We survey basic garbage collection algorithms, and variations such as incremental and generational collection; we then discuss low-level implementation considerations and the relationships between storage management systems, languages, and compilers. Throughout, we attempt to present a uni ed view based on abstract traversal strategies, addressing issues of conservatism, opportunism, and immediacy of reclamation; we also point outavariety of implementation details that are likely to have a significant impact on performance.

this paper, I evaluate the costs of different dynamic storage management algorithms, including domain-specific allocators, widelyused general-purpose allocators, and a publicly available conservative garbage collection algorithm. Surprisingly, I find that programmer enhancements often have little effect on program performance. I also find that the true cost of conservative garbage collection is not the CPU overhead, but the memory system overhead of the algorithm. I conclude that conservative garbage collection is a promising alternative to explicit storage management and that the performance of conservative collection is likely to improve in the future. C programmers should now seriously consider using conservative garbage collection instead of explicitly calling free in programs they write

The allocation and disposal of memory is a ubiquitous operation in most programs. Rarely do programmers concern themselves with details of memory allocators; most assume that memory allocators provided by the system perform well. This paper presents a performance evaluation of the reference locality of dynamic storage allocation algorithms based on trace-driven simulation of five large allocation-intensive C programs. In this paper, we show how the design of a memory allocator can significantly affect the reference locality for various applications. Our measurements show that poor locality in sequential-fit allocation algorithms reduces program performance, both by increasing paging and cache miss rates. While increased paging can be debilitating on any architecture, cache misses rates are also important for modern computer architectures. We show that algorithms attempting to be space-efficient by coalescing adjacent free objects show poor reference locality, possibly negating the benef...

Abstract The trends in software development are towards larger programs, more complex programs, and more use of programs as "component software. " These trends mean that the features of modern programming languages are becoming more important than ever before. Programming languages need to have features such as strong typing, a module system, polymorphism, automatic storage management, and higher-order functions. In short, modern programming languages are becoming more important than ever before.

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.

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We present a comprehensive analysis of all the components of creation, access, and disposal of heap-allocated and stack-allocated activation records. Among our results are: . Although stack frames are known to have a better cache read-miss rate than heap frames, our simple analytical model (backed up by simulation results) shows that the di#erence is too trivial to matter. . The cache write-miss rate of heap frames is very high; we show that a variety of miss-handling strategies (exemplified by specific modern machines) can give good performance, but not all can. . Stacks restrict the flexibility of closure representations (for higher-order functions) in important (and costly) ways. . The extra load placed on the garbage collector by heap-allocated frames is small. . The demands of modern programming languages make stacks complicated to implement e#ciently and correctly. Overall, the execution cost of stack-allocated and heap-allocated frames is similar; but heap frames are s...

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%.

ABSTRACT In systems that support garbage collection, a tension exists between collecting garbage too frequently and not collecting garbage frequently enough. Garbage collection that occurs too frequently may introduce unnecessary overheads at the risk of not collecting much garbage during each cycle. On the other hand, collecting garbage too infrequently can result in applications that execute with a large amount of virtual memory (i.e., with a large footprint) and suffer from increased execution times due to paging. In this paper, we use a large collection of JavaTMapplications and the highly tuned and widely used Boehm-DemersWeiser (BDW) conservative mark-and-sweep garbage collector to experimentally examine the extent to which the frequency of garbage collection impacts an application's execution time, footprint, and pause times. We use these results to devise some guidelines for controlling garbage collection and heap growth in a conservative garbage collector in order to minimize application execution times. Then we describe new strategies for controlling garbage collection and heap growth that impact not only the frequency with which garbage collection occurs but also the points at which garbage collection occurs. Experimental results demonstrate that, when compared with the existing approach used in the standard BDW collector, our new strategy can significantly reduce application execution times. Our goal is to obtain a better understanding of how to control garbage collection and heap growth for an individual application executing in isolation. These results can be applied in a number of high-performance computing and server environments, in addition to some single-user environments. This work should also provide insights into how

Because dynamic memory management is an important part of a large class of computer programs, high-performance algorithms for dynamic memory management have been, and will continue to be, of considerable interest. We evaluate and compare models of the memory allocation behavior in actual programs and investigate how these models can be used to explore the performance of memory management algorithms. These models, if accurate enough, provide an attractive alternative to algorithm evaluation based on trace-driven simulation using actual traces. We explore a range of models of increasing complexity including models that have been used by other researchers. Based on our analysis, we draw three important conclusions. First, a very simple model, which generates a uniform distribution around the mean of observed values, is often quite accurate. Second, two new models we propose show greater accuracy than those previously described in the literature. Finally, none of the models investigated ap...