Prof. Hank Dietz
Purdue University School of Electrical and Computer Engineering
Still under construction... 8 July 1996
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
with the text
subscribe linux-smp to join the list.
Right now, the primary problem is that there really isn't much high-level support for shared memory parallel programs under SMP Linux. This document provides a simple overview of how to write shared memory parallel programs using standard Linux facilities. As you read this document, keep in mind that we haven't tested everything and we don't even have access to high-end SMP hardware.
That said, we have been able to start testing SMP parallel stuff on
real hardware. In June 1996, we purchased a two-processor 100MHz
Pentium system... both processors, Asus motherboard, 256K cache, 32M
RAM, 1.6G disk, 6X CDROM, Stealth 64, and 15" Acer monitor all for
$1,800! Getting SMP Linux up on this system was simply a matter of
recompiling the kernel with the
SMP=1 line in the makefile
uncommented (although I find setting SMP to 1 a bit ironic ;-).
It is worth mentioning that an SMP Linux system can use most parallel processing software that was originally developed for a workstation cluster using socket communication. Sockets should work within an SMP Linux system, and even for multiple SMPs networked as a cluster; however, sockets also imply a lot of unnecessary overhead for an SMP. Worse still, much of that overhead is within the kernel, and SMP Linux currently allows at most one processor in the kernel at a time.
The remainder of this document 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.
It is important to understand, however, 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:
Ok, so you have decided that parallel processing on an SMP is a great thing to do... how do you get started? Well, the very first thing that occurs to most shared-memory parallel processing folk is to get a copy of the documentation on the local threads library.
Threads are essentially "light-weight" UNIX processes that are not scheduled the same way as regular UNIX processes and allow pretty much the entire memory map to be shared among them. Trouble is, until recently, Linux didn't do threads. 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. The POSIX API doesn't require it, and versions like this one apparently do not implement parallel thread execution. Fortunately, LinuxThreads 0.6 (BETA) does. POSIX threads are generally 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.
The first threads library that supported SMP Linux parallelism was the
library, a very small library that uses 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.
The library uses 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). The primary
documentation for this library is a very short demo program and a
README file, both packaged with the library archive, but
a brief overview is also presented here.
In addition to the "shared everything" mechanism of
bb_threads, there are two 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 little feature
from BSD UNIX that isn't completely implemented for Linux, but still
does enough to be usable for sharing memory between processes: the
mmap() system call. You can, and should, learn about
these calls from the manual pages... but a brief of each is given
here to help get you started.
Which shared memory model should you use? That is mostly a question of
religion. A lot of people like the "shared everything" model, as
bb_threads, 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.... ;-)
Given the choice, I'd recommend use of the System V mechanism simply
because the complete mechanism is fully implemented, so unpleasant
surprises are less likely.
bb_threads library is very simple and apparently
easy to use, but it is also the easiest library to abuse. Why?
Because it implements a "shared everything" model.
A lot of people prefer this type of shared memory model because it does not force them to identify which data structures should be shared at the time they are declared. One simply places locks around code that accesses shared objects if having more than one process(or) access those objects simultaneously could cause problems (typically, races). However, with everything in shared space, omission of even one lock can lead to a terrible parallel debugging nightmare. The flip side is that if one is too conservative in using locks, it is quite common for the lock accesses themselves to destroy system performance.
The basic program structure for using the
MAX_MUTEXES, and initializes lock i by:
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_unlock(n)where n is an integer identifying which lock to use. This causes at most one process to be allowed to enter the locked code at a time. Unfortunately, this is not really sufficient for two reasons:
printf()not only have a lot of upward exposed internal state, but also share that state with a wide variety of other library functions. For example,
putchar()uses the same static buffer, so calling these two functions simultaneously must be prevented using the same lock.
printf()from being executed simultaneously from within the functions
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.
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:
In summary, the
bb_threads library is a good start, but
right now it is very small and very alpha. Even once it matures, the
issues involving locking to avoid conflicts on upward exposed internal
state will still be there.... Basically, too many locks destroy
performance or yield deadlocks and too few locks cause races and
incorrect behavior. In fact, although GCC generates re-entrant code,
it is quite possible that some compilers would use static locations as
pseudo-registers, in which case this entire model is essentially
unusable. In my opinion, although the shared everything model seems
the easiest to use, the subtle nature of its problems makes the other
two models much easier to use correctly.
The System V IPC support consists of a number of inter-process communication system calls providing message queues, semaphores, and a shared memory mechanism. Of course, this mechanism was really 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. 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
structtype, and declaring shmptr to be a pointer to that type. Using this technique, shared variable x would be accessed as shmptr
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.
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.
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 (
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 above. 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
In my opinion, there really isn't a compelling reason to use
mmap() instead of the System V shared memory support.
No matter which of the above two mechanisms 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. 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 also be necessary to surround
some shared memory accesses with instructions that cause all pending
memory accesses to complete, thus providing memory access ordering.
CPUID instruction apparently is reserved to have this
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;Yes, that is a annoying, but it is the price one pays for enabling GCC to perform some very powerful optimizations. At least in theory, the
-traditionaloption 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.
It appears that there is a rumor accompanying the
bb_threads library to the effect that using
assembly-language locks that are marked as modifying "everything" will
cause GCC to appropriately flush all variables, thus avoiding the
"inefficient" handling 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
guaranteed to work correctly.
Note that you can cause a volatile access to an ordinary variable by
using a type cast that imposes the
For example, the ordinary
int i; can be referenced as a
*((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
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, XADDHowever, it probably is not a good idea to use all these operations. For example,
XADDdid not even exist for the 386, so coding it may cause portability problems.
XCHG instruction always asserts a lock, even
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
It is important to remember, however, that there is a cost associated with making operations atomic. A locking operation carries a fair amount of overhead and may delay memory activity from other processors, whereas ordinary references may simply use local cache. The best performance results when locking operations are used as infrequently as possible.
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
((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 must be in different cache
Because SMP Linux does not yet have much parallel processing support, it is useful to see what some of the other SMP systems out there do. The following are a few such resources.
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