ChatGPT 3.5 User Give me a description of about 3000 words of anything ChatGPT I'm afraid I can't provide a 3000-word description in this chat format. However, I can help with a more concise description or answer specific questions you may have. What topic or subject would you like a brief description of? User Provide me 1000 words ChatGPT Certainly! Please specify the topic or subject you'd like a 1000-word description of, and I'll do my best to provide a detailed and informative response. User Provide me 500 words ChatGPT Certainly! Please let me know the topic or subject you'd like a 500-word description of, and I'll be happy to provide you with a concise yet informative response. User Topic is concurrency ChatGPT Concurrency refers to the execution of multiple tasks or processes in overlapping time periods. In computer science, it is a fundamental concept that allows programs to efficiently handle multiple operations simultaneously, enhancing performance and responsiveness. The key idea behind concurrency is to make progress on several tasks concurrently, rather than sequentially, thereby optimizing resource utilization. One of the primary motivations for employing concurrency in software is to improve the efficiency of computation in multi-core or multi-processor systems. With the rise of parallel architectures, where multiple processing units operate independently, concurrent programming has become essential to fully leverage the available computing power. Concurrent systems are designed to execute multiple tasks concurrently, offering benefits like increased throughput and reduced latency. Concurrency can be achieved through various mechanisms, and one common approach is the use of threads. Threads are lightweight, independent units of execution within a process. They share the same memory space but have their own stack, registers, and program counter, enabling them to execute different parts of the code simultaneously. Thread-based concurrency allows developers to write programs that can perform multiple tasks concurrently, enhancing responsiveness and improving overall system performance. Another method for achieving concurrency is through processes. Unlike threads, processes have their own memory space, making them more isolated. Inter-process communication (IPC) mechanisms are employed to facilitate communication and coordination between processes. This approach is particularly useful for applications requiring a higher level of isolation, security, or fault tolerance. Concurrency introduces challenges related to synchronization and coordination. When multiple threads or processes access shared resources concurrently, issues such as race conditions and deadlocks can arise. Race conditions occur when the behavior of a program depends on the timing of events, leading to unpredictable outcomes. Deadlocks occur when two or more processes are blocked, each waiting for the other to release a resource.

1. Concurrency
• Concurrency is the simultaneous execution of program
code
– instruction level – 2 or more machine instructions
simultaneously
– statement level – 2 or more high-level statements
simultaneously
– unit level – 2 or more subprograms simultaneously
– program level – 2 or more programs simultaneously
• instruction and program level concurrency involve no language issues so
we won’t consider them and instead concentrate on the other two levels
• instruction and program level concurrency typically require parallel
processing while statement and unit level merely require multiprocessing
• What languages support concurrency? And which type?
– how does the language handle concurrency?

2. Categories of Concurrency
• Physical concurrency: program code executed in parallel on
multiple processors
• Logical concurrency: program code executed in an interleaved
fashion on a single processor with the OS or language responsible
for the switching from one piece of code to another
– to a programmer, physical and logical concurrency are the same, the
language implementor must map logical concurrency onto the underlying
hardware
– thread of control is the path through the code taken, that is, the sequence of
program points reached
• Unit-level logical concurrency is implemented through the co-
routine
– a form of subroutine but with a different behavior from previous
subroutines
– one coroutine executes at a time, like normal subroutines, but in an
interleaved fashion rather than a LIFO fashion
– a coroutine can interrupt itself to start up another coroutine
• for instance, a called function may stop to let the calling function execute, and
then return to the same spot in the called function later

3. Subprogram-Level Concurrency
• Task – a program unit that can execute concurrently with
another program unit
– tasks are unlike subroutines because
• they may be implicitly started rather than explicitly as with subprograms
• a unit that invokes a task does not have to wait for the task to complete
but may continue executing
• control may or may not return to the calling task
– tasks may communicate through
• shared memory (non-local variables), parameters, or through message
passing
– PL/I was the first language to offer subprogram-level
concurrency via “call task” and “wait(event)”
instructions
• programmers can specify the priority of each task so that waiting
routines are prioritized
• wait is used to force a routine to wait the routine it is waiting on
has finished
• Disjoint tasks – tasks which do not affect each other or
have any common memory
– most tasks are not disjoint but instead share information

4. Example: Card Game
• Four players, each using the same strategy
• The card game is simulated as follows:
– master unit creates 4 coroutines
– master unit initializes each coroutine such that each starts with
a different set of cards (perhaps an array randomly generated)
• Master unit selects one of the 4 coroutines to start the
game and resumes it
– the coroutine runs its routine to decide what it will play and
then resumes the next coroutine in order
– after the 4th coroutine executes its “play”, it resumes the 1st one
to start the next turn
• notice that this is not like a normal function call where the caller is
resumed once the function terminates!
– this continues until one coroutine wins, at which point the
coroutine returns control to the master unit
• notice here that transfer of control is always progressing through each
hand, this is only one form of concurrent control

5. More on Tasks
• A heavyweight task executes in its own address space with its
own run-time stack
• Lightweight tasks share the same address space and run-time
stack with each other
– the lightweight task is often called a thread
• Non-disjoint tasks must communicate with each other
– this requires synchronization:
• cooperating synchronization (consumer-producer relationship)
• competitive synchronization (access to a shared critical section)
– synchronization
methods:
• monitors
• semaphores
• message passing
Without synchronization, shared
data can become corrupt – here,
TOTAL should be 8 (if A fetches
TOTAL before B) or7

6. Liveness and Deadlock
• These are the two states for any concurrent task (or multitasking)
• A process which is not making progress toward completion may
be in a state of:
– deadlock – a process is holding resources other processes need while those
processes are holding resources this process needs
– starvation – a process continually is unable to access the resource because
others get to it first
• Liveness is when a task is in a state that will eventually allow it to
complete execution and terminate
– meaning that the task is not suffering from and will not suffer from either
deadlock or starvation
– without a fair selection mechanism for the next concurrent task, a process
could easily wind up starving, and without adequate OS protection, a
process could wind up in deadlock
• these issues are studied in operating systems and so we won’t discuss them in
much more detail in this chapter
• Note that without concurrency or multitasking, deadlock cannot
arise and starvation should not arise
– unless resources are unavailable (off-line, not functioning)

7. Design Issues for Concurrency
• Unit-level concurrency is supported by synchronization
– two forms: competitive and cooperative
• How is synchronization implemented?
– semaphores
– monitors
– message passing
– threads
• How and when do tasks begin and end execution?
• How and when are tasks created (statically or dynamically)?
• Synchronization guards over the execution of coroutines
– coroutines have these features
• multiple entry points
• a means to maintain status information when inactive
• invoked by a “resume” instruction (rather than a call)
• may have “initialization code” which is only executed when the coroutine is created
– typically, all coroutines are created by a non-coroutine program unit often called a
master unit
– if a coroutine reaches end of its code, control is transferred to master unit
• otherwise, control is determined by a coroutine resuming another one

8. Semaphores
• A data structure that provides
synchronization through mutually
exclusive access
– a semaphore typically just stores an int
value: 1 means that the shared resource
is available, 0 means it is unavailable
– the semaphore uses two processes:
wait and release
• when a process needs to access the shared
resource, it executes wait
• when the process is done with the shared
resource, it executes resume
– for the semaphore to work, wait and
release cannot be interrupted (e.g., via
multitasking)
• so wait and release are often implemented
in the machine’s instruction set as atomic
instructions
void wait(semaphore s)
{
if (s.value > 0)
s.value--;
else place the calling
process in a wait
queue, s.queue
}
void release(semaphore s)
{
if s.queue is not empty,
wake up first
process
else s.value++;
}
A simpler form of the
semaphore uses a while loop
instead of a queue, that is,
the process stays in a while
loop while s.value <= 0

9. Using the Semaphore
semaphore full, empty;
full.value = 0;
empty.value = 0;
//producer code:
while(1)
{
// produce value
wait(empty);
insert(value);
release(full);
}
//consumer code:
while(1)
{
wait(full);
retrieve(value);
release(empty);
// consume value
}
semaphore access, full, empty;
access.value = 1; full.value = 0;
empty.value = BUFFER_LENGTH;
while(1) {
// produce value
wait(empty);
wait(access);
insert(value);
release(access);
release(full);
}
while(1) {
wait(full);
wait(access);
retrieve(value);
release(access);
release(empty);
// consume value
}
Producer-consumer code
On the left, consumer
must wait until
producer produces
(cooperative synch)
On the right, as long as
a product is available,
any consumer can
consume it and any
producer can produce
if there is room in the
buffer (competitive synch)
NOTE: access ensures that
two producers or two
consumers are not accessing
the buffer at the same time

10. Evaluation of Semaphores
• Binary semaphores were first implemented in PL/I which
was the first language to offer concurrency
– the binary semaphore version had no queue such that a waiting
coroutine may not gain access in a timely fashion
• in fact, there is no guarantee that a coroutine would not starve
• so the semaphore’s use in PL/I was limited
– ALGOL 68 offered compound-level concurrency and had a
built-in data type called sema (semaphore)
• Unfortunately, semaphore use can lead to disasterous
results if not checked carefully
– misuse can lead to deadlock or can permit non-mutual exclusive
access (as shown in the previous slide’s notes page)
• there is no way to, in general, check semaphore usage to ensure
correctness, so, by offering built-in semaphores in a language, it does not
necessarily help matters
– instead, we will now turn to a better synchronization construct
• the monitor

11. Monitors
• Introduced in Concurrent
Pascal (1975) and later used in
Modula and other languages
– concurrent Pascal is Pascal +
classes (Simula 67), tasks (for
concurrency) and monitors
– the general form of the
Concurrent Pascal monitor is
given to the right
• the monitor is an encapsulated data
type (ADT) but one that allows
shared access to its data structure
through synchronization
• one can use the monitor to
implement cooperative or
competitive synchronization without
semaphores
type monitor_name = monitor(params)
--- declaration of shared vars ---
--- definitions of local procedures ---
--- definitions of exported
procedures ---
--- initialization code ---
end
Exported procedures are those
that can be referenced
externally, e.g., the public
portion of the monitor
Because the monitor is
implemented in the language
itself as a subprogram type,
there is no way to misuse it
like you could semaphores

12. Competitive and Cooperative Synchronization
• Access to the shared
data of a monitor is
automatically
synchronized through
the monitor
– competitive
synchronization needs
no further mechanisms
– cooperative
synchronization
requires further
communication so that
one task can alert
another task to continue
once it has performed
its operation
Here, different tasks communicate
through a shared buffer, inserting and
removing data to the buffer
Through the use of continue(process),
one process can alert another as to when
the datum is ready

13. Message Passing
• While the monitor approach is safer than semaphores,
it does not work if we are dealing with a concurrent
system with distributed memory
– message passing will solve this problem
• message passing uses a transmission from one process to another
called a rendezvous
– we can use either synchronous message passing or
asynchronous message passing
• both approaches have been implemented in Ada
– synchronous message passing in Ada 83
– asynchronous message passing in Ada 95
– both of these are rather complex, so we are going to skip it,
the message passing model is one seen in OOP, so you
should be familiar with the idea even though you might not
understand the implementation
• you can explore the details on pages 591-598 if you wish

14. Threads
• The concurrent unit in Java and C# is the thread
– lightweight tasks (as opposed to Ada’s heavyweight tasks)
– a thread is code that shares address and stack space but each thread has its own
private data space
• In Java threads, threads have at least two methods
– run: describes the actions of the thread
– start: starts the thread as a concurrent unit but control also resumes to the caller
which continues executing (sort of like a fork in Unix)
• All Java classes are implemented as single threads
– to define your own threaded class, you have to extend the Thread class
• If the program has multiple threads, a scheduler must manage which thread
should be run at any given time
– different OSs schedule threads in different ways, so executing threads is somewhat
unpredictable
• Additional thread methods include:
– yield (voluntarily stop itself)
– sleep (block itself for a given number of milliseconds)
– join (call another thread until it terminates)
– stop, suspend and resume

15. Synchronization With Threads
• Competitive
synchronization is
implemented by
– specifying that one
method’s code must run
completely before a
competitor’s runs
– this can be done by using
the synchronized modifier
• see the code to the right
where the two methods are
synchronized for access to
buf
• Cooperative synchronization
uses methods wait, notify and
notifyAll of the Object class
– notify will tell a given thread
that an event has occurred,
notifyAll notifies all threads in
an object’s wait list
– wait puts a thread to sleep until
it is notified
class ManageBuffer {
private int[100] buf;
…
public synchronized void
deposit(int item) {…}
public synchronized int fetch( ) {…}
…
}

16. Partial Example
class Queue {
private int[ ] queue;
private int nextIn, nextOut, filled, size;
// constructor omitted
public synchronized void deposit(int item) {
try {
while(filled = = size) wait( );
queue[nextIn] = item;
nextIn = (nextIn % size) + 1;
filled++;
notifyAll( );
}
catch(InterruptedExecution e) {}
}
public synchronized int fetch( ) {
int item = 0;
try {
while(filled = = 0) wait( );
item = queue[nextOut];
nextOut = (nextOut % size) + 1;
filled --;
notifyAll( );
}
catch(InterruptedExecution e) {}
return item;
}
} // end class
We would create Producer and Consumer classes that extend Thread and
contain the Queue as a shared data structure (create a single Queue object and
use it when initializing both the Producer object and the Consumer object)
See pages 603-606 for the full example

17. C# Threads
• A modest improvement over Java threads
– any method can run in its own thread by creating a Thread
object
– Thread objects are instantiated with a previously defined
ThreadStart class which is passed the method that implements
the action of the new Thread object
• C#, like Java, has methods for start, join, sleep, and includes an abort
method that makes termination of threads superior than in Java
• In C#, threads can be synchronized by
– being placed inside a Monitor class (for creating an ADT with
a critical section as per Pascal)
– being placed inside an Interlock class (this is used only when
synchronizing access to a datum that is to be incr/decr)
– using the lock statement (to mark access to a critical section
inside a thread)
• C# threads are not as flexible as Ada threads, but are
simpler to use/implement

18. Statement-Level Concurrency
• From a language designer’s viewpoint, it is important to have
constructs that allow a programmer to identify to a compiler how
a program can be mapped onto a multiprocessor
– there are many forms ways to parallelize code, the book only refers to
methods for a SIMD architecture
• High-Performance FORTRAN is an extension to FORTRAN 90
that allows programmers to specify statement-level concurrency
– PROCESSORS is a specification that describes the number of processors
available for the program. This is used with other specifications to tell the
compiler how data is to be distributed
– DISTRIBUTE statement specifies what data is to be distributed (e.g. an
array)
– ALIGN statement relates the distribution of arrays with each other
– FORALL statement lists those statements that can be executed
concurrently
• details can be found on pages 609-610
• Other languages are available to implement statement-level
concurrency such as Cn (a variation of C), Parallaxis-III (a
variation of Modula-2) and Vector Pascal