Concurrent Programming

Concurrent Programming Introducing some principles of reentrancy, mutual exclusion and thread-synchronization

Problems with ‘stash.c’ • We wrote a ‘public clipboard’ device-driver in order to illustrate ‘sleeping’ and ‘waking’ • But our initial version of that driver module exhibits some problems – for example, if we allow more one process to read from our ‘/dev/stash’ device-file concurrently: Reader in window #1: $ cat /dev/stash Reader in window #2: $ cat /dev/stash Writer in window #3: $ ls /usr/bin > /dev/stash

What is a ‘race condition’ ? • Without any ‘synchronization’ mechanism, multiprogramming is vulnerable to ‘races’ in which programs produce unpredictable and erroneous results, due to the relative timing of instruction-execution in separate threads or processes • An example program demonstrates this phenomenon (see our ‘concur1.cpp’)

One cure is communication • What’s needed is some way for the tasks to be made aware of each other’s actions, or for a separate ‘supervisor’ program (the operating system -- or one of its installed kernel modules) with power to intervene, and thus to impose some synchronization • Various mechanisms exist in Linux which make it possible for tasks to communicate or for the kernel (and drivers) to mediate

Kernel semaphores • The ‘race conditions’ that are exhibited by our ‘stash.c’ device-driver when we use it with two or more ‘reader’ processes -- or with two or more ‘writer’ processes -- can be eliminated by enabling our driver to enforce a ‘one-writer/one-reader’ policy • This is easy to do by using ‘semaphores’

Mutual-exclusion syntax • Declare a semaphore: struct semaphore sem; • To initialize this semaphore: init_MUTEX ( &sem ); • To acquire this semaphore: down_interruptible( &sem ); • To release this semaphore: up( &sem);

struct file_operations struct file_operations my_fops = { owner: THIS_MODULE, read: my_read, write: my_write, open: my_open, release: my_release, };

‘open()’ uses file->f_fmode • You can implement an ‘open()’ method in your device-driver that lets only one task at a time open your device for reading: { if ( file->f_fmode & FMODE_READ ) down_interruptible( &sem ); return 0; // success }

‘release()’ uses file->f_fmode • You can implement a ‘release()’ method in your device-driver that lets a ‘reader’ task release its earlier acquired semaphore: { if ( file->f_fmode & FMODE_READ ) up( &sem ); return 0; // success }

‘newstash.c’ • We write a new version of ‘stash.c’ that illustrates the use of two semaphores to restrict device-file access to one ‘writer’ and one ‘reader’ at a time • Other tasks that want to ‘read’ or ‘write’ are put to sleep if they try to ‘open’ the device-file, but are woken up when the appropriate semaphore gets ‘released’

‘struct semaphore’ For a mutual-exclusion semaphore (i.e., a ‘mutex’), the ‘count’ field will be initialized to 1, meaning that at most one task is allowed to acquire the semaphore at any given moment A task ‘aquires the semaphore’ when it calls the ‘down( &sem )’ function That function decrements the ‘count’ value, then immediately returns if the new value of ‘count’ is non-negative; otherwise, the task is ‘put to sleep’ on the semaphore’s ‘task_list’ wait-queue (and ‘sleepers’ is incremented); later, when another task that had acquired the semaphore calls the ‘up( &sem )’ function, the value of ‘count’ will get incremented and any tasks sleeping on this wait_queue will be awakened sem count sleepers lock task_list prev next

Multiprogramming • Linux also provides programmers support for writing applications that are comprised of more than just a single process • This is called ‘multiprogramming’ • Again, synchronization is needed in cases where a ‘race-condition’ might arise, as in concurrent access to a shared resources

Advantages of multithreading • For multiprocessor systems (two or more CPUs), there are potential efficiencies in the parallel execution of separate threads (a computing job may be finished sooner) • For uniprocessor systems (just one CPU), there are likely software design benefits in dividing a complex job into simpler pieces (easier to debug and maintain -- or reuse)

Some Obstacles • Separate tasks need to coordinate actions, share data, and avoid competing for same system resources • Management ‘overhead’ could seriously degrade the system’s overall efficiency • Examples: • Frequent task-switching is costly in CPU time • Busy-Waiting is wasteful of system resources

Some ‘work-arounds’ • In place of using ‘pipes’ for the exchange of data among separate processes, Linux lets ‘threads’ use the same address-space (reduces ‘overhead’ in context-switching) • Instead of requiring one thread to waste time busy-waiting while another finishes some particular action, Linux lets a thread voluntarily give up its control of the CPU

Additional pitfalls • Every thread needs some private memory that cannot be ‘trashed’ by another thread (for example, it needs a private stack for handling interrupts, passing arguments to functions, creating local variables, saving CPU register-values temporarily) • Each thread needs a way to prevent being interrupted in a ‘critical’ multi-stage action

Example of a ‘critical section’ • Updating a shared variable: • Algorithm: • (1) copy variable’s current value to a register • (2) perform arithmetical operation on register • (3) copy register’s new value back to variable • If a task-switch occurred between any of these steps, another task could interfere with the correct updating of this variable (as our ‘concur1.cpp’ demo illustrates)

‘mutual exclusion’ • To prevent one thread from ‘sabotaging’ the actions of another, some mechanism is needed that allows a thread to temporarily ‘block’ other threads from gaining control of the CPU -- until the first thread has completed its ‘critical’ action • Some ways to accomplish this: • Disable interrupts (stops CPU time-sharing) • Use a ‘mutex’ (a mutual exclusion variable) • Put other tasks to sleep (remove from run-queue)

What about ‘cli’? • Disabling interrupts will stop ‘time-sharing’ among tasks on a uniprocessor system • But it would be ‘unfair’ in to allow this in a multi-user system (monopolize the CPU) • So ‘cli’ is a privileged instruction: it cannot normally be executed by user-mode tasks • It won’t work for a multiprocessor system

What about a ‘mutex’? • A shared global variable acts as a ‘lock’ • Initially it’s ‘unlocked’: e.g., int mutex = 1; • Before entering a ‘critical section’ of code, a task ‘locks’ the mutex: i.e., mutex = 0; • As soon as it leaves its ‘critical section’, it ‘unlocks’ the mutex: i.e., mutex = 1; • While the mutex is ‘locked’, no other task can enter the ‘critical section’ of code

Advantages and cautions • A mutex can be used in both uniprocessor and multiprocessor systems – provided it is possible for a CPU to ‘lock’ the mutex with a single ‘atomic’ instruction (requires special support by processors’ hardware) • Use of a mutex can introduce busy-waiting by tasks trying to enter the ‘critical section’ (thereby severely degrading performance)

Software mechanism • The operating system can assist threads needing mutual exclusion, simply by not scheduling other threads that might want to enter the same ‘critical section’ of code • Linux accomplishes this by implementing ‘wait-queues’ for those threads that are all contending for access to the same system resource – including ‘critical sections’

Demo programs • To show why ‘synchronization’ is needed in multithreaded programs, we wrote the ‘concur1.cpp’ demo-program • Here several separate threads will all try to increment a shared ‘counter’ – but without any mechanism for doing synchronization • The result is unpredictable – a different total is gotten each time the program runs!

How to employ a ‘mutex’ • Declare a global variable: int mutex = 1; • Define a pair of shared subroutines • void enter_critical_section( void ); • void leave_critical_section( void ); • Insert calls to these subroutines before and after accessing the global ‘counter’

Special x86 instructions • We need to use x86 assembly-language (to implement ‘atomic’ mutex-operations) • Several instruction-choices are possible, but ‘btr’ and ‘bts’ are simplest to use: • ‘btr’ means ‘bit-test-and-reset’ • ‘bts’ means ‘bit-test-and’set’ • Syntax and semantics: • asm(“ btr $0, mutex “); // acquire the mutex • asm(“ bts $0, mutex “); // release the mutex

Our two mutex-functions void enter_critical_section( void ) { asm(“spin: btr $0, mutex “); asm(“ jnc spin “); } void leave_critical_section( void ) { asm(“ bts $0, mutex “); }

Where to use the functions void my_thread( int * data ) { int i, temp; for (i = 0; i < maximum; i++) { enter_critical_section(); temp = counter; temp += 1; counter = temp; leave_critical_section(); } }

‘reentrancy’ • By the way, we point out as an aside that our ‘my_thread()’ function (on the previous slide) is an example of ‘reentrant’ code • More than one process (or processor) can be safely executing it concurrently • It needs to obey two cardinal rules: • It contains no ‘self-modifying’ instructions • Access to shared variables is ‘exclusive’

‘concur2.cpp’ • We rewrote ‘concur1.cpp’ demo-program, as ‘concur2.cpp’, inserting these functions that will implement ‘mutual exclusion’ for our thread’s ‘critical section’ • But note how much time it now consumes $ time ./concur2

The x86 ‘lock’ prefix • In order for the ‘btr’ instruction to perform an ‘atomic’ update (when multiple CPUs are using the same bus to access memory simultaneously), it is necessary to insert an x86 ‘lock’ prefix, like this: asm(“ spin: lock btr $0, mutex “); • This instruction ‘locks’ the shared system-bus during this instruction execution -- so another CPU cannot intervene

In-class exercise #1 • Add the ‘lock’ prefix to your ‘concur2.cpp’ demo, and then try executing it again on the multiprocessor system • Use the Linux ‘time’ command to measure how long it takes for your demo to finish • Observe the ‘degraded’ performance due to adding the ‘mutex’ functions – penalty for achieving a ‘correct’ parallel program

The ‘nanosleep()’ system-call • Your multithreaded demo-program shows poor performance because your threads are doing lots of ‘busy-waiting’ • When a thread can’t acquire the mutex, it should voluntarily give up control of the CPU (so another thread can do real work) • The Linux ‘nanosleep()’ system-call allows a thread to ‘yield’ its time-slice

In-class exercise #2 • Revise your ‘concur3.cpp’ program so that a thread will ‘yield’ if it cannot immediately acquire the mutex (see our ‘yielding.cpp’ demo for header-files and call-syntax) • Use the Linux ‘time’ command to compare the performance of ‘concur3’ and ‘concur2’

Concurrent Programming