Then put the execution time and matrix size into that formula. For matrix sizes big enough to keep the entire machine busy, the FLOPs is only weakly dependent on matrix size. The GPU matrix multiplication algorithm performs the same number of floating-point operations as the naive algorithm. That's a very unfair comparison. For one the i7-3770k has SIMD as well (8-wide AVX). A better comparison is maximum GFLOPs/s. The max for the 3700k is frequency * 8 (AVX) * 2(simultaneous multiplication and addition) * 4 cores. Sometime it is good, but often it isn’t – it depends on the use-case. One applications of GPUs for hash generation is bitcoin mining. However the main measure of success in bitcoin mining (and cryptocurrency mining in general) is to generate as many hashes per watt of energy; GPUs are in the mid-field here, beating CPUs but are beaten by FPGA and other low-energy hardware. Using an RNS of thirty-two 32-bit moduli, Wang et al  reported a speedup of up to 273.6 for matrix multiplication on GPU. Roy et al.  demonstrated a speedup factor of 24.5 over software Cpu vs gpu processing power What's the Difference Between a CPU vs a GPU? NVIDIA Blo . CPU Vs GPU. Architecturally, the CPU is composed of just a few cores with lots of cache memory that can handle a few software threads at a time.
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An updated deep learning introduction using Python, TensorFlow, and Keras. Text-tutorial and notes: https://pythonprogramming.net/introduction-deep-learning-... In this video we code the matrix product in C++. It is important that we have a working Neural Network library before we worry about performance coding. We also code the Hadamard product, element ... In this video we look at implementing cache tiled matrix multiplication from scratch in CUDA! For code samples: http://github.com/coffeebeforearch For live c... For the Love of Physics - Walter Lewin - May 16, 2011 - Duration: 1:01:26. Lectures by Walter Lewin. They will make you ♥ Physics. Recommended for you This short video will explain you about the "Convolution Function" in Convolution Neural Network, which is an Deep Learning technique for image classification.