1 / 26

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.

tavon
Download Presentation

CSE 160 - Lecture 15

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. CSE 160 - Lecture 15 Introduction to Threads, Synchronization and Mutual Exclusion

  2. Heavyweight Processes • Complete stand-alone programs • Code segment • Data Segment • Static data • Heap • Malloc’ed data • Stack • Registers

  3. How can two heavyweight processed communicate Process 1 Process 2 myshmPtr myshmPtr Shared Memory Segment or Communication Socket

  4. 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

  5. Concurrent Access Problem Shared Memory Segment Process 2 Process 1 ptrY = myshmPtr + sizeof (int); *ptrY = 1; if (*ptrY > 0) *ptrY --; ptrY = myshmPtr + sizeof (int); *ptrY = 1; if (ptrY > 0) *ptrY --; myshmPtr myshmPtr int x; int y; int z; What value is y after these programs execute?

  6. 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

  7. Implementing Mutual Exclusion • Spin Locks While (lock == 1) /* wait */ ; lock = 1; <critical section> lock = 0; • Busy waiting is inefficient • Naïve implementation has pitfalls (how?)

  8. Atomic Operations • Implementing locks, semaphores, monitors requires atomic building blocks load 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)

  9. 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

  10. 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

  11. 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).

  12. 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

  13. 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.

  14. heap stack 1 SP1 data stack 2 SP2 stack 3 SP3 PC1 function f PC2 code function g PC3 Three Threads Within a Process

  15. pool of processors pool of threads Thread Execution Model • 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

  16. 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.

  17. 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.

  18. 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.

  19. 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()

  20. 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

  21. 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

  22. 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;}

  23. 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;} attributes Routine to execute Thread args

  24. 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.

  25. 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)

  26. 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

More Related