On-the-fly garbage collection: New algorithms inspired by program proofs

Garbage collection (GC) algorithms play a key role in reducing the write amplification in flash-based solid state drives, where the write amplification affects the lifespan and speed of the drive. This paper introduces a mean field model to assess the write amplification and the distribution of the number of valid pages per block for a class C of GC algorithms. Apart from the Random GC algorithm, class C includes two novel GC algorithms: the d-Choices GC algorithm, that selects d blocks uniformly at random and erases the block containing the least number of valid pages among the $d$ selected blocks, and the Random++ GC algorithm, that repeatedly selects another block uniformly at random until it finds a block with a lower than average number of valid blocks. Using simulation experiments we show that the proposed mean field model is highly accurate in predicting the write amplification (for drives with $N=50000$ blocks). We further show that the d-Choices GC algorithm has a write amplification close to that of the Greedy GC algorithm even for small d values, e.g., d = 10, and offers a more attractive trade-off between its simplicity and its performance than the Windowed GC algorithm introduced and analyzed in earlier studies. The Random++ algorithm is shown to be less effective as it is even inferior to the FIFO algorithm when the number of pages $b$ per block is large (e.g., for b ≥ 64).

Programmers are writing a large and rapidly growing number of programs in object-oriented languages such as Java that require garbage collection (GC). To explore the design and evaluation of GC algorithms quickly, researchers are using simulation based on traces of object allocation and lifetime behavior. The brute force method generates perfect traces using a whole-heap GC at every potential GC point in the program. Because this process is prohibitively expensive, researchers often use granulated traces by collecting only periodically, e.g., every 32K bytes of allocation.We extend the state of the art for simulating GC algorithms in two ways. First, we present a systematic methodology and results on the effects of trace granularity for a variety of copying GC algorithms. We show that trace granularity often distorts GC performance results compared with perfect traces, and that some GC algorithms are more sensitive to this effect than others. Second, we introduce and measure the performance of a new precise algorithm for generating GC traces which is over 800 times faster than the brute force method. Our algorithm, called Merlin, frequently timestamps objects and later uses the timestamps of dead objects to reconstruct precisely when they died. It performs only periodic garbage collections and achieves high accuracy at low cost, eliminating any reason to use granulated traces.

Programmers are writing a large and rapidly growing number of programs in object-oriented languages such as Java that require garbage collection (GC). To explore the design and evaluation of GC algorithms quickly, researchers are using simulation based on traces of object allocation and lifetime behavior. The brute force method generates perfect traces using a whole-heap GC at every potential GC point in the program. Because this process is prohibitively expensive, researchers often use granulated traces by collecting only periodically, e.g., every 32K bytes of allocation.We extend the state of the art for simulating GC algorithms in two ways. First, we present a systematic methodology and results on the effects of trace granularity for a variety of copying GC algorithms. We show that trace granularity often distorts GC performance results compared with perfect traces, and that some GC algorithms are more sensitive to this effect than others. Second, we introduce and measure the performance of a new precise algorithm for generating GC traces which is over 800 times faster than the brute force method. Our algorithm, called Merlin, frequently timestamps objects and later uses the timestamps of dead objects to reconstruct precisely when they died. It performs only periodic garbage collections and achieves high accuracy at low cost, eliminating any reason to use granulated traces.

The paper presents the cache performance analysis of the Chronological Garbage Collection algorithm used in the LVM system. The LVM is a new Logic Virtual Machine for Prolog. It adopts one stack policy for all dynamic memory requirements and cooperates with an efficient garbage collection algorithm, Chronological Garbage Collection to recuperate space, not as deliberate garbage collection operation but as a natural activity of the LVM engine to gather useful objects. This algorithm takes advantages of the traditional copying, mark-compact, generational, and incremental garbage collection schemes. In order to determine the improvement of cache performance under our garbage collection algorithm, we developed an emulator to do the trace driven cache simulation. Direct mapped cache and set-associative cache with different cache sizes, block sizes and set associativities are simulated and measured. The objectives of this simulation are to verify and validate our experimental results, and to find important factors which influence the performance of the CGC algorithm.

Big Data applications suffer from unpredictable and unacceptably high pause times due to Garbage Collection (GC). This is the case in latency-sensitive applications such as on-line credit-card fraud detection, graph-based computing for analysis on social networks, etc. Such pauses compromise latency requirements of the whole application stack and result from applications' aggressive buffering/caching of data, exposing an ill-suited GC design, which assumes that most objects will die young and does not consider that applications hold large amounts of middle-lived data in memory. To avoid such pauses, we propose NG2C, a new GC algorithm that combines pretenuring with user-defined dynamic generations. By being able to allocate objects into different generations, NG2C is able to group objects with similar lifetime profiles in the same generation. By allocating objects with similar lifetime profiles close to each other, i.e. in the same generation, we avoid object promotion (copying between generations) and heap fragmentation (which leads to heap compactions) both responsible for most of the duration of HotSpot GC pause times. NG2C is implemented for the OpenJDK 8 HotSpot Java Virtual Machine, as an extension of the Garbage First GC. We evaluate NG2C using Cassandra, Lucene, and GraphChi with three different GCs: Garbage First (G1), Concurrent Mark Sweep (CMS), and NG2C. Results show that NG2C decreases the worst observable GC pause time by up to 94.8% for Cassandra, 85.0% for Lucene and 96.45% for GraphChi, when compared to current collectors (G1 and CMS). In addition, NG2c has no negative impact on application throughput or memory usage.

NAND flash memory is a popular storage device for consumer electronics and it is exploited to be swap space for extending the limited main memory of consumer electronics. Due to the out-of-place update scheme provided by NAND flash memory to solve its erase-before-write hardware constraint, garbage collection should be performed to reclaim garbage in terms of invalid pages and obtain free space in terms of free blocks. The garbage collection, which consists of a series of copy operations and erase operations, is energy-consuming. In order to reduce the energy consumption, a swap-aware garbage collection algorithm for NAND flash-based consumer electronics is proposed in this paper. The proposed algorithm focuses on reducing the garbage collection overhead and improving the endurance of NAND flash memory. Experimental results show that the proposed algorithm is superior to the existing garbage collection algorithms in terms of the number of copy operations, the number of erase operations, the degree of wear-leveling, and energy consumption.

Summary form only given. Modern programming languages are provided with a mechanism for automatic memory management called garbage collection. A lot of researches had been introduced in this area. On the other hand, some problems existed when applying such languages to the real-time applications. Among these problems, there was the threat of predictability and schedulability of hard real-time tasks. As a solution, different concurrent garbage collection scheduling strategies had been proposed. Unfortunately, these concurrent garbage collectors suffered from redundant usage of system memory. A latter algorithm that minimizes the worst-case system memory requirement with the schedulability of tasks not jeopardized is the deferrable server based garbage collector scheduling strategy. The deferrable server based garbage collection had been proved to surpass all other garbage collection scheduling strategies in minimizing system memory requirements under the worst-case conditions. In this paper, the performance of the deferrable server based garbage collector is explored under the actual real-time working environment. It also investigates the selection of thresholds to further reduce the memory requirement by using higher capacities of the deferrable server task. The simulation results demonstrate that the deferrable sever based garbage collector achieves better performance under actual real-time environment than other garbage collection algorithms. It also shows that using higher values for the server capacities can lead to better memory usage if a proper value of threshold is chosen.

We present the design and implementation of a new garbage collection framework that significantly generalizes existing copying collectors. The Beltway framework exploits and separates object age and incrementality. It groups objects in one or more increments on queues called belts , collects belts independently, and collects increments on a belt in first-in-first-out order. We show that Beltway configurations, selected by command line options, act and perform the same as semi-space, generational, and older-first collectors, and encompass all previous copying collectors of which we are aware. The increasing reliance on garbage collected languages such as Java requires that the collector perform well. We show that the generality of Beltway enables us to design and implement new collectors that are robust to variations in heap size and improve total execution time over the best generational copying collectors of which we are aware by up to 40%, and on average by 5 to 10%, for small to moderate heap sizes. New garbage collection algorithms are rare, and yet we define not just one, but a new family of collectors that subsumes previous work. This generality enables us to explore a larger design space and build better collectors.

We present the design and implementation of a new garbage collection framework that significantly generalizes existing copying collectors. The Beltway framework exploits and separates object age and incrementality. It groups objects in one or more increments on queues called belts, collects belts independently, and collects increments on a belt in first-in-first-out order. We show that Beltway configurations, selected by command line options, act and perform the same as semi-space, generational, and older-first collectors, and encompass all previous copying collectors of which we are aware. The increasing reliance on garbage collected languages such as Java requires that the collector perform well. We show that the generality of Beltway enables us to design and implement new collectors that are robust to variations in heap size and improve total execution time over the best generational copying collectors of which we are aware by up to 40%, and on average by 5 to 10%, for small to moderate heap sizes. New garbage collection algorithms are rare, and yet we define not just one, but a new family of collectors that subsumes previous work. This generality enables us to explore a larger design space and build better collectors.

Lock-free parallel and concurrent garbage collection by mark&sweep

A stable heap is storage that is managed automatically using garbage collection, manipulated using atomic transactions, and accessed using a uniform storage model. These features enhance reliability and simplify programming by preventing errors due to explicit deallocation, by masking failures and concurrency using transactions, and by eliminating the distinction between accessing temporary storage and permanent storage. Stable heap management is useful for programming languages for reliable distributed computing, programming languages with persistent storage, and object-oriented database systems.Many applications that could benefit from a stable heap (e.g., computer-aided design, computer-aided software engineering, and office information systems) require large amounts of storage, timely responses for transactions, and high availability. We present garbage collection and recovery algorithms for a stable heap implementation that meet these goals and are appropriate for stock hardware. The collector is incremental: it does not attempt to collect the whole heap at once. The collector is also atomic: it is coordinated with the recovery system to prevent problems when it moves and modifies objects. The time for recovery is independent of heap size, even if a failure occurs during garbage collection.

Application-specific garbage collection

Most application's performance is impacted by the amount of available memory. In a traditional application, which has a fixed working set size, increasing memory has a beneficial effect up until the application's working set is met. In the presence of garbage collection this relationship becomes more complex. While increasing the size of the program's heap reduces the frequency of collections, collecting a heap with memory paged to the backing store is very expensive. We first demonstrate the presence of an optimal heap size for a number of applications running on a machine with a specific configuration. We then introduce a scheme which adaptively finds this good heap size. In this scheme, we track the memory usage and number of page faults at a program's phase boundaries. Using this information, the system selects the soft heap size. By adapting itself dynamically, our scheme is independent of the underlying main memory size, code optimizations, and garbage collection algorithm. We present several experiments on real applications to show the effectiveness of our approach. Our results show that program-level heap control provides up to a factor of 7.8 overall speedup versus using the best possible fixed heap size controlled by the virtual machine on identical garbage collectors.

Garbage collection algorithms that do not require tagging of data have been around since the early days of LISP. With the emergence of strongly-typed languages that require heap allocation, interest in tag-free garbage collection has increased. Several papers published recently describe methods for performing tag-free copying garbage collection by retaining compile-time type information at run time. However, all of these algorithms have the “stop and collect” property, in which program execution is suspended for a significant amount of time during garbage collection. For many programs an incremental garbage collection method, in which the garbage collection overhead is spread evenly throughout the computation, is desirable.

Because of their numerous benefits, solid-state drives (SSDs) are increasingly being used in a wide range of applications, including data centers, cloud computing, and high-performance computing. The growing demand for SSDs has led to a continuous improvement in their technology and a reduction in their cost, making them a more accessible storage solution for a wide range of users. Garbage collection (GC) is a process that reclaims wasted storage space in NAND flash memories, which are used as the memory devices for SSDs. However, the GC process can cause performance degradation and lifetime reduction. This paper proposes an efficient garbage collection (GC) scheme that minimizes overhead by invoking GC operations only when necessary. Each GC operation is executed in a specific order based on the expected storage gain and the execution cost, ensuring that the storage space requirement is met while minimizing the frequency of GC invocation. This approach not only reduces the overhead due to GC, but also improves the overall performance of SSDs, including the latency and write amplification factor (WAF) which is an important indicator of the longevity of SSDs.