4/9/2023 0 Comments Fp64 cores pascal![]() ![]() Using FP16 with Tensor Cores in V100 is just part of the picture. NVIDIA Tesla V100 includes both CUDA Cores and Tensor Cores, allowing computational scientists to dramatically accelerate their applications by using mixed-precision. Figure 1: IEEE 754 standard floating point format Figure 1 describes the IEEE 754 standard floating point formats for FP64, FP32, and FP16 precision levels. Using reduced precision levels can accelerate data transfers rates,increase application performance, and reduce power consumption, especially on GPUs with Tensor Core support for mixed-precision. In recent years, the big bang for machine learning and deep learning has focused significant attention on half-precision (FP16). Researchers have experimented with single-precision (FP32) in the fields of life science and seismic for several years. Problem complexity and the sheer magnitude of data coming from various instruments and sensors motivate researchers to mix and match various approaches to optimize compute resources, including different levels of floating-point precision. ![]() However, FP64 also requires more computing resources and runtime to deliver the increased precision levels. Most numerical methods used in engineering and scientific applications require the extra precision to compute correct answers or even reach an answer. Double-precision floating point (FP64) has been the de facto standard for doing scientific simulation for several decades. ![]()
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