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.

Dynamic memory allocation has been a fundamental part of most computer systems since roughly 1960, and memory allocation is widely considered to be either a solved problem or an insoluble one. In this survey, we describe a variety of memory allocator designs and point out issues relevant to their design and evaluation. We then chronologically survey most of the literature on allocators between 1961 and 1995. (Scores of papers are discussed, in varying detail, and over 150 references are given.) We argue that allocator designs have been unduly restricted by an emphasis on mechanism, rather than policy, while the latter is more important; higher-level strategic issues are still more important, but have not been given much attention. Most theoretical analyses and empirical allocator evaluations to date have relied on very strong assumptions of randomness and independence, but real program behavior exhibits important regularities that must be exploited if allocators are to perform well in practice.

Dynamic storage allocation has become increasingly important in many applications, in part due to the use of the object-oriented paradigm. At the same time, processor speeds are increasing faster than memory speeds and programs are increasing in size faster than memories. In this paper, we investigate e#orts to predict heap object reference and lifetime behavior at the time objects are allocated. Our approach uses profile-based optimization, and considers a variety of di#erent information sources present at the time of object allocation to predict the object's reference frequency and lifetime. Our results, based on measurements of six allocation intensive programs, show that program references to heap objects are highly predictable and that our prediction methods can successfully predict the behavior of these heap objects. We show that our methods can decrease the page fault rate of the programs measured, sometimes dramatically, in cases where the physical memory available to the progr...

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...

The complexity of systems for automatic control and other safety-critical applications grows rapidly. Computer software represents an increasing part of the complexity. As larger systems are developed, we need to find scalable techniques to manage the complexity in order to guarantee high product quality. Memory management is a key quality factor for these systems. Automatic memory management, or garbage collection, is a technique that significantly reduces the complex problem of correct memory management. The risk of software errors decreases and development time is reduced. Garbage collection techniques suitable for interactive and soft real-time systems exist, but few approaches are suitable for systems with hard real-time requirements, such as control systems (embedded systems). One part of the problem is solved by incremental garbage collection algorithms, which have been presented before. We focus on the scheduling problem which forms the second part of the problem, i.e. how the work of a garbage collector should be scheduled in order

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.

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...

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

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...