Garbage collection algorithms have been enhanced in recent years with two methods: generation-based collection and Baker incremental copying collection. Generation-based collection requires special actions during certain store operations to implement the "write barrier." Incremental collection requires special actions on certain load operations to implement the "read barrier." This paper evaluates the performance of different implementations of the read and write barriers and reaches several important conclusions. First, the inlining of barrier checks results in surprisingly low overheads, both for the write barrier (2--6%) and the read barrier (! 20%). Contrary to previous belief, these results suggest that a Baker-style read barrier can be implemented efficiently without hardware support. Second, the use of operating system traps to implement garbage collection methods results in extremely high overheads because the cost of trap handling is so high. Since this large overhead is comple...

Heap allocation with copying garbage collection is believed to have poor memory subsystem performance. We conducted a study of the memory subsystem performance of heap allocation for memory subsystems found on many machines. We found that many machines support heap allocation poorly. However, with the appropriate memory subsystem organization, heap allocation can have good memory subsystem performance.

Cache performance is an important part of total performance in modern computer systems. This paper describes the use of trace-driven simulation to estimate the effect of garbage collection algorithms on cache performance. Traces from four large Common Lisp programs have been collected and analyzed with an all-associativity cache simulator. While previous work has focused on the effect of garbage collection on page reference locality, this evaluation unambiguously shows that garbage collection algorithms can have a profound effect on cache performance as well. On processors with a direct-mapped cache, a generation stop-and-copy algorithm exhibits a miss rate up to four times higher than a comparable generation mark-and-sweep algorithm. Furthermore, two-way set-associative caches are shown to reduce the miss rate in stop-and-copy algorithms often by a factor of two and sometimes by a factor of almost five over directmapped caches. As processor speeds increase, cache performance will play...

This paper describes the design and implementation of a concurrent compacting garbage collector for languages that distinguish mutable data from immutable data (e.g., ML) as well as for languages that manipulate only immutable data (e.g., pure functional languages such as Haskell). The collector runs on shared-memory parallel computers and requires minimal mutator/collector synchronization. No special hardware or operating system support is required. Our concurrent collector derives from sequential semi-space copying collectors. The collector requires that a heap object includes a forwarding pointer in addition to its data fields. An access to an immutable object can be satisfied either by the original or the forwarded copy of the object. A mutable object is always accessed through the forwarded copy, if one exists. Measurements of this collector in a Standard ML compiler on a shared-memory computer indicate that it eliminates perceptible garbage-collection pauses by reclaiming storage...

Abstract. We present a garbage collection algorithm that extends generational scavenging to collect large older generations (mature objects) non-disruptively. The algorithm’s approach is to process bounded-size pieces of mature object space at each collection; the subtleties lie in guaranteeing that it eventually collects any and all garbage. The algorithm does not assume any special hardware or operating system support, e.g., for forwarding pointers or protection traps. The algorithm copies objects, so it naturally supports compaction and reclustering.

Garbage collection yields numerous software engineering benefits, but its quantitative impact on performance remains elusive. One can compare the cost of conservative garbage collection to explicit memory management in C/C++ programs by linking in an appropriate collector. This kind of direct comparison is not possible for languages designed for garbage collection (e.g., Java), because programs in these languages naturally do not contain calls to free. Thus, the actual gap between the time and space performance of explicit memory management and precise, copying garbage collection remains unknown. We introduce a novel experimental methodology that lets us quantify the performance of precise garbage collection versus explicit memory management. Our system allows us to treat unaltered Java programs as if they used explicit memory management by relying

Heap allocation with copying garbage collection is a general storage management technique for modern programming languages. It is believed to have poor memory subsystem performance. To investigate this, we conducted an in-depth study of the memory subsystem performance of heap allocation for memory subsystems found on many machines. We studied the performance of mostly-functional Standard ML programs which made heavy use of heap allocation. We found that most machines support heap allocation poorly. However, with the appropriate memory subsystem organization, heap allocation can have good performance. The memory subsystem property crucial for achieving good performance was the ability to allocate and initialize a new object into the cache without a penalty. This can be achieved by having subblock placement with a subblock size of one word with a write allocate policy, along with fast page-mode writes or a write buffer. For caches with subblock placement, the data cache overhead was under 9% for a 64K of larger data cache; without subblock placement the overhead was often higher than 50%.

Garbage collection is the automatic reclamation of computer storage [Knu73, Coh81, Wil92, Wil95]. While in many systems, programmers must explicitly reclaim heap memory at some point in their program by using a "free" or "dispose" statement, garbage collected systems free the programmer from this burden. In spite of its obvious attractiveness for many applications, garbage collection for real-time programs is not popular. This is largely due to the perceived cost and disruptiveness of garbage collection in general, and of incremental garbage collection in particular. Most existing "real-time" garbage collectors are not in fact usefully real-time, largely due to the use of a read barrier to trigger incremental copying of data structures being traversed by the running application. This may slow down running applications unpredictably, even though individual increments of garbage collection work are small and bounded. We have developed a hard real-time garbage collector which us...

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If a fixed exponentially decreasing probability distribution function is used to model every object’s lifetime, then the age of an object gives no information about its future life expectancy. This radioactive decay model implies there can be no rational basis for deciding which live objects should be promoted to another generation. Yet there remains a rational basis for deciding how many objects to promote, when to collect garbage, and which generations to collect. Analysis of the model leads to a new kind of generational garbage collector whose effectiveness does not depend upon heuristics that predict which objects will live longer than others. This result provides insight into the computational advantages of generational garbage collection, with implications for the management of objects whose life expectancies are difficult to predict. 1