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<TITLE>Garbage collector scalability</TITLE>
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<H1>Garbage collector scalability</h1>
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In its default configuration, the Boehm-Demers-Weiser garbage collector
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is not thread-safe. It can be made thread-safe for a number of environments
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by building the collector with the appropriate
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<TT>-D</tt><I>XXX</i><TT>-THREADS</tt> compilation
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flag. This has primarily two effects:
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<LI> It causes the garbage collector to stop all other threads when
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it needs to see a consistent memory state.
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<LI> It causes the collector to acquire a lock around essentially all
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allocation and garbage collection activity.
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Since a single lock is used for all allocation-related activity, only one
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thread can be allocating or collecting at one point. This inherently
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limits performance of multi-threaded applications on multiprocessors.
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On most platforms, the allocator/collector lock is implemented as a
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spin lock with exponential back-off. Longer wait times are implemented
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by yielding and/or sleeping. If a collection is in progress, the pure
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spinning stage is skipped. This has the advantage that uncontested and
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thus most uniprocessor lock acquisitions are very cheap. It has the
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disadvantage that the application may sleep for small periods of time
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even when there is work to be done. And threads may be unnecessarily
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woken up for short periods. Nonetheless, this scheme empirically
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outperforms native queue-based mutual exclusion implementations in most
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cases, sometimes drastically so.
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<H2>Options for enhanced scalability</h2>
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Version 6.0 of the collector adds two facilities to enhance collector
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scalability on multiprocessors. As of 6.0alpha1, these are supported
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only under Linux on X86 and IA64 processors, though ports to other
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otherwise supported Pthreads platforms should be straightforward.
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They are intended to be used together.
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Building the collector with <TT>-DPARALLEL_MARK</tt> allows the collector to
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run the mark phase in parallel in multiple threads, and thus on multiple
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processors. The mark phase typically consumes the large majority of the
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collection time. Thus this largely parallelizes the garbage collector
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itself, though not the allocation process. Currently the marking is
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performed by the thread that triggered the collection, together with
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threads, where <I>N</i> is the number of processors detected by the collector.
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The dedicated threads are created once at initialization time.
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A second effect of this flag is to switch to a more concurrent
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implementation of <TT>GC_malloc_many</tt>, so that free lists can be
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built, and memory can be cleared, by more than one thread concurrently.
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Building the collector with -DTHREAD_LOCAL_ALLOC adds support for thread
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local allocation. It does not, by itself, cause thread local allocation
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to be used. It simply allows the use of the interface in
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<TT>gc_local_alloc.h</tt>.
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Memory returned from thread-local allocators is completely interchangeable
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with that returned by the standard allocators. It may be used by other
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threads. The only difference is that, if the thread allocates enough
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memory of a certain kind, it will build a thread-local free list for
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objects of that kind, and allocate from that. This greatly reduces
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locking. The thread-local free lists are refilled using
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<TT>GC_malloc_many</tt>.
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An important side effect of this flag is to replace the default
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spin-then-sleep lock to be replace by a spin-then-queue based implementation.
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This <I>reduces performance</i> for the standard allocation functions,
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though it usually improves performance when thread-local allocation is
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used heavily, and thus the number of short-duration lock acquisitions
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The easiest way to switch an application to thread-local allocation is to
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<LI> Define the macro <TT>GC_REDIRECT_TO_LOCAL</tt>,
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and then include the <TT>gc.h</tt>
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header in each client source file.
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<LI> Invoke <TT>GC_thr_init()</tt> before any allocation.
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<LI> Allocate using <TT>GC_MALLOC</tt>, <TT>GC_MALLOC_ATOMIC</tt>,
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and/or <TT>GC_GCJ_MALLOC</tt>.
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<H2>The Parallel Marking Algorithm</h2>
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We use an algorithm similar to
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<A HREF="http://www.yl.is.s.u-tokyo.ac.jp/gc/">that developed by
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Endo, Taura, and Yonezawa</a> at the University of Tokyo.
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However, the data structures and implementation are different,
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and represent a smaller change to the original collector source,
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probably at the expense of extreme scalability. Some of
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the refinements they suggest, <I>e.g.</i> splitting large
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objects, were also incorporated into out approach.
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The global mark stack is transformed into a global work queue.
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Unlike the usual case, it never shrinks during a mark phase.
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The mark threads remove objects from the queue by copying them to a
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local mark stack and changing the global descriptor to zero, indicating
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that there is no more work to be done for this entry.
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is done with no synchronization. Thus it is possible for more than
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one worker to remove the same entry, resulting in some work duplication.
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The global work queue grows only if a marker thread decides to
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return some of its local mark stack to the global one. This
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is done if the global queue appears to be running low, or if
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the local stack is in danger of overflowing. It does require
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synchronization, but should be relatively rare.
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The sequential marking code is reused to process local mark stacks.
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Hence the amount of additional code required for parallel marking
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It should be possible to use generational collection in the presence of the
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parallel collector, by calling <TT>GC_enable_incremental()</tt>.
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This does not result in fully incremental collection, since parallel mark
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phases cannot currently be interrupted, and doing so may be too
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Gcj-style mark descriptors do not currently mix with the combination
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of local allocation and incremental collection. They should work correctly
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with one or the other, but not both.
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The number of marker threads is set on startup to the number of
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available processors (or to the value of the <TT>GC_NPROCS</tt>
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environment variable). If only a single processor is detected,
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parallel marking is disabled.
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Note that setting GC_NPROCS to 1 also causes some lock acquisitions inside
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the collector to immediately yield the processor instead of busy waiting
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first. In the case of a multiprocessor and a client with multiple
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simultaneously runnable threads, this may have disastrous performance
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consequences (e.g. a factor of 10 slowdown).
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We conducted some simple experiments with a version of
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<A HREF="gc_bench.html">our GC benchmark</a> that was slightly modified to
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run multiple concurrent client threads in the same address space.
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Each client thread does the same work as the original benchmark, but they share
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This benchmark involves very little work outside of memory allocation.
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This was run with GC 6.0alpha3 on a dual processor Pentium III/500 machine
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Running with a thread-unsafe collector, the benchmark ran in 9
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seconds. With the simple thread-safe collector,
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built with <TT>-DLINUX_THREADS</tt>, the execution time
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increased to 10.3 seconds, or 23.5 elapsed seconds with two clients.
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(The times for the <TT>malloc</tt>/i<TT>free</tt> version
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with glibc <TT>malloc</tt>
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are 10.51 (standard library, pthreads not linked),
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20.90 (one thread, pthreads linked),
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and 24.55 seconds respectively. The benchmark favors a
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garbage collector, since most objects are small.)
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The following table gives execution times for the collector built
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with parallel marking and thread-local allocation support
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(<TT>-DGC_LINUX_THREADS -DPARALLEL_MARK -DTHREAD_LOCAL_ALLOC</tt>). We tested
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the client using either one or two marker threads, and running
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one or two client threads. Note that the client uses thread local
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allocation exclusively. With -DTHREAD_LOCAL_ALLOC the collector
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switches to a locking strategy that is better tuned to less frequent
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lock acquisition. The standard allocation primitives thus peform
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slightly worse than without -DTHREAD_LOCAL_ALLOC, and should be
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avoided in time-critical code.
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(The results using <TT>pthread_mutex_lock</tt>
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directly for allocation locking would have been worse still, at
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least for older versions of linuxthreads.
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With THREAD_LOCAL_ALLOC, we first repeatedly try to acquire the
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lock with pthread_mutex_try_lock(), busy_waiting between attempts.
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After a fixed number of attempts, we use pthread_mutex_lock().)
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These measurements do not use incremental collection, nor was prefetching
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enabled in the marker. We used the C version of the benchmark.
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All measurements are in elapsed seconds on an unloaded machine.
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<TABLE BORDER ALIGN="CENTER">
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<TR><TH>Number of threads</th><TH>1 marker thread (secs.)</th>
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<TH>2 marker threads (secs.)</th></tr>
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<TR><TD>1 client</td><TD ALIGN="CENTER">10.45</td><TD ALIGN="CENTER">7.85</td>
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<TR><TD>2 clients</td><TD ALIGN="CENTER">19.95</td><TD ALIGN="CENTER">12.3</td>
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The execution time for the single threaded case is slightly worse than with
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simple locking. However, even the single-threaded benchmark runs faster than
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even the thread-unsafe version if a second processor is available.
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The execution time for two clients with thread local allocation time is
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only 1.4 times the sequential execution time for a single thread in a
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thread-unsafe environment, even though it involves twice the client work.
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That represents close to a
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factor of 2 improvement over the 2 client case with the old collector.
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The old collector clearly
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still suffered from some contention overhead, in spite of the fact that the
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locking scheme had been fairly well tuned.
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Full linear speedup (i.e. the same execution time for 1 client on one
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processor as 2 clients on 2 processors)
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is probably not achievable on this kind of
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hardware even with such a small number of processors,
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since the memory system is
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a major constraint for the garbage collector,
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the processors usually share a single memory bus, and thus
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the aggregate memory bandwidth does not increase in
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proportion to the number of processors.
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These results are likely to be very sensitive to both hardware and OS
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issues. Preliminary experiments with an older Pentium Pro machine running
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an older kernel were far less encouraging.
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