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COMP3031: Parallel and Concurrent Programming

创建日期：2024-04-22 15:33:10

所属分类：计算机computer science

标签： COMP3031

Parallel and Concurrent Programming

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COMP3031: Parallel and Concurrent Programming

Concurrency can happen at different levels:
Instruction level - executing two ore more machine instructions
simultaneously.
Statement level.
Unit level - executing two or more subprograms simultaneously.
Program level - executing two or more programs simultaneously.
and can occur either
physically on separate processors or
logically through a single processor.
Motivations for Studying Concurrency
Concurrency is a natural way to conceptualize and solve many
problems
I Concurrency is ubiquitous, and many problems lend themselves
naturally to concurrency in much the same way that recursion is a
natural way to solve a problem.
I This is particularly true in simulation systems: the systems being
simulated contain many independent entities.
I Think of cars in a traffic simulation system, airplanes in a air-control
system,...
Computing devices with multiple processors are becoming more
common, and we need software to make effective use of these devices.
Subprogram-Level Concurrency
Basic Concepts:
Task or process: a piece of code that can be run concurrently.
I Heavyweight tasks: have their own address space and run-time stacks.
I Lightweight tasks: have to share the same address space on the
machine/processor where they are run.
I States of a process: new; runnable or ready; running; blocked; dead.
Synchronization: a mechanism that controls the order in which tasks
execute
I Cooperation synchronization: task A’s job depends on task B.
I Competition synchronization: task A and B compete for a same
resource.
Scheduler: decides which task to run (where there are more ready
tasks than processors).
Examples
A global variable x, and two tasks - A: x = x++; B: x = 2x. How
many possible results if they are performed concurrently:
Producer-consumer problem: a number of producers produce some
products and put them into a queue, and a number of consumers take
them to consume from the queue. How should they be synchronized.
Semaphores
Semaphores are special data structures devised by Dijkstra in 1965 for
synchronizing concurrent tasks. In modern terms, one could think of them
as a class with the following components:
A private data member counter of int type, and a queue of tasks;
two public methods:
I wait - if counter > 0 then counter-- else put the caller in the queue.
I release - if the queue is empty then counter++ else dequeue a waiting
task.

Semaphores: Cooperation Synchronization
Allow simultaneous access to the VALUE queue:
semaphore fullspots, emptyspots;
fullspots.counter = 0;
emptyspots.counter = BUFLEN;
task producer;
while (true) {
-- produce VALUE --
wait(emptyspots);
DEPOSIT(VALUE);
release(fullspots); }
task consumer;
while (true) {
wait(fullspots);
FETCH(VALUE);
release(emptyspots);
-- consume VALUE -- }

Semaphores: Competition Synchronizations
Only one task can access VALUE queue at any time:
semaphore fullspots, emptyspots, access;
fullspots.counter = 0;
emptyspots.counter = BUFLEN;
access.counter = 1;
task producer;
while (true) {
-- produce VALUE --
wait(emptyspots);
wait(access); // wait for access to the queue
DEPOSIT(VALUE);
release(access); //relinguish access
release(fullspots); }

task consumer;
while (true) {
wait(fullspots);
wait(access);
FETCH(VALUE);
release(access);
release(emptyspots);
-- consume VALUE -- }

Java Threads
In Java, tasks are called threads. They are lightweight tasks, meaning
that they can only be run on the same machine.
Java has a build-in class called Thread, from which user-defined
threads are derived.
Threads are initiated by ”start()” method, which in turn calls the
”run()” method.
start() returns immediately, but run() is the life-span of the thread.
Some other methods in Thread:
I yield() - voluntarily surrender the execution of the thread;
I sleep(n) - suspend the thread for n milliseconds;
I thr.join(n) - wait for n millisecond for the thread thr to complete;

Competition Synchronization
In Java, competition synchronization is done by ”synchronized methods”
or ”synchronized statements”: in the following, ”synchronized” is a
reserved word
”synchronized method()” locks the object until the method is finished;
during the execution of method(), no other synchronized method is
allowed to access the object.
class ManageBuf {
...
public synchronized void deposit(int item) { ... }
public synchronized int fetch() { ... }
... }
”synchronized(expression) statement” locks the object specified by
”expression” when ”statement” is executed.

Cooperation Synchronization
Cooperation synchronization in Java is done with ”wait”, ”notify”,
and ”notifyAll” methods in Object - the root class of all Java classes.
Every object has a wait list of all the threads that have called ”wait”
on that object.
The ”notify” method is called to free one of the waiting threads,
randomly.
The ”notifyAll” method awakens all the threads on the object’s
waiting list.
The ”wait” method can throw ”InterruptedException”, so any code
that uses ”wait” must also catch this exception:
try {
while (!condition)
wait();
-- codes after ’condition’ becomes true
} catch (InterruptedException myProblem) { ... }

Producer-Consumer in Java
//A circular queue for storing int values with synchronized
//methods for inserting and removing values from the queue
class Queue {
private int [] que;
private int nextIn, nextOut, filled, queSize;
public Queue(int size) { que = new int [size];
filled = 0; nextIn = 0; nextOut = 0; queSize = size; }
public synchronized void deposit (int item) {
try {
while (filled == queSize) wait();
que [nextIn] = item; nextIn = (nextIn++ % queSize);
filled++; notifyAll(); }
catch (InterruptedException e) {} }

Producer-Consumer in Java (Cont’d)
public synchronized int fetch ( ) {
int item = 0;
try { while (filled == 0) wait();
item=que[nextOut]; nextOut=(nextOut++ % queSize);
filled--; notifyAll(); }
catch (InterruptedException e) {}
return item; }
} // end of Queue class
class Producer extends Thread {
private Queue buffer;
public Producer(Queue que) { buffer = que; }
public void run() {
int new_item;
while (true) { //-- create a new_item
buffer.deposit(new_item); }}}

Producer-Consumer in Java (Cont’d)
class Consumer extends Thread {
private Queue buffer;
public Consumer(Queue que) { buffer = que; }
public void run() {
int stored_item;
while (true) {
stored_item = buffer.fetch();
//-- consume stored_item
}}}
Queue buff1 = new Queue(100);
Producer prod1 = new Producer(buff1);
Consumer cons1 = new Consumer(buff1);
prod1.start(); cons1.start();

Statement-Level Concurrency
Statement-level concurrency is used to inform the compiler of ways it
can map the program onto a multiprocessor machine.
CUDA = a programming language for GPU - later.
HPF - high-performance Fortran (1993), an extension of Fortran 90
to include statements on how concurrent programs should be run on a
multiprocessor machine.
These include specifications about the number of processors,
statements about how data should be distributed over the memories
of these processors, and the alignments of data.

HPF Statements
!HPF$ PROCESSOR procs (n) specifies there are n processors
available.
!HPF$ DISTRIBUTE (kind) ONTO procs :: lists:
I Here ’kind’ can be either BLOCK or CYCLIC, and ’lists’ is the names
of the array variables that are to be distributed.
I BLOCK means that each array is to be divided into n (the number of
processors) groups consisting of contiguous collections of array
elements, and distributed over the memories of the n processors.
Example: a 500 element array BLOCK distribute over 5 processors
means that the first 100 elements on the first processor, the second
100 elements on the second processor, etc..
I CYCLIC means to distribute the 1st element on the 1st processor, 2nd
element on the 2nd processor, ..., nth element on the nth processor,
(n+1)th element on the 1st processor, ...
!HPF$ ALIGN list1(index) WITH list2(index+1) says that for
all values of index, store list1(index) and list2(index+1) in the
memory of the same processor.

Example
REAL list1 (1000), list2(1000)
INTEGER list3(500), list4(501)
!HPF$ PROCESSOR procs (10)
!HPF$ DISTRIBUTE (BLOCK) ONTO procs :: list1, list2
!HPF$ ALIGN list3(index) WITH list4(index+1)
FORALL (index = 1:1000) list1(index) = list2(index)+1.0
FORALL (index = 1:500) list3(index) = list4(index+1)
FORALL statement specifies a collection of statements to be executed
concurrently.

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