A Lifetime-Based Garbage Collector for LISP Systems on General-Purpose Computers.

Sobalvarro, Patrick

doi:10.21236/ada290169

Cited by 36 publications

(21 citation statements)

References 10 publications

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“…mov eax,allocationPtr ; load allocation pointer into a register add eax, 12 ; add amount of space to be allocated cmp eax,allocationLimitPtr ; compare against limit pointer ja needgc ; call gc if necessary mov allocationPtr,eax ; update the allocation pointer Figure 2 shows the filtering code sequence shared by all the filtering write barriers. It uses a table of ages indexed by the higher-order 16 bits of pointer addresses [29]. The ages are inverted (older generations are assigned lower numbers) and 0 is used for non-heap memory such as stacks and statically-allocated data.…”

Section: Write Barrier Implementationsmentioning

confidence: 99%

“…The card table (cards) divides memory into cards that are a power of 2 in size and alignment [25,29,36]. The card table stores one bit of information per card.…”

Section: Write Barrier Implementationsmentioning

confidence: 99%

“…The 2-level card table (cards2) is similar to the wordmarking write barrier of the Lucid Common LISP ephemeral garbage collector [29]. It has two levels: a coarse grain level that has 1 bit per 4 KB of virtual memory and a fine grain level that has 1 bit per pointer (word).…”

Section: Write Barrier Implementationsmentioning

confidence: 99%

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The case for profile-directed selection of garbage collectors

FitzgeraldRobert

TarditiDavid

2000

SIGPLAN Not.

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Many garbage-collected systems use a single garbage collection algorithm across all applications. It has long been known that this can produce poor performance on applications for which that collector is not well suited. In some systems, such as those that execute stand-alone compiled executables, an appropriate collector for each application can be selected from a pool of available collectors and tuned by using profile information. In a study of 20 benchmarks and several collectors, compiled with the Marmot optimizing Java-to-native compiler, for every collector there was at least one benchmark that would have been at least 15% faster with a more appropriate collector. The collectors are a copying collector, a generational copying collector, which is combined with each of 4 different write barriers, and the null collector, which allocates but never collects. A detailed analysis of storage management costs shows how they vary by application and collector.

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Section: Write Barrier Implementationsmentioning

confidence: 99%

“…The card table (cards) divides memory into cards that are a power of 2 in size and alignment [25,29,36]. The card table stores one bit of information per card.…”

Section: Write Barrier Implementationsmentioning

confidence: 99%

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The case for profile-directed selection of garbage collectors

FitzgeraldRobert

TarditiDavid

2000

SIGPLAN Not.

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“…For both algorithms, the remembered set is implemented with a two-level bitmap that indicates the locations of intergenerational pointers as described by Sobalvarro [18].…”

Section: Algorithmsmentioning

confidence: 99%

Comparing mark-and sweep and stop-and-copy garbage collection

Zorn

1990

Proceedings of the 1990 ACM Conference on LISP and Functional Programming

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Stop-and-copy garbage collection has been preferred to markand-sweep collection in the last decade because its collection time is proportional to the size of reachable data and not to the memory size. This paper compares the CPU overhead and the memory requirements of the two collection algorithms extended with generations, and finds that mark-and-sweep collection requires at most a small amount of additional CPU overhead (3-690) but, requires an average of 20% (and up to 40%) less memory to achieve the same page fault rate. The comparison is based on results obtained using trace-driven simulation with large Common Lisp programs.

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“…The default sizes as specified in the SSCLI source code are 800 K-bytes and 1 M-bytes, respectively. Intergenerational pointers are maintained using card marking [9,25] where each card is 4 K-bytes. Objects from unmanaged code are also managed by the garbage collector; handle list is used to store references coming from unmanaged environment.…”

Section: Garbage Collection In the Ssclimentioning

confidence: 99%

Remote Objects: The Next Garbage Collection Challenge.

Srisa-an¹,

Oey²

2005

JOT

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In this paper, we present an analysis of remote objects in Microsoft's Shared Source Common Language Infrastructure. The contribution of our work is threefold. First, we analyze the behavior of remote objects. We find that the basic behavior of these objects significantly differ from the ones of local objects, which have been thoroughly studied in the past. We also study the behavior of remote objects with respect to different activation modes (i.e. single-call, singleton, and client-activated). Second, based on those behavioral differences, we study the impacts of managing remote objects with a generational garbage collector that is designed essentially to manage local objects. We find that the garbage collection efficiency degrades significantly when the heap is interspersed with both local and remote objects. Third, we suggest various optimization techniques to improve the garbage collection efficiency in distributed objects environments.

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A Lifetime-Based Garbage Collector for LISP Systems on General-Purpose Computers.

Cited by 36 publications

References 10 publications

The case for profile-directed selection of garbage collectors

The case for profile-directed selection of garbage collectors

Comparing mark-and sweep and stop-and-copy garbage collection

Remote Objects: The Next Garbage Collection Challenge.

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