUDA features in Numba. Uses C/C++ combined with specialized code to accelerate computations. Based on Python programming language. We have three implementation of these algorithms for benchmarking: Python Numpy library; Cython; Cython with multi-cpu (not yet available) CUDA with Cython (Not available. float32) # move input data to the device d_a = cuda. This comparison table shows a list of NumPy / SciPy APIs and their corresponding CuPy implementations. Python-CUDA compilers, specifically Numba 3. You can easily make a custom CUDA kernel if you want to make your code run faster, requiring only a small code snippet of C++. Notice the mandel_kernel function uses the cuda.threadIdx, cuda.blockIdx, cuda.blockDim, and cuda.gridDim structures provided by Numba to compute the global X and Y pixel indices for the current thread. Supported numpy features: accessing ndarray attributes .shape, .strides, .ndim, .size, etc.. To compile and run the same function on the CPU, we simply change the target to ‘cpu’, which yields performance at the level of compiled, vectorized C code on the CPU. Many applications will be able to get significant speedup just from using these libraries, without writing any GPU-specific code. [Note, this post was originally published September 19, 2013. NumPy can be installed with conda, with pip, with a package manager on macOS and Linux, or from source. For best performance, users should write code such that each thread is dealing with a single element at a time. Matrix multiplication; Debugging CUDA Python with the the CUDA Simulator. Numba supports CUDA-enabled GPU with compute capability (CC) 2.0 or above with an up-to-data Nvidia driver. in cudakernel1[1024, 1024](array), is equivalent to launching a kernel with y and z dimensions equal to 1, e.g. OpenCV 4.5.0 (changelog) which is compatible with CUDA 11.1 and cuDNN 8.0.4 was released on 12/10/2020, see Accelerate OpenCV 4.5.0 on Windows – build with CUDA and python bindings, for the updated guide. The GPU backend of Numba utilizes the LLVM-based NVIDIA Compiler SDK. Launching a kernel specifying only two integers like we did in Part 1, e.g. The jit decorator is applied to Python functions written in our Python dialect for CUDA.NumbaPro interacts with the CUDA Driver API to load the PTX onto the CUDA device and execute. You can install CuPy using pip: The latest version of cuDNN and NCCL libraries are included in binary packages (wheels).For the source package, you will need to install cuDNN/NCCL before installing CuPy, if you want to use it. The Plan ; Hang on...what is a Julia fractal? numba.cuda.cudadrv.driver.CudaAPIError: [1] Call to cuLaunchKernel results in CUDA_ERROR_INVALID_VALUE Even when I got close to the limit the CPU was still a lot faster than the GPU. from numba import cuda import numpy as np from PIL import Image @ cuda. In this introduction, we show one way to use CUDA in Python, and explain some basic principles of CUDA programming. The only prerequisite for installing NumPy is Python itself. Work needs to be done to write compiler wrapper for nvcc, to be called from python. Learn More Try Numba » ... With support for both NVIDIA's CUDA and AMD's ROCm drivers, Numba lets you write parallel GPU algorithms entirely from Python. This is a CuPy wheel (precompiled binary) package for CUDA … You can start with simple function decorators to automatically compile your functions, or use the powerful CUDA libraries exposed by pyculib. Example; Device management. CUDA Python¶ We will mostly foucs on the use of CUDA Python via the numbapro compiler. Low level Python code using the numbapro.cuda module is similar to CUDA C, and will compile to the same machine code, but with the benefits of integerating into Python for use of numpy arrays, convenient I/O, graphics etc. Scaling these libraries out with Dask 4. Andreas Kl ockner PyCUDA: Even Simpler GPU Programming with Python @harrism on Twitter, DGX-2 Server Virtualization Leverages NVSwitch for Faster GPU Enabled Virtual Machines, RAPIDS Accelerates Data Science End-to-End, CUDA 10 Features Revealed: Turing, CUDA Graphs, and More. If you do not have a CUDA-capable GPU, you can access one of the thousands of GPUs available from cloud service providers including Amazon AWS, Microsoft Azure and IBM SoftLayer.The NVIDIA-maintained CUDA Amazon … The easiest way to install CuPy is to use pip. 最后发布:2017-11-24 11:23:44 首次发布:2017-11-24 11:23:44. Casting behaviors from float to integer are defined in CUDA specification. (c) Lison Bernet 2019 Introduction In this post, you will learn how to do accelerated, parallel computing on your GPU with CUDA, all in python! that can be used as GPU kernels through numba.cuda.jit and numba.hsa.jit. Single-GPU CuPy Speedups on the RAPIDS AI Medium blog. Python libraries written in CUDA like CuPy and RAPIDS 2. For most users, use of pre-build wheel distributions are recommended: cupy-cuda111 (for CUDA 11.1) cupy-cuda110 (for CUDA 11.0) cupy-cuda102 (for CUDA 10.2) For example, the @vectorize decorator in the following code generates a compiled, vectorized version of the scalar function Add at run time so that it can be used to process arrays of data in parallel on the GPU. CuPy is a library that implements Numpy arrays on Nvidia GPUs by leveraging the CUDA GPU library. Andreas Kl ockner PyCUDA: Even Simpler GPU Programming with Python Enter numba.cuda.jit Numba’s backend for CUDA. It translates Python functions into PTX code which execute on the CUDA hardware. Pac… You can also see the use of the to_host and to_device API functions to copy data to and from the GPU. As you advance your understanding of parallel programming concepts and when you need expressive and flexible control of parallel threads, CUDA is available without requiring you to jump in on the first day. 1700x may seem an unrealistic speedup, but keep in mind that we are comparing compiled, parallel, GPU-accelerated Python code to interpreted, single-threaded Python code on the CPU. Python is a high-productivity dynamic programming language that is widely used in science, engineering, and data analytics applications. This post lays out the current status, and describes future work. I also recommend that you check out the Numba posts on Anaconda’s blog. There are a number of factors influencing the popularity of python, including its clean and expressive syntax and standard data structures, comprehensive “batteries included” standard library, excellent documentation, broad ecosystem of libraries and tools, availability of professional support, and large and open community. The CUDA JIT is a low-level entry point to the CUDA features in NumbaPro. On a server with an NVIDIA Tesla P100 GPU and an Intel Xeon E5-2698 v3 CPU, this CUDA Python Mandelbrot code runs nearly 1700 times faster than the pure Python version. Numba is a BSD-licensed, open source project which itself relies heavily on the capabilities of the LLVM compiler. The only difference is writing the vectorAdd kernel and linking the libraries. Use Tensor.cpu() to copy the tensor to host memory first.” when I am calculating cosine-similarity in bert_1nn. 作为 Python 语言的一个扩展程序库,Numpy 支持大量的维度数组与矩阵运算,为 Python 社区带来了很多帮助。借助于 Numpy,数据科学家、机器学习实践者和统计学家能够以一种简单高效的方式处理大量的矩阵数据。那么… There are a number of projects aimed at making this optimization easier, such as Cython, but they often require learning a new syntax. It also summarizes and links to several other more blogposts from recent months that drill down into different topics for the interested reader. PythonからGPU計算を行うライブラリは複数ありますが、Cupyの特徴はなんといっても、 NumPy と(ほとんど)同じAPIを提供する点です。 そのため、使い方の習得が容易で、デバッグも非常に簡単で … Before starting GPU work in any … Nvidia Cuda can accelerate C or Python by GPU power. CUDA can operate on the unpackaged Numpy arrays in the same way that we did with our for loop in the last example. The following code example demonstrates this with a simple Mandelbrot set kernel. Boost python with numba + CUDA! Write your own CUDA kernels in python to accelerate your computing on the GPU. We have three implementation of these algorithms for benchmarking: Python Numpy library; Cython; Cython with multi-cpu (not yet available) CUDA with Cython (Not available. For most users, use of pre-build wheel distributions are recommended: cupy-cuda111 (for CUDA 11.1) cupy-cuda110 (for CUDA 11.0) cupy-cuda102 (for CUDA 10.2) Numba understands NumPy array types, and uses them to generate efficient compiled code for execution on GPUs or multicore CPUs. ... CuPy is a library that implements Numpy arrays on Nvidia GPUs by leveraging the CUDA GPU library. All you need to do is just replace No previous knowledge of CUDA programming is required. NumPy competency, including the use of ndarrays and ufuncs. CuPy consists of cupy.ndarray, the core multi-dimensional array class, and many functions on it. If you do not have a CUDA-capable GPU, you can access one of the thousands of GPUs available from cloud service providers including Amazon AWS, Microsoft Azure and IBM SoftLayer.The NVIDIA-maintained CUDA Amazon Machine … NumPy makes it easy to process vast amounts of data in a matrix format in an efficient way. Coding directly in Python functions that will be executed on GPU may allow to remove bottlenecks while keeping the code short and simple. Python-CUDA compilers, specifically Numba 3. Once you have Anaconda installed, install the required CUDA packages by typing conda install numba cudatoolkit pyculib. This disables a large number of NumPy APIs. However, it is wise to use GPU with compute capability 3.0 or above as this allows for double precision operations. This didn’t happen when I run the code on CPU. arange (256 * 1000000, dtype = np. Install CuPy for more details. It translates Python functions into PTX code which execute on the CUDA hardware. Another project by the Numba team, called pyculib, Fundamentals of Accelerated Computing with CUDA Python, Jupyter Notebook for the Mandelbrot example, Follow Python is nimble and flexible, making it a great language for quick prototyping, but also for building complete systems. The figure shows CuPy speedup over NumPy. I am looking for an expert-level, reliable numpy developer who can start an existing python project (that already uses numpy) but the components needs to be made much more capable and better performing. shape [1]: img_out [x, y] = 0xFF-img_in [x, y] # 画像を読み込んで NumPy 配列に変換する img = np. Ideally, Python programmers would like to make their existing Python code faster without using another programming language, and, naturally, many would like to use accelerators to get even higher performance from their code. For best performance, users should write code such that each thread is dealing with a single element at a time. And it can also accelerate the existing NumPy code through GPU and CUDA libraries. Work needs to be done to write compiler wrapper for nvcc, to be called from python. These packages include cuDNN and NCCL. Anaconda (formerly Continuum Analytics) recognized that achieving large speedups on some computations requires a more expressive programming interface with more detailed control over parallelism than libraries and automatic loop vectorization can provide. The Basics of CuPy tutorial is useful to learn first steps with CuPy. We’re improving the state of scalable GPU computing in Python. CuPy provides GPU accelerated computing with Python. This package (cupy) is a source distribution. CuPy : A NumPy-compatible array library accelerated by CUDA. Xarray: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization: Sparse CuPy provides wheels (precompiled binary packages) for the recommended environments. The jit decorator is applied to Python functions written in our Python dialect for CUDA.NumbaPro interacts with the CUDA Driver API to load the PTX onto the CUDA device and execute. Defining the complex plane ... both in NumPy and in PyTorch. Then check out the Numba tutorial for CUDA on the ContinuumIO github repository. CuPy is an open-source array library accelerated with NVIDIA CUDA. Uses NumPy syntax but can be used for GPUs. Supported numpy features: accessing ndarray attributes .shape, .strides, .ndim, .size, etc.. 1700x may seem an unrealistic speedup, but keep in mind that we are comparing compiled, parallel, GPU-accelerated Python code to interpreted, single-threaded Python code on the CPU. The figure shows CuPy speedup over NumPy. cupy in your Python code.

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