# Garbage collector scalability

If Makefile.direct is used, in its default configuration the
Boehm-Demers-Weiser garbage collector is not thread-safe. Generally, it can be
made thread-safe by building the collector with `-DGC_THREADS` compilation
flag. This has primarily the following effects:

  1. It causes the garbage collector to stop all other threads when it needs
  to see a consistent memory state. It intercepts thread creation and
  termination events to maintain a list of client threads to be stopped when
  needed.
  2. It causes the collector to acquire a lock around essentially all
  allocation and garbage collection activity.  Since a single lock is used for
  all allocation-related activity, only one thread can be allocating
  or collecting at one point. This inherently limits performance
  of multi-threaded applications on multiprocessors.

On most platforms, the allocator/collector lock is implemented as a spin lock
with exponential back-off. Longer wait times are implemented by yielding
and/or sleeping. If a collection is in progress, the pure spinning stage
is skipped. This has the uncontested advantage that most uniprocessor lock
acquisitions are very cheap. It has the disadvantage that the application may
sleep for small periods of time even when there is work to be done. And
threads may be unnecessarily woken up for short periods. Nonetheless, this
scheme empirically outperforms native queue-based mutual exclusion
implementations in most cases, sometimes drastically so.

## Options for enhanced scalability

The collector uses two facilities to enhance collector scalability on
multiprocessors. They are intended to be used together. (The following refers
to Makefile.direct again.)

  * Building the collector with `-DPARALLEL_MARK` allows the collector to run
  the mark phase in parallel in multiple threads, and thus on multiple
  processors (or processor cores). The mark phase typically consumes the large
  majority of the collection time. Thus, this largely parallelizes the garbage
  collector itself, though not the allocation process. Currently the marking
  is performed by the thread that triggered the collection, together with
  _N_ - 1 dedicated threads, where _N_ is the number of processors (cores)
  detected by the collector. The dedicated marker threads are created when the
  client calls `GC_start_mark_threads()` or when the client starts the first
  non-main thread after the GC initialization (or after fork operation in
  a child process). Another effect of this flag is to switch to a more
  concurrent implementation of `GC_malloc_many`, so that free lists can be
  built and memory can be cleared by more than one thread concurrently.
  * Building the collector with `-DTHREAD_LOCAL_ALLOC` adds support for
  thread-local allocation. This causes `GC_malloc` (actually `GC_malloc_kind`)
  and `GC_gcj_malloc` to be redefined to perform thread-local allocation.

Memory returned from thread-local allocators is completely interchangeable
with that returned by the standard allocators. It may be used by other
threads. The only difference is that, if the thread allocates enough memory
of a certain kind, it will build a thread-local free list for objects of that
kind, and allocate from that. This greatly reduces locking. The thread-local
free lists are refilled using `GC_malloc_many`.

An important side effect of this flag is to replace the default
spin-then-sleep lock to be replaced by a spin-then-queue based implementation.
This _reduces performance_ for the standard allocation functions, though
it usually improves performance when thread-local allocation is used heavily,
and, thus, the number of short-duration lock acquisitions is greatly reduced.

## The Parallel Marking Algorithm

We use an algorithm similar to that developed by Endo, Taura, and Yonezawa
([An Effective Garbage Collection Strategy for Parallel Programming Languages on Large Scale Distributed-Memory Machines](https://dl.acm.org/doi/pdf/10.1145/263767.263801))
at the University of Tokyo. However, the data structures and implementation
are different, and represent a smaller change to the original collector
source, probably at the expense of extreme scalability. Some of the
refinements they suggest, e.g. splitting large objects, were also incorporated
into our approach.

The global mark stack is transformed into a global work queue. Unlike the
usual case, it never shrinks during a mark phase. The mark threads remove
objects from the queue by copying them to a local mark stack and changing the
global descriptor to zero, indicating that there is no more work to be done
for this entry. This removal is done with no synchronization. Thus it is
possible for more than one worker to remove the same entry, resulting in some
work duplication.

The global work queue grows only if a marker thread decides to return some
of its local mark stack to the global one. This is done if the global queue
appears to be running low, or if the local stack is in danger of overflowing.
It does require synchronization, but should be relatively rare.

The sequential marking code is reused to process local mark stacks. Hence the
amount of additional code required for parallel marking is minimal.

It should be possible to use incremental/generational collection in the
presence of the parallel collector by calling `GC_enable_incremental`, but
the current implementation does not allow interruption of the parallel marker,
so the latter is mostly avoided if the client sets the collection time limit.

Gcj-style mark descriptors do not currently mix with the combination of local
allocation and incremental collection. They should work correctly with one or
the other, but not both.

The number of marker threads is set on startup to the number of available
processor cores (or to the value of either `GC_MARKERS` or `GC_NPROCS`
environment variable, if provided). If only a single processor is detected,
parallel marking is disabled.

Note that setting `GC_NPROCS` to 1 also causes some lock acquisitions inside
the collector to immediately yield the processor instead of busy waiting
first. In the case of a multiprocessor and a client with multiple
simultaneously runnable threads, this may have disastrous performance
consequences (e.g. a factor of 10 slowdown).

## Performance

We conducted some simple experiments with a version of
[our GC benchmark](http://www.hboehm.info/gc/gc_bench/) that was slightly
modified to run multiple concurrent client threads in the same address space.
Each client thread does the same work as the original benchmark, but they
share a heap. This benchmark involves very little work outside of memory
allocation. This was run with an ancient GC (released in 2000) on a dual
processor Pentium III/500 machine under Linux 2.2.12.

Running with a thread-unsafe collector, the benchmark ran in 9 seconds. With
the simple thread-safe collector, built with `-DGC_THREADS`, the execution
time increased to 10.3 seconds, or 23.5 elapsed seconds with two clients. (The
times for the `malloc`/`free` version with glibc `malloc` are 10.51 (standard
library, pthreads not linked), 20.90 (one thread, pthreads linked), and 24.55
seconds, respectively. The benchmark favors a garbage collector, since most
objects are small.)

The following table gives execution times for the collector built with
parallel marking and thread-local allocation support
(`-DGC_THREADS -DPARALLEL_MARK -DTHREAD_LOCAL_ALLOC`). We tested the client
using either one or two marker threads, and running one or two client threads.
Note that the client uses thread local allocation exclusively. With
`-DTHREAD_LOCAL_ALLOC` the collector switches to a locking strategy that
is better tuned to less frequent lock acquisition. The standard allocation
primitives thus perform slightly worse than without `-DTHREAD_LOCAL_ALLOC`,
and should be avoided in time-critical code.

(The results using `pthread_mutex_lock` directly for allocation locking would
have been worse still, at least for older versions of linuxthreads. With
`-DTHREAD_LOCAL_ALLOC`, we first repeatedly try to acquire the lock with
`pthread_mutex_try_lock`, busy-waiting between attempts. After a fixed number
of attempts, we use `pthread_mutex_lock`.)

These measurements do not use incremental collection, nor was prefetching
enabled in the marker. We used the C version of the benchmark. All
measurements are in elapsed seconds on an unloaded machine.

Number of client threads| 1 marker thread (secs.)| 2 marker threads (secs.)
---|------|-----
  1| 10.45| 7.85
  2| 19.95| 12.3

The execution time for the single threaded case is slightly worse than with
simple locking. However, even the single-threaded benchmark runs faster than
even the thread-unsafe version if a second processor is available. The
execution time for two clients with thread local allocation time is only 1.4
times the sequential execution time for a single thread in a thread-unsafe
environment, even though it involves twice the client work. That represents
close to a factor of 2 improvement over the 2 client case with the old
collector. The old collector clearly still suffered from some contention
overhead, in spite of the fact that the locking scheme had been fairly well
tuned.

Full linear speedup (i.e. the same execution time for 1 client on one
processor as 2 clients on 2 processors) is probably not achievable on this
kind of hardware even with such a small number of processors, since the memory
system is a major constraint for the garbage collector, the processors usually
share a single memory bus, and thus the aggregate memory bandwidth does not
increase in proportion to the number of processors (cores).

These results are likely to be very sensitive to both hardware and OS issues.
Preliminary experiments with an older Pentium Pro machine running an older
kernel were far less encouraging.
