Transcript 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.

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 devicefile, 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
sem
count
sleepers
lock
task_list
prev
next
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
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 systembus 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’