Gautam Bose GautamBose

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

How to migrate Django from SQLite to PostgreSQL

Dump existing data:

python3 manage.py dumpdata > datadump.json

Change settings.py to Postgres backend.

Make sure you can connect on PostgreSQL. Then:

Row	Season	No. in series	Company	Deal	Industry	Entrepreneur Gender	Amount	Equity	Valuation	Corcoran	Cuban	Greiner	Herjavec	John	O'Leary	Harrington	Guest	# Sharks	$ per shark	Details / Notes

2	1	1	Ava the Elephant	Yes	Healthcare	Female	$50,000	55%	$90,909	1								1	$50,000
3	1	1	Mr. Tod's Pie Factory	Yes	Food and Beverage	Male	$460,000	50%	$920,000	1				1				2	$230,000
4	1	1	Wispots	No	Business Services	Male												0
5	1	1	College Foxes Packing Boxes	No	Lifestyle / Home	Male												0
6	1	1	Ionic Ear	No	Uncertain / Other	Male												0
7	1	2	A Perfect Pear	Yes	Food and Beverage	Female	$500,000	50%	$1,000,000				1		1			2	$250,000
8	1	2	Classroom Jams	Yes	Children / Education	Male	$250,000	10%	$2,500,000	1	1		1	1	1			5	$50,000
9	1	2	Lifebelt	No	Consumer Products	Male												0

	<script>
	// Based on code from https://stackoverflow.com/questions/59629947/how-do-i-load-an-external-js-library-in-svelte-sapper
	import { onMount, createEventDispatcher } from "svelte";

	const dispatch = createEventDispatcher();
	export let src;
	export let libraryDetectionObject;
	let script;

	onMount(() => {

	import tensorflow as tf
	from tensorflow.python.platform import gfile
	import numpy as np

	from imagenet_classes import class_names
	from scipy.misc import imread, imresize

	dir_name = 'mobilenet_v1_1.0_224'
	with tf.Graph().as_default() as graph:
	with tf.Session() as sess:

	# zmqimage.py -- classes to send, receive and display cv2 images via zmq
	# based on serialization in pyzmq docs and pyzmq/examples/serialization

	'''
	PURPOSE:
	These classes allow a headless (no display) computer running OpenCV code
	to display OpenCV images on another computer with a display.
	For example, a headless Raspberry Pi with no display can run OpenCV code
	and can display OpenCV images on a Mac with a display.
	USAGE:

	/*
	Reading one Rotary Encoder from a Raspberry Pi

	Translated from C to Processing by Heracles Papatheodorou
	via http://theatticlight.net/posts/Reading-a-Rotary-Encoder-from-a-Raspberry-Pi/


	GND MIDDLE encoder leg
	GPIO22 GPIO23 LEFT and RIGHT legs
	3.3V LEFT and RIGHT, splits to two 10k resistors for pull-up

	import PIL.Image
	from cStringIO import StringIO
	import IPython.display
	import numpy as np
	def showarray(a, fmt='png'):
	a = np.uint8(a)
	f = StringIO()
	PIL.Image.fromarray(a).save(f, fmt)
	IPython.display.display(IPython.display.Image(data=f.getvalue()))

	Number.prototype.map = function (in_min, in_max, out_min, out_max) {
	return (this - in_min) * (out_max - out_min) / (in_max - in_min) + out_min;
	}

	// Based on http://stackoverflow.com/a/10520017/1307721 and http://stackoverflow.com/a/16022728/1307721

	Podium = {};

	Podium.keydown = function(k) {
	var oEvent = document.createEvent('KeyboardEvent');

	// Chromium Hack
	Object.defineProperty(oEvent, 'keyCode', {
	get : function() {