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CUDA works with all Nvidia GPUs from the G8x series onwards, including GeForce, Quadro and the Tesla line. CUDA is compatible with most standard operating systems. CUDA 8.0 comes with the following libraries (for compilation & runtime, in alphabetical order): cuBLAS – CUDA Basic Linear Algebra Subroutines library; CUDART – CUDA Runtime library
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.
Binary compatible operating systems are OSes that aim to implement binary compatibility with another OS, or another variant of the same brand. This means that they are ABI-compatible (for application binary interface). As the job of an OS is to run programs, the instruction set architectures running the OSes have to be the same or compatible.
The list is organized by guest operating system (the system being emulated), grouped by word length. Each section contains a list of emulators capable of emulating the specified guest, details of the range of guest systems able to be emulated, and the required host environment and licensing.
March 10, 2009 G98 TSMC 65 nm 210 86 PCIe 2.0 x16 567 1400 500 8:8:4 512 8.0 DDR2 64 2.15 4.3 22.4 10.0 3.3 35 OEM products GeForce GT 120 G96b TSMC 55 nm 314 121 500 800 32:16:8 16.0 128 4.4 8.8 89.6 50 GeForce GT 130 G94b 505 196 1250 500 48:24:12 1536 24.0 192 6 12 120 75 GeForce GT 140 650 1625 1800 64:32:16 512 1024 57.6 GDDR3 256 10.4 20.8
Julia is a high-level, general-purpose [17] dynamic programming language, still designed to be fast and productive, [18] for e.g. data science, artificial intelligence, machine learning, modeling and simulation, most commonly used for numerical analysis and computational science.