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It is available as a Visual Studio extension on Visual Studio Marketplace. Microsoft introduced the DirectCompute GPU computing API, released with the DirectX 11 API. Alea GPU, [19] created by QuantAlea, [20] introduces native GPU computing capabilities for the Microsoft .NET languages F# [21] and C#. Alea GPU also provides a simplified GPU ...
C/C++ programmers can use 'CUDA C/C++', compiled to PTX with nvcc, Nvidia's LLVM-based C/C++ compiler, or by clang itself. [10] Fortran programmers can use 'CUDA Fortran', compiled with the PGI CUDA Fortran compiler from The Portland Group. [needs update] Python programmers can use the cuNumeric library to accelerate applications on Nvidia GPUs.
The test functions used to evaluate the algorithms for MOP were taken from Deb, [4] Binh et al. [5] and Binh. [6] The software developed by Deb can be downloaded, [ 7 ] which implements the NSGA-II procedure with GAs, or the program posted on Internet, [ 8 ] which implements the NSGA-II procedure with ES.
TensorFlow serves as a core platform and library for machine learning. TensorFlow's APIs use Keras to allow users to make their own machine-learning models. [33] [43] In addition to building and training their model, TensorFlow can also help load the data to train the model, and deploy it using TensorFlow Serving. [44]
CUDA code runs on both the central processing unit (CPU) and graphics processing unit (GPU). NVCC separates these two parts and sends host code (the part of code which will be run on the CPU) to a C compiler like GNU Compiler Collection (GCC) or Intel C++ Compiler (ICC) or Microsoft Visual C++ Compiler, and sends the device code (the part which will run on the GPU) to the GPU.
Microsoft Visual C++ (MSVC) is a compiler for the C, C++, C++/CLI and C++/CX programming languages by Microsoft.MSVC is proprietary software; it was originally a standalone product but later became a part of Visual Studio and made available in both trialware and freeware forms.
OpenCL (Open Computing Language) is a framework for writing programs that execute across heterogeneous platforms consisting of central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs) and other processors or hardware accelerators.
The CPU first detects OpenCL devices (GPU in this case) and then invokes a just-in-time compiler to translate the OpenCL source code into target binary. CPU then sends data to GPU to perform computations. When the GPU is processing data, CPU is free to process its own tasks.