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High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

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##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

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#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

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import re
_comparer = None

import contextlib

#ALWAYS_INLINE = "__attribute__((__always_inline__))"
ALWAYS_INLINE = "ALWAYS_INLINE"

class CodeGenerator(object):
    def __init__(self):

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#/usr/bin/env python
import math

class RunningCorrelation:
  def __init__(self):
    self.n = 0.0
    self.mean_x = 0.0
    self.mean_y = 0.0
    self.m2_x = 0.0
    self.m2_y = 0.0

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/* 
  A Minimal Capture Program

  This program opens an audio interface for capture, configures it for
  stereo, 16 bit, 44.1kHz, interleaved conventional read/write
  access. Then its reads a chunk of random data from it, and exits. It
  isn't meant to be a real program.

  From on Paul David's tutorial : http://equalarea.com/paul/alsa-audio.html