| //#!node | |
| const sleep = (n) => new Promise( (resolve) => setTimeout(resolve, n) ); | |
| const asciichart = require('asciichart') | |
| const screen = { | |
| clear: function () { | |
| console.log('\x1b[2J'); | |
| }, |
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).
Matrix multiplication is a mathematical operation that defines the product of
| // Eg.: http://cho45.github.com/tasscss/ | |
| (function () { | |
| var scripts = document.getElementsByTagName('script'); | |
| var script = scripts[scripts.length-1]; | |
| var container = document.createElement('div'); | |
| script.parentNode.insertBefore(container, script); | |
| var callback; do { | |
| callback = 'jsonp' + Math.floor(Math.random() * 0xffff); |
| require 'fileutils' | |
| # Loop through all .jpg and .png files in the current directory | |
| Dir.glob("{*.jpg,*.png}").each do |img| | |
| # Construct the output filename with .webp extension | |
| output_filename = "#{File.basename(img, File.extname(img))}.webp" | |
| # Execute ffmpeg command to convert the image | |
| system("ffmpeg -i '#{img}' '#{output_filename}'") | |
| end |
| title | type | duration | creator | competencies | ||||
|---|---|---|---|---|---|---|---|---|
Mastering Control Flow and Loops |
lesson |
1:35 |
|
Programming |
You are a computer scientist, VP of engineering at microprocessor design company. You know how x86 and ARM architectures work. You know all about how computers work!
Explain to me, as for computer science student in the first year of studing at university, what is a computer "process" and what is a "thread" and how they correlates.
Certainly! As a first-year computer science student, understanding the concepts of processes and threads is essential in the field of computing.
| 0: ['motion', 'matrix', 'object', 'item', 'function', 'viewpoint', 'point', 'slice', 'agent', 'location'] | |
| 1: ['grid', 'topic', 'method', 'tracking', 'open', 'lattice', 'set', 'functionality', 'model', 'probability'] | |
| 2: ['cloth', 'glyph', 'link', 'cycle', 'protein', 'rule', 'rapport', 'phenomenon', 'contact', 'permission'] | |
| 3: ['community', 'moment', 'multivariate', 'plot', 'invariant', 'slice', 'gesture', 'team', 'training', 'sample'] | |
| 4: ['segmentation', 'relation', 'ensemble', 'proximity', 'modality', 'fiber', 'ner', 'interviewer', 'bin', 'distortion'] |
One of the goals of the concurrency effort is to prevent data races. This document describes the approach taken to preventing data races overall, by categorizing the sources of data races and describing how they are addressed with other proposals in the Swift Concurrency effort.
A data race occurs when two threads access the same memory concurrently and at least one of the accesses can change the value. Within the safe subset of Swift (e.g., ignoring the use of UnsafeMutablePointer and related types), the memory in question is always a stored property. There are several different categories of stored properties that need to be considered for data races:
| <% for ( var i = 0; i < dataObject.length; i++ ) { %> | |
| <article id="<%=dataObject[i].id%>" class="<%=(i % 2 == 1 ? "even" : "")%>"> | |
| <figure class="clearfix"> | |
| <img src="img/<%=i%>_<%=dataObject[i].profile_image_url%>" alt="<%=dataObject[i].id%>" title="<%=dataObject[i].id%>" /> | |
| <figcaption> | |
| <strong><%=dataObject[i].id%></strong> | |
| <span class="text"><%=dataObject[i].text%></span> | |
| <span class="contact">Per contatti: <a href="mailto:<%=dataObject[i].from_user%>"><%=dataObject[i].from_user%></a></span> | |
| </figcaption> | |
| </figure> |
| public struct Heap<T> { | |
| var nodes: [T] = [] | |
| let comparer: (T,T) -> Bool | |
| var isEmpty: Bool { | |
| return nodes.isEmpty | |
| } | |
| init(comparer: @escaping (T,T) -> Bool) { | |
| self.comparer = comparer |
