Searching for Double Precision Cuda Support information? Find all needed info by using official links provided below.
https://devtalk.nvidia.com/default/topic/669447/support-double-precision-in-cuda/
Jan 26, 2014 · compile with -arch=compute_20,code=sm_20 --> to support double precision and rewrite your code with double precision variable. A Best practice should be to make your cuda methods templated in order to generate the good version at compile time and avoid copy/paste errors.
https://www.nvidia.com/content/cudazone/CUDABrowser/downloads/papers/DoublePrecision-CFD-Cohen-parCFD09.pdf
previously been possible. Our code is implemented using the CUDA platform [1] and is designed to run on the NVIDIA GT200 architecture for GPU computing [3]. The NVIDIA Quadro FX5800 card consists of a single GT200 GPU with 240 cores and 4GB of memory. GT200 supports IEEE-compliant double precision math with peak
http://www.informit.com/articles/article.aspx?p=2103809&seqNum=3
Aug 06, 2013 · CUDA’s hardware support for double precision features full-speed denormals and, starting in SM 2.x, a native fused multiply-add instruction (FMAD), compliant with IEEE 754 c. 2008, that performs only one rounding step.
https://boinc.berkeley.edu/wiki/GPU_computing
Most computers are equipped with a Graphics Processing Unit (GPU) that handles their graphical output, including the 3-D animated graphics used in computer games. The computing power of GPUs has increased rapidly, and they are now often much faster than the computer's main processor, or CPU.
https://www.electronicdesign.com/industrial-automation/article/21121636/should-you-send-a-cpu-to-do-a-gpus-job
2 days ago · Supporting a memory bandwidth of 9000 GB/s, the Nvidia V100 has 640 Tensor cores plus 5120 CUDA cores with 16 GB HBM2. The V100’s specified performance is 7.8 TFLOPS of double-precision ...
https://en.wikipedia.org/wiki/CUDA?oldformat=true
CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach termed GPGPU (General-Purpose computing on Graphics Processing Units).
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