1. C H A P T E R E L E V E N Concurrent Programming Programming Languages – Principles and Paradigms by Allen Tucker, Robert Noonan

2. Background: Parallelism can provide a distinct way of conceptualizing problems. Modern processors can contain a number of functional units that can not be fully utilized without considering explicit parallelism. Operating systems have long embraced the concept of concurrent programming (more about this later). Applications are finding more and more use for concurrent/parallel operation (examples?).

3. Concepts: A process is a program in execution. A process has state, including resources like memory and open files, as well as the contents of its program counter. This is the process’s execution context. A concurrent program has more than one execution context. This type of program is also referred to as a multi-threaded program. A parallel program is a concurrent program with more than one thread executing simultaneously.

4. Concepts: A distributed program is a system of programs designed to execute simultaneously on a network of computers. Because the individual processes may be executed on different computers the do not share memory (as threads do). Distributed programs must communicate using some form of messaging. Multi-threaded programs most often communicate using shared (e.g. non local) variables.

5. Concurrency in Operating Systems: The earliest operating systems executed programs in batch mode, sequentially executing programs from start to finish. Later OSs were multiprogrammed, loading and executing several programs in an interleaved fashion. A scheduler forced execution to switch among the active processes (context switching). Modern OSs us interactive time-sharing, quickly switching among processes to give the appearance of simultaneous execution of many programs (even on a single cpu).

6. Concurrency in Programs: Concurrent execution of a program can use multiple processors within a single cpu or interleave a single processor using time slicing. In Java and Ada separate threads are applied to functions or methods. Starting a thread is different than the normal procedure for calling a function:  The invoking thread doesn’t wait for the new thread to complete.  When the new thread does complete control doesn’t return to the calling invoking thread.

7. States of a Thread Figure 11.1 Created but not yet ready to run Ready to run but needs a processor Actually executing on a processor Waiting to gain access to some resource Finished

8. Communication: All concurrent programs involve inter-thread communication or interaction:  Threads compete for exclusive access to shared resources (such as?).  Threads communicate to share data. A thread can communicate using:  Non-local shared variables (used by Java).  Message passing (used by Ada).  Access Parameters (used by Ada with message passing [like pointers]). Note that Ada uses a mechanism called a rendezvous to provide points of synchronization among threads.

9. Sharing Access: The fundamental problem in shared access is called a race condition. Race conditions can occur when operations on shared variables are not atomic. When two or more threads something like “load value, change value, store value” results become unpredictable. Some computers provide atomic test and set instructions to help avoid such problems (alpha). Programmatically, code that accesses a shared variable is termed a critical section.

10. Critical Sections: For a thread to safely execute a critical section some form of locking mechanism must be implemented. The locking mechanism ensures that only one thread at a time is executing within the critical section making it effectively atomic. When implementing locks you must beware of possible deadlock conditions (how can this happen?). It is also possible for locks to be unfair, giving preference to some threads and starving others.

11. Semaphores: Semaphores were invented by Dijkstra in 1968. Semaphores consist of a shared integer variable and an associated queue to hold blocked threads. There are two atomic operations defined on semaphores: P(s) – if s > 0 then set s = s – 1, else the thread blocks V(s) – if there is a thread blocked on s wake it, else set s = s + 1. If a semaphore only takes on the values 0 and 1 it is called a binary semaphore. If the semaphore can take on any non-negative integer value it is called a counting semaphore.

12. A word about Edsgar Dijkstra: Professor Edsger Wybe Dijkstra, a noted pioneer of the science and industry of computing, died after a long struggle with cancer on 6 August 2002 at his home in Neunen, the Netherlands. Dijkstra was the 1972 recipient of the ACM Turing Award, often viewed as the Nobel Prize for computing. He was particularly acerbic about the many sins he considered encouraged by the Basic programming language, which he said irreparably harmed young programmers, and wrote a famous paper: Go To Statement Considered Harmful. The operations get their name from the Dutch proberen (to test) and verhogen (to increment).

13. Simple Producer-Consumer Cooperation Using Semaphores Figure 11.2 With only one producer and one consumer we can use a pair of binary semaphores to create a critical section around the variable buffer.

14. Multiple Producers-Consumers Figure 11.3 With multiple producers and consumers we use counting semaphores to keep track of a circular buffer pool. As before we use binary semaphores to create a critical section protecting the buffer.

15. Next time… More Concurrent Programming