Mamy Ratsimbazafy mratsim

libdispatch efficiency tips

I suspect most developers are using the libdispatch inefficiently due to the way it was presented to us at the time it was introduced and for many years after that, and due to the confusing documentation and API. I realized this after reading the 'concurrency' discussion on the swift-evolution mailing-list, in particular the messages from Pierre Habouzit (who is the libdispatch maintainer at Apple) are quite enlightening (and you can also find many tweets from him on the subject).

My take-aways are:

You should have very few queues that target the global pool. If all these queues are active at once, you will get as many threads running. These queues should be seen as execution contexts in the program (gui, storage, background work, ...) that benefit from executing in parallel.

SKS Keyserver Network Under Attack

This work is released under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Terminological Note

"OpenPGP" refers to the OpenPGP protocol, in much the same way that HTML refers to the protocol that specifies how to write a web page. "GnuPG", "SequoiaPGP", "OpenPGP.js", and others are implementations of the OpenPGP protocol in the same way that Mozilla Firefox, Google Chromium, and Microsoft Edge refer to software packages that process HTML data.

Who am I?

Python Versus Nim: Async

No performance benchmark.
Python ⟿ PEP-8
Nim ⟿ NEP1
Python ⟿ 3.7
Nim ⟿ 0.19
No Ofuscation, no Code-Golf.

This is to compare elegant, simple, expressive code.

A comparison of Theano with other deep learning frameworks, highlighting a series of low-level design choices in no particular order.

Overview

Differentiation

Differentiation

Symbolic: Theano, CGT; Automatic: Torch, MXNet

Symbolic and automatic differentiation are often confused or used interchangeably, although their implementations are significantly different.

	namespace TryOuts
	{
	using System.Linq;
	using System.Diagnostics;
	using System.Collections.Generic;

	/// <summary>
	/// Tracks usage of a block of 2^14 (16384) slots using a bitmap that
	/// contains the 16384 used/free bit flags for each slot (taking up a
	/// total space of 2048 bytes, accessed as an array of 512 ints), in

	package experimental

	import "encoding/binary"

	// Experimental code! Everything a tree and cached by default.


	type HashFn func(a Root, b Root) Root

	// A link is called to rebind a value

	# This is just an example to get you started. A typical binary package
	# uses this file as the main entry point of the application.

	import strformat, tables, json, strutils, asyncdispatch, asyncnet, strutils, parseutils, options, net
	from cgi import decodeUrl
	import terminaltables
	import mimetypes


	type

	"""
	nim_magic.py
	Jupyter cell magic for your favorite programming language.

	This is now also available as a proper repo:
	https://github.com/apahl/nim_magic

	All further changes will be tracked there.

	Requirements: Nim (https://nim-lang.org), nimpy (`nimble install nimpy`, thanks to @yglukhov for this great library!)

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

	# -- coding: utf-8 --
	"""
	Backprop as the method of Lagrange multiplers (and even the implicit function
	theorem).
	"""
	from __future__ import division
	import numpy as np
	from arsenal.alphabet import Alphabet
	from arsenal.math.checkgrad import finite_difference