cse 160 - lecture 15

CSE 160 - Lecture 15

Introduction to Threads, Synchronization and Mutual

Exclusion

Heavyweight Processes

• Complete stand-alone programs– Code segment– Data Segment

• Static data

– Heap• Malloc’ed data

– Stack– Registers

How can two heavyweight processed communicate

Process 1 Process 2

Shared Memory Segment

myshmPtrmyshmPtr

Communication Socket

or

Shared Memory Segment

• Only a single cpu or multiprocessor shared memory

• A “named” segment of memory that processes attach to – shmat() function call for Unix

• Processes are given pointers to the beginning of the shared memory segment– Structure of the segment contents are not specified

Concurrent Access Problem

ptrY = myshmPtr + sizeof (int);*ptrY = 1;if (*ptrY > 0)*ptrY --;

ptrY = myshmPtr + sizeof (int);*ptrY = 1;if (ptrY > 0)*ptrY --;

int x;int y;int z;

myshmPtrmyshmPtr

What value is y after these programs execute?

Shared Memory Segment

Process 1 Process 2

Mutual Exclusion

• In general, the temporal (time) order in which processes execute code relative to each other is unknown

• Portions of code that modify shared variables are called critical sections– Access to critical shared variables must regulated so

that only one process at a time may have access to the section;

• This is called serialization of access or mutual exclusion

Implementing Mutual Exclusion

• Spin LocksWhile (lock == 1) /* wait */ ;

lock = 1;

<critical section>

lock = 0;

• Busy waiting is inefficient

• Naïve implementation has pitfalls (how?)

Atomic Operations

• Implementing locks, semaphores, monitors requires atomic building blocksload r0, <lock>

cmp r0, 0

jne again:

add r0, 1

store <lock>, r0

Again:A second process could be swapped in. (Simultaneously in an SMP)

Need to make sure all operations complete without interruption (atomically)

Test and Set

• CPU designers recognize this need and have special hardware instructions– test and set

• test for zero, set if not zero

– fetch and increment• fetch location and add one

Semaphores• Introduced by Dijkstra.

– Give a higher-level test and set semantic

• Two operations P and V.– P(semaphore) : if > 0, decrement semaphore, otherwise, wait– V(semaphore): increment semaphore by one– Semaphore initialized > 0

• Provides the functionality needed to implement mutual exclusion

• Standard OS construct– semget(), semctl(), semop() system calls

More Mutual Exclusion

• Monitors– Higher-level than Semaphores making them

less prone to error– To gain access to shared resource, programs

must always go through the monitor.

• Condition variables– Gain access to a resource, when a particular

condition occurs (more later).

Threads

• For SMP, could always use heavyweight processes– Performance penalties– More burden on the programmer to manage

shared structures (“pointer hell”)

• Threads allow concurrency within a single process– Lighter-weight access

Processes and Threads

• Process includes address space.• Thread is program counter and stack pointer.• Process may have many threads.• All the threads share the same address space.• Processes are heavyweight, threads are

lightweight.• Processes/threads need not map one-to-one onto

processors.

Three Threads Within a Process

function f

function g

code

data

heap

PC1

PC2

PC3

stack 1

stack 2

stack 3

SP1

SP2

SP3

Thread Execution Model

pool of threads

pool of processors

•Each thread of control can be scheduled by the OS when it is in a runnable state.

•Threads within one process can run concurrently

• mutual exclustion is very important

Thread Execution Model: Key Points

• Pool of processors, pool of threads.• Threads are peers.• Dynamic thread creation.• Can support many more threads than processors.• Threads dynamically switch between processors.• Threads share access to memory.• Synchronization needed between threads.

Why Use Threads?

• Representing Concurrent Entities– Concurrency is part of the problem specification.– Examples: systems programming and user interfaces.– Single or multiple processors.– This kind of multithreaded programming is difficult.

• Multiprocessing for Performance– Concurrency is under programmer’s control.– Programs could be written sequentially.– This kind of multithreaded programming should be

easier.

Commercial Thread Libraries

• Win32 threads (Windows NT and Windows 95).• Pthreads (POSIX Thread Interface).

(SGI IRIX, Sun Solaris, HP-UX, IBM AIX, Linux, etc.).

• Solaris threads (SunOS 5.x).• All designed primarily for systems programming.

Example: Pthreads

• POSIX Threads – available on many platforms• Thread Management: pthread_create(),

pthread_join(), pthread_exit(), pthread_kill(),pthread_cancel()

• Mutexes: pthread_mutex_create(), pthread_mutex_init(), pthread_mutex_lock(), pthread_mutex_unlock(), pthread_mutux_trylock()

• Events: pthread_cond_init(), pthread_cond_wait(), pthread_cond_timedwait(), pthread_cond_signal()

• Scheduling: pthread_setschedparam(), pthread_attr_setschedpolicy()

Condition Variables

• Would like to be “woken up” when a particular condition occurs– Calling pthread_cond_wait(mutex) releases

exclusive access to a mutex. Thread sleeps.– When condition is signalled, thread wakes up

and given access back to the mutex

Conditional Waiting

action()

{

lock();

while (x != 0)

wait (s);

unlock();

}

counter()

{

lock();

x--;

if (x==0)

signal(s);

unlock();

}

Both must occur before wait() returns

A Simple Example: Array Summation

int array_sum(int n, int data[]){ int mid; int low_sum, high_sum;

mid = n/2; low_sum = 0; high_sum = 0; #pragma multithreadable { for (int i = 0; i < mid; i++) low_sum = low_sum + data[i]; for (int j = mid; j < n; j++) high_sum = high_sum + data[j]; } return low_sum + high_sum;}

typedef struct { int n, *data, mid; int *high_sum, *low_sum;} args_block;

void sum_0(args_block *args){ for (int i = 0; i < args->mid; i++) *args->low_sum = *args->low_sum + args->data[i];}

void sum_1(args_block *args){ for (int j = args->mid; j < args->n; j++) *args->high_sum = *args->high_sum + args->data[j];}

int array_sum(int n, int data[]){ int mid; int low_sum, high_sum; args_block args; pthread_t threads[2]; mid = n/2; args.n = n; args.data = data; args.mid = mid; args.low_sum = &low_sum; args.high_sum = &high_sum;

pthread_create(&thread[0], NULL, (void *) sum_0, (void *) &args); pthread_create(&thread[1], NULL, (void *) sum_1, (void *) &args);

for (i = 0; i < 2; i++) /* wait for threads to complete */

pthread_join(&thread[i], &retval); return low_sum + high_sum;}

attributesRoutine to execute

Thread args

Commodity Multithreaded Applications

• Example Problems: Spreadsheets, CAD/CAM, simulation, video/photo editing and production, games, voice/handwriting recognition, real-time 3D rendering, job scheduling, etc. etc.

• Need to run as fast as sequential on one processor.

• Need to run significantly faster on multiprocessors.

• No recompilation, no relinking, no reconfiguration.

• Need to adapt dynamically to changing resources.

• Need to be reliable and timely.

Last Thoughts on Threading

• Threads provide a way to expose parallelism within a task.

• Advantages– Straightforward parallelism– Common construction (Java, Win32, Pthreads)– Shared variables eliminates copying

• Disadvantages– Mutual exclusion hard to think about– Not scalable to outside of a single SMP

• (Active research to eliminate this)

An Aside: Automatic Parallelization ?

• Write a sequential program.• Compiler transforms sequential program into efficient parallel

(multithreaded) program • A very very very very very very very difficult problem.• Decades of work on this problem.• Some success with some regular scientific programs.• Not a general solution (and probably never will be).• Not applicable to large, irregular, dynamic programs.

• Compilers must overuse locking to insure correctness • Compilers need help determining what code blocks can operate

independently OpenMP directives