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CUDA-Z is a lightweight and portable software tool that lets you examine technical information which revolves around your computer's hardware configuration when it comes to CUDA-enabled GPUs.
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News Support Get your CUDA-Z >>> This program was born as a parody of another Z-utilities such as CPU-Z and GPU-Z. CUDA-Z shows some basic information about CUDA-enabled GPUs and GPGPUs . It works with nVIDIA Geforce, Quadro and Tesla cards, ION chipsets. CUDA-Z shows following information: Installed CUDA driver and dll version.
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The setup of CUDA development tools on a system running the appropriate version of Windows consists of a few simple steps: Verify the system has a CUDA-capable GPU. Download the NVIDIA CUDA Toolkit. Install the NVIDIA CUDA Toolkit. Test that the installed software runs correctly and communicates with the hardware. 2.1.
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Hi, i'm sorry if this has been asked before, but i couldn't find any references… From what i know, Shader Units that execute CUDA are Vectorial SIMD processors, that support swizzle operators and vector instruction at an hardware level… for example in a shader was perfectly legal to write a thing like "position.xyz /= position.w", or "uv.xy = uv.yx"; scalar operations were in.
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1. Introduction CUDA ® is a parallel computing platform and programming model invented by NVIDIA ®. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU). CUDA was developed with several design goals in mind:
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CUDA C++ Programming Guide 1.1. The Benefits of Using GPUs 1.2. CUDA®: A General-Purpose Parallel Computing Platform and Programming Model 1.3. A Scalable Programming Model 2.1. Kernels 2.2. Thread Hierarchy 2.2.1. Thread Block Clusters 2.3. Memory Hierarchy 2.4. Heterogeneous Programming 2.5. Asynchronous SIMT Programming Model 2.5.1.
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Unified Memory in CUDA makes this easy by providing a single memory space accessible by all GPUs and CPUs in your system. To allocate data in unified memory, call cudaMallocManaged (), which returns a pointer that you can access from host (CPU) code or device (GPU) code. To free the data, just pass the pointer to cudaFree ().