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Some very heavily optimized pipelines have yielded speed increases of several hundred times the original CPU-based pipeline on one high-use task. A simple example would be a GPU program that collects data about average lighting values as it renders some view from either a camera or a computer graphics program back to the main program on the CPU ...
TensorFlow since version 1.6 and tensorflow above versions requires CPU supporting at least AVX. [58] Various CPU-based cryptocurrency miners (like pooler's cpuminer for Bitcoin and Litecoin) use AVX and AVX2 for various cryptography-related routines, including SHA-256 and scrypt. FFTW can utilize AVX, AVX2 and AVX-512 when available.
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]
Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. [2] Google began using TPUs internally in 2015, and in 2018 made them available for third-party use, both as part of its cloud infrastructure and by ...
Instead of the MMX registers they use the XMM registers, which are wider and allow for significant performance improvements in specialized applications. Another advantage of replacing MMX with SSE2 is avoiding the mode switching penalty for issuing x87 instructions present in MMX because it is sharing register space with the x87 FPU.
The SX-9 features the first CPU capable of a peak vector performance of 102.4 gigaFLOPS per single core. On February 4, 2008, the NSF and the University of Texas at Austin opened full scale research runs on an AMD , Sun supercomputer named Ranger , [ 44 ] the most powerful supercomputing system in the world for open science research, which ...
The program focuses on commands, in line with the von Neumann [2]: p.3 vision of sequential programming, where data is normally "at rest". [3]: p.7 In contrast, dataflow programming emphasizes the movement of data and models programs as a series of connections.
Transactional Synchronization Extensions (TSX), also called Transactional Synchronization Extensions New Instructions (TSX-NI), is an extension to the x86 instruction set architecture (ISA) that adds hardware transactional memory support, speeding up execution of multi-threaded software through lock elision.