This document gives a brief overview of how to use
SMP Linux systems
for parallel processing. The most up-to-date information on SMP Linux
is probably available via the SMP Linux project mailing list; send
email to
[email protected] with the text subscribe
linux-smp to join the list.
Does SMP Linux really work? In June 1996, I purchased a brand new
(well, new off-brand ;-) two-processor 100MHz Pentium system. The
fully assembled system, including both processors, Asus motherboard,
256K cache, 32M RAM, 1.6G disk, 6X CDROM, Stealth 64, and 15" Acer
monitor, cost a total of $1,800. This was just a few hundred dollars
more than a comparable uniprocessor system. Getting SMP Linux running
was simply a matter of installing the "stock" uniprocessor Linux,
recompiling the kernel with the SMP=1 line in the makefile
uncommented (although I find setting SMP to 1 a bit
ironic ;-), and informing lilo about the new kernel. This
system performs well enough, and has been stable enough, to serve as
my primary workstation ever since. In summary, SMP Linux really does
work.
The next question is how much high-level support is available for writing and executing shared memory parallel programs under SMP Linux. Through early 1996, there wasn't much. Things have changed. For example, there is now a very complete POSIX threads library.
Although performance may be lower than for native shared-memory mechanisms, an SMP Linux system also can use most parallel processing software that was originally developed for a workstation cluster using socket communication. Sockets (see section 3.3) work within an SMP Linux system, and even for multiple SMPs networked as a cluster. However, sockets imply a lot of unnecessary overhead for an SMP. Much of that overhead is within the kernel or interrupt handlers; this worsens the problem because SMP Linux generally allows only one processor to be in the kernel at a time and the interrupt controller is set so that only the boot processor can process interrupts. Despite this, typical SMP communication hardware is so much better than most cluster networks that cluster software will often run better on an SMP than on the cluster for which it was designed.
The remainder of this section discusses SMP hardware, reviews the basic Linux mechanisms for sharing memory across the processes of a parallel program, makes a few observations about atomicity, volatility, locks, and cache lines, and finally gives some pointers to other shared memory parallel processing resources.
Although SMP systems have been around for many years, until very recently, each such machine tended to implement basic functions differently enough so that operating system support was not portable. The thing that has changed this situation is Intel's Multiprocessor Specification, often referred to as simply MPS. The MPS 1.4 specification is currently available as a PDF file at http://www.intel.com/design/pro/datashts/242016.htm, and there is a brief overview of MPS 1.1 at http://support.intel.com/oem_developer/ial/support/9300.HTM, but be aware that Intel does re-arrange their WWW site often. A wide range of vendors are building MPS-compliant systems supporting up to four processors, but MPS theoretically allows many more processors.
The only non-MPS, non-IA32, systems supported by SMP Linux are Sun4m multiprocessor SPARC machines. SMP Linux supports most Intel MPS version 1.1 or 1.4 compliant machines with up to sixteen 486DX, Pentium, Pentium MMX, Pentium Pro, or Pentium II processors. Unsupported IA32 processors include the Intel 386, Intel 486SX/SLC processors (the lack of floating point hardware interferes with the SMP mechanisms), and AMD & Cyrix processors (they require different SMP support chips that do not seem to be available at this writing).
It is important to understand that the performance of MPS-compliant systems can vary widely. As expected, one cause for performance differences is processor speed: faster clock speeds tend to yield faster systems, and a Pentium Pro processor is faster than a Pentium. However, MPS does not really specify how hardware implements shared memory, but only how that implementation must function from a software point of view; this means that performance is also a function of how the shared memory implementation interacts with the characteristics of SMP Linux and your particular programs.
The primary way in which systems that comply with MPS differ is in how they implement access to physically shared memory.
Some MPS Pentium systems, and all MPS Pentium Pro and Pentium II systems, have independent L2 caches. (The L2 cache is packaged within the Pentium Pro or Pentium II modules.) Separate L2 caches are generally viewed as maximizing compute performance, but things are not quite so obvious under Linux. The primary complication is that the current SMP Linux scheduler does not attempt to keep each process on the same processor, a concept known as processor affinity. This may change soon; there has recently been some discussion about this in the SMP Linux development community under the title "processor binding." Without processor affinity, having separate L2 caches may introduce significant overhead when a process is given a timeslice on a processor other than the one that was executing it last.
Many relatively inexpensive systems are organized so that two Pentium processors share a single L2 cache. The bad news is that this causes contention for the cache, seriously degrading performance when running multiple independent sequential programs. The good news is that many parallel programs might actually benefit from the shared cache because if both processors will want to access the same line from shared memory, only one had to fetch it into cache and contention for the bus is averted. The lack of processor affinity also causes less damage with a shared L2 cache. Thus, for parallel programs, it isn't really clear that sharing L2 cache is as harmful as one might expect.
Experience with our dual Pentium shared 256K cache system shows quite a wide range of performance depending on the level of kernel activity required. At worst, we see only about 1.2x speedup. However, we also have seen up to 2.1x speedup, which suggests that compute-intensive SPMD-style code really does profit from the "shared fetch" effect.
The first thing to say is that most modern systems connect the processors to one or more PCI buses that in turn are "bridged" to one or more ISA/EISA buses. These bridges add latency, and both EISA and ISA generally offer lower bandwidth than PCI (ISA being the lowest), so disk drives, video cards, and other high-performance devices generally should be connected via a PCI bus interface.
Although an MPS system can achieve good speed-up for many compute-intensive parallel programs even if there is only one PCI bus, I/O operations occur at no better than uniprocessor performance... and probably a little worse due to bus contention from the processors. Thus, if you are looking to speed-up I/O, make sure that you get an MPS system with multiple independent PCI busses and I/O controllers (e.g., multiple SCSI chains). You will need to be careful to make sure SMP Linux supports what you get. Also keep in mind that the current SMP Linux essentially allows only one processor in the kernel at any time, so you should choose your I/O controllers carefully to pick ones that minimize the kernel time required for each I/O operation. For really high performance, you might even consider doing raw device I/O directly from user processes, without a system call... this isn't necessarily as hard as it sounds, and need not compromise security (see section 3.3 for a description of the basic techniques).
It is important to note that the relationship between bus speed and processor clock rate has become very fuzzy over the past few years. Although most systems now use the same PCI clock rate, it is not uncommon to find a faster processor clock paired with a slower bus clock. The classic example of this was that the Pentium 133 generally used a faster bus than a Pentium 150, with appropriately strange-looking performance on various benchmarks. These effects are amplified in SMP systems; it is even more important to have a faster bus clock.
Memory interleaving actually has nothing whatsoever to do with MPS, but you will often see it mentioned for MPS systems because these systems are typically more demanding of memory bandwidth. Basically, two-way or four-way interleaving organizes RAM so that a block access is accomplished using multiple banks of RAM rather than just one. This provides higher memory access bandwidth, particularly for cache line loads and stores.
The waters are a bit muddied about this, however, because EDO DRAM and various other memory technologies tend to improve similar kinds of operations. An excellent overview of DRAM technologies is given in http://www.pcguide.com/ref/ram/tech.htm.
So, for example, is it better to have 2-way interleaved EDO DRAM or non-interleaved SDRAM? That is a very good question with no simple answer, because both interleaving and exotic DRAM technologies tend to be expensive. The same dollar investment in more ordinary memory configurations generally will give you a significantly larger main memory. Even the slowest DRAM is still a heck of a lot faster than using disk-based virtual memory....
Ok, so you have decided that parallel processing on an SMP is a great thing to do... how do you get started? Well, the first step is to learn a little bit about how shared memory communication really works.
It sounds like you simply have one processor store a value into memory and another processor load it; unfortunately, it isn't quite that simple. For example, the relationship between processes and processors is very blurry; however, if we have no more active processes than there are processors, the terms are roughly interchangeable. The remainder of this section briefly summarizes the key issues that could cause serious problems, if you were not aware of them: the two different models used to determine what is shared, atomicity issues, the concept of volatility, hardware lock instructions, cache line effects, and Linux scheduler issues.
There are two fundamentally different models commonly used for shared memory programming: shared everything and shared something. Both of these models allow processors to communicate by loads and stores from/into shared memory; the distinction comes in the fact that shared everything places all data structures in shared memory, while shared something requires the user to explicitly indicate which data structures are potentially shared and which are private to a single processor.
Which shared memory model should you use? That is mostly a question of religion. A lot of people like the shared everything model because they do not really need to identify which data structures should be shared at the time they are declared... you simply put locks around potentially-conflicting accesses to shared objects to ensure that only one process(or) has access at any moment. Then again, that really isn't all that simple... so many people prefer the relative safety of shared something.
The nice thing about sharing everything is that you can easily take an existing sequential program and incrementally convert it into a shared everything parallel program. You do not have to first determine which data need to be accessible by other processors.
Put simply, the primary problem with sharing everything is that any action taken by one processor could affect the other processors. This problem surfaces in two ways:
errno; if two shared
everything processes perform various calls, they would interfere with
each other because they share the same errno. Although there
is now a library version that fixes the errno problem,
similar problems still exist in most libraries. For example, unless
special precautions are taken, the X library will not work if calls
are made from multiple shared everything processes.
core file that
clues you in to what happened. In shared everything parallel
processing, it is very likely that the stray accesses will bring the
demise of a process other than the one at fault, making it
nearly impossible to localize and correct the error.Neither of these types of problems is common when shared something is used, because only the explicitly-marked data structures are shared. It also is fairly obvious that shared everything only works if all processors are executing the exact same memory image; you cannot use shared everything across multiple different code images (i.e., can use only SPMD, not general MIMD).
The most common type of shared everything programming support is a threads library. Threads are essentially "light-weight" processes that might not be scheduled in the same way as regular UNIX processes and, most importantly, share access to a single memory map. The POSIX Pthreads package has been the focus of a number of porting efforts; the big question is whether any of these ports actually run the threads of a program in parallel under SMP Linux (ideally, with a processor for each thread). The POSIX API doesn't require it, and versions like http://www.aa.net/~mtp/PCthreads.html apparently do not implement parallel thread execution - all the threads of a program are kept within a single Linux process.
The first threads library that supported SMP Linux parallelism was the
now somewhat obsolete bb_threads library,
ftp://caliban.physics.utoronto.ca/pub/linux/, a very small
library that used the Linux clone() call to fork new,
independently scheduled, Linux processes all sharing a single address
space. SMP Linux machines can run multiple of these "threads" in
parallel because each "thread" is a full Linux process; the trade-off
is that you do not get the same "light-weight" scheduling control
provided by some thread libraries under other operating systems. The
library used a bit of C-wrapped assembly code to install a new chunk
of memory as each thread's stack and to provide atomic access
functions for an array of locks (mutex objects). Documentation
consisted of a README and a short sample program.
More recently, a version of POSIX threads using clone() has
been developed. This library,
LinuxThreads, is clearly the preferred shared everything
library for use under SMP Linux. POSIX threads are well documented,
and the
LinuxThreads README and
LinuxThreads FAQ are very well done. The primary problem now
is simply that POSIX threads have a lot of details to get right and
LinuxThreads is still a work in progress. There is also the problem
that the POSIX thread standard has evolved through the standardization
process, so you need to be a bit careful not to program for obsolete
early versions of the standard.
Shared something is really "only share what needs to be shared." This approach can work for general MIMD (not just SPMD) provided that care is taken for the shared objects to be allocated at the same places in each processor's memory map. More importantly, shared something makes it easier to predict and tune performance, debug code, etc. The only problems are:
Currently, there are two very similar mechanisms that allow groups of
Linux processes to have independent memory spaces, all sharing only a
relatively small memory segment. Assuming that you didn't foolishly
exclude "System V IPC" when you configured your Linux system, Linux
supports a very portable mechanism that has generally become known as
"System V Shared Memory." The other alternative is a memory mapping
facility whose implementation varies widely across different UNIX
systems: the mmap() system call. You can, and should, learn
about these calls from the manual pages... but a brief of each is
given in sections 2.5 and 2.6 to help get you started.
No matter which of the above two models you use, the result is pretty much the same: you get a pointer to a chunk of read/write memory that is accessible by all processes within your parallel program. Does that mean I can just have my parallel program access shared memory objects as though they were in ordinary local memory? Well, not quite....
Atomicity refers to the concept that an operation on an object is accomplished as an indivisible, uninterruptible, sequence. Unfortunately, sharing memory access does not imply that all operations on data in shared memory occur atomically. Unless special precautions are taken, only simple load or store operations that occur within a single bus transaction (i.e., aligned 8, 16, or 32-bit operations, but not misaligned nor 64-bit operations) are atomic. Worse still, "smart" compilers like GCC will often perform optimizations that could eliminate the memory operations needed to ensure that other processors can see what this processor has done. Fortunately, both these problems can be remedied... leaving only the relationship between access efficiency and cache line size for us to worry about.
However, before discussing these issues, it is useful to point-out
that all of this assumes that memory references for each processor
happen in the order in which they were coded. The Pentium does this,
but also notes that future Intel processors might not. So, for future
processors, keep in mind that it may be necessary to surround some
shared memory accesses with instructions that cause all pending memory
accesses to complete, thus providing memory access ordering. The
CPUID instruction apparently is reserved to have this
side-effect.
To prevent GCC's optimizer from buffering values of shared memory
objects in registers, all objects in shared memory should be declared
as having types with the volatile attribute. If this is
done, all shared object reads and writes that require just one word
access will occur atomically. For example, suppose that p
is a pointer to an integer, where both the pointer and the integer it
will point at are in shared memory; the ANSI C declaration might be:
volatile int * volatile p;
In this code, the first volatile refers to the int
that p will eventually point at; the second volatile
refers to the pointer itself. Yes, it is annoying, but it is the
price one pays for enabling GCC to perform some very powerful
optimizations. At least in theory, the -traditional option
to GCC might suffice to produce correct code at the expense of some
optimization, because pre-ANSI K&R C essentially claimed that all
variables were volatile unless explicitly declared as
register. Still, if your typical GCC compile looks like
cc -O6 ..., you really will want to explicitly mark
things as volatile only where necessary.
There has been a rumor to the effect that using assembly-language
locks that are marked as modifying all processor registers will cause
GCC to appropriately flush all variables, thus avoiding the
"inefficient" compiled code associated with things declared as
volatile. This hack appears to work for statically allocated
global variables using version 2.7.0 of GCC... however, that behavior
is not required by the ANSI C standard. Still worse, other
processes that are making only read accesses can buffer the values in
registers forever, thus never noticing that the shared memory
value has actually changed. In summary, do what you want, but only
variables accessed through volatile are guaranteed
to work correctly.
Note that you can cause a volatile access to an ordinary variable by
using a type cast that imposes the volatile attribute. For
example, the ordinary int i; can be referenced as a volatile
by *((volatile int *) &i); thus, you can explicitly
invoke the "overhead" of volatility only where it is critical.
If you thought that ++i; would always work to add one to a
variable i in shared memory, you've got a nasty little
surprise coming: even if coded as a single instruction, the load and
store of the result are separate memory transactions, and other
processors could access i between these two transactions.
For example, having two processes both perform ++i; might
only increment i by one, rather than by two. According to
the Intel Pentium "Architecture and Programming Manual," the
LOCK prefix can be used to ensure that any of the following
instructions is atomic relative to the data memory location it
accesses:
BTS, BTR, BTC mem, reg/imm XCHG reg, mem XCHG mem, reg ADD, OR, ADC, SBB, AND, SUB, XOR mem, reg/imm NOT, NEG, INC, DEC mem CMPXCHG, XADD
However, it probably is not a good idea to use all these operations.
For example, XADD did not even exist for the 386, so coding
it may cause portability problems.
The XCHG instruction always asserts a lock, even
without the LOCK prefix, and thus is clearly the preferred
atomic operation from which to build higher-level atomic constructs
such as semaphores and shared queues. Of course, you can't get GCC to
generate this instruction just by writing C code... instead, you must
use a bit of in-line assembly code. Given a word-size volatile object
obj and a word-size register value reg, the GCC
in-line assembly code is:
__asm__ __volatile__ ("xchgl %1,%0"
:"=r" (reg), "=m" (obj)
:"r" (reg), "m" (obj));
Examples of GCC in-line assembly code using bit operations for locking are given in the source code for the bb_threads library.
It is important to remember, however, that there is a cost associated with making memory transactions atomic. A locking operation carries a fair amount of overhead and may delay memory activity from other processors, whereas ordinary references may use local cache. The best performance results when locking operations are used as infrequently as possible. Further, these IA32 atomic instructions obviously are not portable to other systems.
There are many alternative approaches that allow ordinary instructions to be used to implement various synchronizations, including mutual exclusion - ensuring that at most one processor is updating a given shared object at any moment. Most OS textbooks discuss at least one of these techniques. There is a fairly good discussion in the Fourth Edition of Operating System Concepts, by Abraham Silberschatz and Peter B. Galvin, ISBN 0-201-50480-4.
One more fundamental atomicity concern can have a dramatic impact on SMP performance: cache line size. Although the MPS standard requires references to be coherent no matter what caching is used, the fact is that when one processor writes to a particular line of memory, every cached copy of the old line must be invalidated or updated. This implies that if two or more processors are both writing data to different portions of the same line a lot of cache and bus traffic may result, effectively to pass the line from cache to cache. This problem is known as false sharing. The solution is simply to try to organize data so that what is accessed in parallel tends to come from a different cache line for each process.
You might be thinking that false sharing is not a problem using a
system with a shared L2 cache, but remember that there are still
separate L1 caches. Cache organization and number of separate levels
can both vary, but the Pentium L1 cache line size is 32 bytes and
typical external cache line sizes are around 256 bytes. Suppose that
the addresses (physical or virtual) of two items are a and
b and that the largest per-processor cache line size is
c, which we assume to be a power of two. To be very precise,
if ((int) a) & ~(c - 1) is equal to
((int) b) & ~(c - 1), then both
references are in the same cache line. A simpler rule is that if
shared objects being referenced in parallel are at least c
bytes apart, they should map to different cache lines.
Although the whole point of using shared memory for parallel processing is to avoid OS overhead, OS overhead can come from things other than communication per se. We have already said that the number of processes that should be constructed is less than or equal to the number of processors in the machine. But how do you decide exactly how many processes to make?
For best performance, the number of processes in your parallel
program should be equal to the expected number of your program's
processes that simultaneously can be running on different
processors. For example, if a four-processor SMP typically has
one process actively running for some other purpose (e.g., a WWW
server), then your parallel program should use only three processes.
You can get a rough idea of how many other processes are active on
your system by looking at the "load average" quoted by the
uptime command.
Alternatively, you could boost the priority of the processes in your
parallel program using, for example, the renice command or
nice() system call. You must be privileged to increase
priority. The idea is simply to force the other processes out of
processors so that your program can run simultaneously across all
processors. This can be accomplished somewhat more explicitly using
the prototype version of SMP Linux at
http://luz.cs.nmt.edu/~rtlinux/, which offers real-time
schedulers.
If you are not the only user treating your SMP system as a parallel machine, you may also have conflicts between the two or more parallel programs trying to execute simultaneously. This standard solution is gang scheduling - i.e., manipulating scheduling priority so that at any given moment, only the processes of a single parallel program are running. It is useful to recall, however, that using more parallelism tends to have diminishing returns and scheduler activity adds overhead. Thus, for example, it is probably better for a four-processor machine to run two programs with two processes each rather than gang scheduling between two programs with four processes each.
There is one more twist to this. Suppose that you are developing a program on a machine that is heavily used all day, but will be fully available for parallel execution at night. You need to write and test your code for correctness with the full number of processes, even though you know that your daytime test runs will be slow. Well, they will be very slow if you have processes busy waiting for shared memory values to be changed by other processes that are not currently running (on other processors). The same problem occurs if you develop and test your code on a single-processor system.
The solution is to embed calls in your code, wherever it may loop
awaiting an action from another processor, so that Linux will give
another process a chance to run. I use a C macro, call it
IDLE_ME, to do this: for a test run, compile with cc
-DIDLE_ME=usleep(1); ...; for a "production" run, compile with
cc -DIDLE_ME={} .... The usleep(1) call requests a
1 microsecond sleep, which has the effect of allowing the Linux
scheduler to select a different process to run on that processor. If
the number of processes is more than twice the number of processors
available, it is not unusual for codes to run ten times faster with
usleep(1) calls than without them.
The bb_threads ("Bare Bones" threads) library,
ftp://caliban.physics.utoronto.ca/pub/linux/, is a remarkably
simple library that demonstrates use of the Linux clone()
call. The gzip tar file is only 7K bytes! Although this
library is essentially made obsolete by the LinuxThreads library
discussed in section 2.4, bb_threads is still usable, and it is small
and simple enough to serve well as an introduction to use of Linux
thread support. Certainly, it is far less daunting to read this
source code than to browse the source code for LinuxThreads. In
summary, the bb_threads library is a good starting point, but is not
really suitable for coding large projects.
The basic program structure for using the bb_threads library is:
bb_threads_stacksize(b).
MAX_MUTEXES, and initializes lock i by
bb_threads_mutexcreate(i).
void-returning function f with the
single argument arg, you do something like
bb_threads_newthread(f, &arg), where f
should be declared something like void f(void *arg, size_t
dummy). If you need to pass more than one argument, pass a
pointer to a structure initialized to hold the argument values.
bb_threads_lock(n) and
bb_threads_unlock(n) where n is an integer
identifying which lock to use. Note that the lock and unlock
operations in this library are very basic spin locks using atomic
bus-locking instructions, which can cause excessive memory-reference
interference and do not make any attempt to ensure fairness.
The demo program packaged with bb_threads did not correctly
use locks to prevent printf() from being executed
simultaneously from within the functions fnn and
main... and because of this, the demo does not always work.
I'm not saying this to knock the demo, but rather to emphasize that
this stuff is very tricky; also, it is only slightly easier
using LinuxThreads.
return, it actually destroys
the process... but the local stack memory is not automatically
deallocated. To be precise, Linux doesn't support deallocation, but
the memory space is not automatically added back to the
malloc() free list. Thus, the parent process should reclaim
the space for each dead child by
bb_threads_cleanup(wait(NULL)). The following C program uses the algorithm discussed in section 1.3 to compute the approximate value of Pi using two bb_threads threads.
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <sys/types.h>
#include <sys/wait.h>
#include "bb_threads.h"
volatile double pi = 0.0;
volatile int intervals;
volatile int pids[2]; /* Unix PIDs of threads */
void
do_pi(void *data, size_t len)
{
register double width, localsum;
register int i;
register int iproc = (getpid() != pids[0]);
/* set width */
width = 1.0 / intervals;
/* do the local computations */
localsum = 0;
for (i=iproc; i<intervals; i+=2) {
register double x = (i + 0.5) * width;
localsum += 4.0 / (1.0 + x * x);
}
localsum *= width;
/* get permission, update pi, and unlock */
bb_threads_lock(0);
pi += localsum;
bb_threads_unlock(0);
}
int
main(int argc, char **argv)
{
/* get the number of intervals */
intervals = atoi(argv[1]);
/* set stack size and create lock... */
bb_threads_stacksize(65536);
bb_threads_mutexcreate(0);
/* make two threads... */
pids[0] = bb_threads_newthread(do_pi, NULL);
pids[1] = bb_threads_newthread(do_pi, NULL);
/* cleanup after two threads (really a barrier sync) */
bb_threads_cleanup(wait(NULL));
bb_threads_cleanup(wait(NULL));
/* print the result */
printf("Estimation of pi is %f\n", pi);
/* check-out */
exit(0);
}
LinuxThreads
http://pauillac.inria.fr/~xleroy/linuxthreads/ is a fairly
complete and solid implementation of "shared everything" as per the
POSIX 1003.1c threads standard. Unlike other POSIX threads ports,
LinuxThreads uses the same Linux kernel threads facility
(clone()) that is used by bb_threads. POSIX
compatibility means that it is relatively easy to port quite a few
threaded applications from other systems and various tutorial
materials are available. In short, this is definitely the threads
package to use under Linux for developing large-scale threaded
programs.
The basic program structure for using the LinuxThreads library is:
pthread_mutex_t
lock. Use pthread_mutex_init(&lock,val) to
initialize each one you will need to use.
pthread_t to identify each thread. To
create a thread pthread_t thread running f(), one
calls pthread_create(&thread,NULL,f,&arg).
pthread_mutex_lock(&lock) and
pthread_mutex_unlock(&lock) as appropriate.
pthread_join(thread,&retval) to clean-up after
each thread.
-D_REENTRANT when compiling your C code.An example parallel computation of Pi using LinuxThreads follows. The algorithm of section 1.3 is used and, as for the bb_threads example, two threads execute in parallel.
#include <stdio.h>
#include <stdlib.h>
#include "pthread.h"
volatile double pi = 0.0; /* Approximation to pi (shared) */
pthread_mutex_t pi_lock; /* Lock for above */
volatile double intervals; /* How many intervals? */
void *
process(void *arg)
{
register double width, localsum;
register int i;
register int iproc = (*((char *) arg) - '0');
/* Set width */
width = 1.0 / intervals;
/* Do the local computations */
localsum = 0;
for (i=iproc; i<intervals; i+=2) {
register double x = (i + 0.5) * width;
localsum += 4.0 / (1.0 + x * x);
}
localsum *= width;
/* Lock pi for update, update it, and unlock */
pthread_mutex_lock(&pi_lock);
pi += localsum;
pthread_mutex_unlock(&pi_lock);
return(NULL);
}
int
main(int argc, char **argv)
{
pthread_t thread0, thread1;
void * retval;
/* Get the number of intervals */
intervals = atoi(argv[1]);
/* Initialize the lock on pi */
pthread_mutex_init(&pi_lock, NULL);
/* Make the two threads */
if (pthread_create(&thread0, NULL, process, "0") ||
pthread_create(&thread1, NULL, process, "1")) {
fprintf(stderr, "%s: cannot make thread\n", argv[0]);
exit(1);
}
/* Join (collapse) the two threads */
if (pthread_join(thread0, &retval) ||
pthread_join(thread1, &retval)) {
fprintf(stderr, "%s: thread join failed\n", argv[0]);
exit(1);
}
/* Print the result */
printf("Estimation of pi is %f\n", pi);
/* Check-out */
exit(0);
}
The System V IPC (Inter-Process Communication) support consists of a number of system calls providing message queues, semaphores, and a shared memory mechanism. Of course, these mechanisms were originally intended to be used for multiple processes to communicate within a uniprocessor system. However, that implies that it also should work to communicate between processes under SMP Linux, no matter which processors they run on.
Before going into how these calls are used, it is important to understand that although System V IPC calls exist for things like semaphores and message transmission, you probably should not use them. Why not? These functions are generally slow and serialized under SMP Linux. Enough said.
The basic procedure for creating a group of processes sharing access to a shared memory segment is:
shmget() to create a new segment of the desired size.
Alternatively, this call can be used to get the ID of a pre-existing
shared memory segment. In either case, the return value is either the
shared memory segment ID or -1 for error. For example, to create a
shared memory segment of b bytes, the call might be shmid
= shmget(IPC_PRIVATE, b, (IPC_CREAT | 0666)).
shmat() call allows the programmer to specify
the virtual address at which the segment should appear, the address
selected must be aligned on a page boundary (i.e., be a multiple of
the page size returned by getpagesize(), which is usually
4096 bytes), and will override the mapping of any memory formerly at
that address. Thus, we instead prefer to let the system pick the
address. In either case, the return value is a pointer to the base
virtual address of the segment just mapped. The code is shmptr =
shmat(shmid, 0, 0).
Notice that you can allocate all your static shared variables into
this shared memory segment by simply declaring all shared variables as
members of a struct type, and declaring shmptr to be
a pointer to that type. Using this technique, shared variable
x would be accessed as
shmptr->x.
shmctl() to set-up this default action. The code is
something like shmctl(shmid, IPC_RMID, 0).
fork() call to make the desired
number of processes... each will inherit the shared memory segment.
shmdt(shmptr).Although the above set-up does require a few system calls, once the shared memory segment has been established, any change made by one processor to a value in that memory will automatically be visible to all processes. Most importantly, each communication operation will occur without the overhead of a system call.
An example C program using System V shared memory segments follows. It computes Pi, using the same algorithm given in section 1.3.
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <fcntl.h>
#include <sys/ipc.h>
#include <sys/shm.h>
volatile struct shared { double pi; int lock; } *shared;
inline extern int xchg(register int reg,
volatile int * volatile obj)
{
/* Atomic exchange instruction */
__asm__ __volatile__ ("xchgl %1,%0"
:"=r" (reg), "=m" (*obj)
:"r" (reg), "m" (*obj));
return(reg);
}
main(int argc, char **argv)
{
register double width, localsum;
register int intervals, i;
register int shmid;
register int iproc = 0;;
/* Allocate System V shared memory */
shmid = shmget(IPC_PRIVATE,
sizeof(struct shared),
(IPC_CREAT | 0600));
shared = ((volatile struct shared *) shmat(shmid, 0, 0));
shmctl(shmid, IPC_RMID, 0);
/* Initialize... */
shared->pi = 0.0;
shared->lock = 0;
/* Fork a child */
if (!fork()) ++iproc;
/* get the number of intervals */
intervals = atoi(argv[1]);
width = 1.0 / intervals;
/* do the local computations */
localsum = 0;
for (i=iproc; i<intervals; i+=2) {
register double x = (i + 0.5) * width;
localsum += 4.0 / (1.0 + x * x);
}
localsum *= width;
/* Atomic spin lock, add, unlock... */
while (xchg((iproc + 1), &(shared->lock))) ;
shared->pi += localsum;
shared->lock = 0;
/* Terminate child (barrier sync) */
if (iproc == 0) {
wait(NULL);
printf("Estimation of pi is %f\n", shared->pi);
}
/* Check out */
return(0);
}
In this example, I have used the IA32 atomic exchange instruction to implement locking. For better performance and portability, substitute a synchronization technique that avoids atomic bus-locking instructions (discussed in section 2.2).
When debugging your code, it is useful to remember that the
ipcs command will report the status of the System V IPC
facilities currently in use.
Using system calls for file I/O can be very expensive; in fact, that is
why there is a user-buffered file I/O library (getchar(),
fwrite(), etc.). But user buffers don't work if multiple
processes are accessing the same writeable file, and the user buffer
management overhead is significant. The BSD UNIX fix for this was the
addition of a system call that allows a portion of a file to be mapped
into user memory, essentially using virtual memory paging mechanisms to
cause updates. This same mechanism also has been used in systems from
Sequent for many years as the basis for their shared memory parallel
processing support. Despite some very negative comments in the (quite
old) man page, Linux seems to correctly perform at least some of the
basic functions, and it supports the degenerate use of this system
call to map an anonymous segment of memory that can be shared across
multiple processes.
In essence, the Linux implementation of mmap() is a plug-in
replacement for steps 2, 3, and 4 in the System V shared memory scheme
outlined in section 2.5. To create an anonymous shared memory segment:
shmptr =
mmap(0, /* system assigns address */
b, /* size of shared memory segment */
(PROT_READ | PROT_WRITE), /* access rights, can be rwx */
(MAP_ANON | MAP_SHARED), /* anonymous, shared */
0, /* file descriptor (not used) */
0); /* file offset (not used) */
The equivalent to the System V shared memory shmdt()
call is munmap():
munmap(shmptr, b);
In my opinion, there is no real benefit in using mmap()
instead of the System V shared memory support.