Welcome to the Brixton Library Technology Initiative

(Coding for Adults)

ZCDixon@lambeth.gov.uk

BasilBibi@hotmail.com

January 30th 2016

Week 3 – Under The Covers

Technicalities

• Computer architectures – CPU, Ram and disk

• Threads, frames, stack and heap.

• Python - under the covers.

• Garbage Collection.

CPU, Ram, disk and peripherals.• Computers architected in a modular fashion.• Hierarchically organised like most control systems.• Living things, quantum -> cosmological, all knowledge

systems.• Simple sub units are combined into ever more complex

assemblies of functional components.• Gestalt - The whole is greater than the sum of it’s parts.• Emergence - a process whereby larger entities, patterns, and

regularities arise through interactions among smaller or simpler entities that themselves do not exhibit such properties - wikipedia

• Computer’s parts are mainly CPU and storage - RAM and disk.• Other things are considered peripherals and may have their

own CPUs that are subservient to the main CPU.

CPU, Ram, disk and peripherals.

CPU

• CPU – Central Processing Unit.• The main coordinator of all that is going on in the

computer.• A marvel of human achievement in both

engineering and philosophical terms.• Miniaturisation and speed.• An abstraction on the problem domain – any

problem domain.• Does nothing in it’s own right but can be

programmed to do anything that is computationally possible.

CPU

CPU

RAM and diskBoth types of storage.

Data in Ram is lost when we switch off.

Data on disk is saved in ferrous material as magnetic orientations in a ferrous substrate.

Modern storage blurs the lines. Solid state ‘disks’.

How are programs processed?

Instructions are executed by a “thread”

Program data and instructions are kept in memory (ram)

Threads, Frames, Stack and Heap• A ‘thread’ is the computer equivalent of you reading and carrying

out the instructions in your program.• Thought experiment : You as a CPU.

Imagine your eyes reading your code with you performing each statement. Your memory remembers the value of variables as you execute the instructions in sequence.

• In this way you are enacting a cpu running a thread.• A thread is a CPU’s core running through a program using the

instruction pointer (IP) and all registers and their state.• The CPU is where the thread carries out this process.• A CPU with one core can only be executing one thread at any one

time but it can keep track of many threads and switch between them.

• This ‘context switching’ happens so fast that to a human observer many things appear to be are happening ‘at the same time’

Threads, Frames, Stack and Heap• Every time you call a function a whole set of local variables are defined.

• These are kept in a ‘frame’.

def f1(myVar) :localVar = “Hi”print localVar, myVar

def Main() :f1(“hello”)

• When Main calls function f1 – the frame for Main is ‘pushed’ on to a stack and a frame for f1 is created.

• When f1 returns, all the local variables are deleted from memory and the frame is ‘popped’ off the stack.

Threads, Frames, Stack and Heap

def f1(myVar) :localVar = “Hi”print localVar, myVar

def Main() :f1(“hello”)

Threads, Frames, Stack and Heap

• What can go wrong?

def f() :myLocalVar=20f()

• f() calling itself will go on infinitely.• The stack will get longer and longer until you run

out of memory – “stack overflow”

Threads, Frames, Stack and Heap

• The Heap is where objects are created.

• Everything in Python is an object.

• varFloat = 3.145

• Results in a float being created on the heap.

• If we create a lot of objects we run out of heap.

Threads, Frames, Stack and Heap

• Python has two ways of dealing with the large heap this problem.

• Objects that are no longer in use are called garbage.

• You can directly ask Python to collect the garbage

import gccollected = gc.collect() print "Garbage collector: collected %d objects." % (collected)

• Python keeps a count of how many times an object is referenced from another object.

• When the size of the heap reaches a certain threshold the garbage collector kicks in.

• Everything has to stop while it does a GC – causes program jitter.• Called a “Stop The World”

Python - Under The Covers• Python code runs on the Python Virtual Machine.• Virtual Machines written for every OS so your code

will run anywhere.• Python can run as either interpreted or compiled

code.• Python interprets ascii files, compiling on the fly to

create bytecode• Python VM actually runs bytecode which is a series

of instructions like assembler.• Precompile code using CPython which is loaded

directly by the VM.

Python - Under The Covers