Hardware Concurrent Garbage Collection for Short-Lived Objects in Mobile Java Devices

Tracing and reference counting are uniformly viewed as being fundamentally different approaches to garbage collection that possess very distinct performance properties. We have implemented high-performance collectors of both types, and in the process observed that the more we optimized them, the more similarly they behaved - that they seem to share some deep structure. We present a formulation of the two algorithms that shows that they are in fact duals of each other. Intuitively, the difference is that tracing operates on live objects, or "matter", while reference counting operates on dead objects, or "anti-matter". For every operation performed by the tracing collector, there is a precisely corresponding anti-operation performed by the reference counting collector. Using this framework, we show that all high-performance collectors (for example, deferred reference counting and generational collection) are in fact hybrids of tracing and reference counting. We develop a uniform cost-model for the collectors to quantify the trade-offs that result from choosing different hybridizations of tracing and reference counting. This allows the correct scheme to be selected based on system performance requirements and the expected properties of the target application.

Despite some clear advantages and recent advances, reference counting remains a poor cousin to high-performance tracing garbage collectors. The advantages of reference counting include a) immediacy of reclamation, b) incrementality, and c) local scope of its operations. After decades of languishing with hopelessly bad performance, recent work narrowed the gap between reference counting and the fastest tracing collectors to within 10%. Though a major advance, this gap remains a substantial barrier to adoption in performance-conscious application domains. Our work identifies heap organization as the principal source of the remaining performance gap. We present the design, implementation, and analysis of a new collector, RC Immix, that replaces reference counting's traditional free-list heap organization with the line and block heap structure introduced by the Immix collector. The key innovations of RC Immix are 1) to combine traditional reference counts with per-line live object counts to identify reusable memory and 2) to eliminate fragmentation by integrating copying with reference counting of new objects and with backup tracing cycle collection. In RC Immix, reference counting offers efficient collection and the line and block heap organization delivers excellent mutator locality and efficient allocation. With these advances, RC Immix closes the 10% performance gap, matching the performance of a highly tuned production generational collector. By removing the performance barrier, this work transforms reference counting into a serious alternative for meeting high performance objectives for garbage collected languages.

With the advent of new flash-based memory technologies with ultra-low latency, directly applying inline data deduplication in flash-based storage devices can degrade the system performance since key deduplication operations lie on the shortened critical write path of such devices. To address the problem, we propose a Content-Aware Garbage Collection scheme (CAGC), which embeds the data deduplication into the data movement workflow of the Garbage Collection (GC) process in ultra-low latency flash-based SSDs. By parallelizing the operations of valid data pages migration, hash computing and flash block erase, the deduplication-induced performance overhead is alleviated and redundant page writes during the GC period are eliminated. To further reduce data writes and write amplification during GC, CAGC separates and stores data pages in different regions based on their reference counts. The performance evaluation of our CAGC prototype implemented in FlashSim shows that CAGC significantly reduces the number of flash blocks erased and data pages migrated during GC, leading to improved user I/O performance and reliability of ultra-low latency flash-based SSDs.

General purpose garbage collectors have yet to combine short pause times with high throughput. For example, generational collectors can achieve high throughput. They have modest average pause times, but occasionally collect the whole heap and consequently incur long pauses. At the other extreme, concurrent collectors, including reference counting, attain short pause times but with significant performance penalties. This paper introduces a new hybrid collector that combines copying generational collection for the young objects and reference counting the old objects to achieve both goals. It restricts copying and reference counting to the object demographics for which they perform well. Key to our algorithm is a generalization of deferred reference counting we call Ulterior Reference Counting . Ulterior reference counting safely ignores mutations to select heap objects. We compare a generational reference counting hybrid with pure reference counting, pure mark-sweep, and hybrid generational mark-sweep collectors. This new collector combines excellent throughput, matching a high performance generational mark-sweep hybrid, with low maximum pause times.

Distributed applications for data analytics have emerged to be a significant growth domain both in terms of technology as well as business relevance. With the advent of Hadoop MapReduce, several distributed execution frameworks have been developed for data analytics and machine learning applications. Many of these frameworks are written in Java, Scala or Python and run on top of the Java Virtual Machine (JVM), leaving them vulnerable to the performance impact of garbage collection (GC) operations. Application pauses induced by the Java GC are notorious in being one of the most important contributors of runtime performance bottleneck for a single process execution. These pauses are present in the distributed scenario as well, though in this case, it is significantly more complex to measure the impact of a GC pause for the overall execution runtime. Typically, in a distributed workload, a GC can result in delayed tasks impacting the overall completion time of the application. We present herein a novel way to preemptively trigger Java GC, stealing idle cycles, so as to prevent future tasks from being impacted by a GC pause during peak execution periods, resulting in improved overall application throughput.

Of the Gabriel Lisp Benchmarks, the Boyer Benchmark ("Boyer") is the most representative of real AI applications, because it performs Prolog-like rule-directed rewriting, and because it relies heavily on garbage collection (GC) for the recovery of storage. We investigated the notion that such programs are unsuitable for explicit storage management---e.g., by means of a "linear" programming style in which every bound name dynamically occurs exactly once. We programmed Boyer in a "linear" fragment of Lisp in both interpretive-rule and compiled-rule versions, using both true linear (unshared) and reference count methods.We found that since the intermediate result of rewrite is unshared, the linear interpreted version is slower than the non-linear interpreted version, while the linear compiled version is slightly faster than the non-linear compiled version. When sharing is allowed, requiring reference counts for the linear versions, the linear shared versions are not competitive with the non-linear versions, except when "anchored pointers" are used. The anchored pointer reference count version, which reclaims storage, is still 1.25X slower than the non-linear version, which reclaims no storage.

The garbage collector (GC) is a critical component of any managed runtime environment (MRE), such as the Java virtual machine. While the main goal of the GC is to simplify and automate memory management, it may have a negative impact on the application performance, especially on multi-core systems. This is typically due to stop-the-world pauses, i.e., intervals for which the application threads are blocked during the collection. Existing approaches to concurrent GCs allow the application threads to perform at the same time as the GC at the expense of throughput and simplicity. In this paper we build upon an existing pauseless transactional GC algorithm and design an important optimization that would significantly increase its throughput. More precisely, we devise selective access barriers, that define multiple paths based on the state of the garbage collector. Preliminary evaluation of the selective barriers shows up to 93% improvement over the initial transactional barriers in the worst case scenario. We estimate the performance of a pauseless GC having selective transactional barriers and find it to be on par with Java's concurrent collector.

Nondeferred reference-counting (RC) garbage collection is among the oldest memory-management methods. Despite offering unique advantages, little attention has been paid on how to correctly implement it for modern programming languages. This paper revisits this collection method and describes how to implement it for a modern object-oriented language in an optimizing compiler.The main contribution is a general algorithm that realizes one form of nondeferred RC collection for an object-oriented language having features such as exceptions, interior pointers, and object pinning. The algorithm abstracts the pertinent characteristics of instructions using concepts from data-flow analysis, such as def/use information, so that instructions are handled in a uniform manner, instead of in an ad hoc or special-case way. The abstracted information is used to systematically compute what increments and decrements to do, even in the presence of subtle conditions such as exceptional control flow. These techniques enabled us to compile a large suite of programs to use nondeferred RC collection. The paper also discusses the modifications that were necessary in the compiler for supporting the inserted RC operations, and reports measurements from a reference implementation.

Live migration of Java Virtual Machines (JVMs) consumes significant amounts of time and resources, imposing relevant application performance overhead. This problem is specially hard when memory modified by applications changes faster than it can be transferred through the network (to a remote host). Current solutions to this problem resort to several techniques which depend on high-speed networks and application throttling, require lots of CPU time to compress memory, or need explicit assistance from the application. We propose a novel approach, Garbage Collector (GC) assisted JVM Live Migration for Java Server Applications (ALMA). ALMA makes a snapshot to be migrated containing a minimal amount of application state, by taking into account the amount of reachable memory (i.e. live data) detected by the GC. The main novelty of ALMA is the following: ALMA analyzes the JVM heap looking for regions in which a collection phase is advantageous w.r.t. the network bandwidth available (i.e. it pays to collect because a significant amount of memory will not be part of the snapshot). ALMA is implemented on OpenJDK 8 and extends CRIU (a Linux disk-based process checkpoint/restore tool) to support process live migration over the network. We evaluate ALMA using well-known JVM performance benchmarks (SPECjvm2008 and DaCapo), and by comparing it to other previous approaches. ALMA shows very good performance results.

During recent years, microprocessor energy consumption has been surging and efforts to reduce power and energy have received a lot of attention. At the same time, virtual execution environments (VEEs), such as Java virtual machines, have grown in popularity. Hence, it is important to evaluate the impact of virtual execution environments on microprocessor energy consumption. This paper characterizes the energy and power impact of two important components of VEEs, Just-in-time(JIT) optimization and garbage collection. We find that by reducing instruction counts, JIT optimization significantly reduces energy consumption, while garbage collection incurs runtime overhead that consumes more energy. Importantly, both JIT optimization and garbage collection decrease the average power dissipated by a program. Detailed analysis reveals that both JIT optimizer and JIT optimized code dissipate less power than un-optimized code. On the other hand, being memory bound and with low ILP, the garbage collector dissipates less power than the application code, but rarely affects the average power of the latter.Adaptive microarchitectures are another recent trend for energy reduction where microarchitectural resources can be dynamically tuned to match program runtime requirements. This research reveals that both JIT optimization and garbage collection alter a program's behavior and runtime requirements, which considerably affects the adaptation of configurable hardware units, and influences the overall energy consumption. This work also demonstrates that the adaptation preferences of the two VEE services differ substantially from those of the application code. Both VEE services prefer a simple core for high energy reduction. On the other hand, the JIT optimizer usually requires larger data caches, while the garbage collector rarely benefits from large data caches. The insights gained in this paper point to novel techniques that can further reduce microprocessor energy consumption.

Now a day's data is growing very rapidly, where processing and analyzing data to get useful information is the main task. There are many big data processing tools and framework such as Hadoop, Hive, Cassandra etc. Spark is one of the fastest big data processing framework in cluster computation.Basic Idea is to analyze the performance of java virtual machine (JVM) [1], by characterizing java virtual machine using SparkBench benchmark on Apache Spark™ [2]. Java virtual machine is a core execution platform for spark application. When we run the spark application on java virtual machine, its behavior is affected, which needs to be monitored to analyze the JVM performance. Here we are considering Machine Learning workloads like K-Means, Matrix Factorization and Logistic Regression. Main goal here is to analyze the machine learning workloads end to end across the cluster, with respect to following parameters such as garbage collection, memory such as heap usage, CPU process time. Characterization of JVM is done with spark cluster setup and HDFS is used as storage with distributed Hadoop cluster setup.

As large amounts of garbage are generated in concurrent logic programs, it becomes necessary to salvage used memory frequently and efficiently, with a garbage collector. Another approach is to detect when a data structure becomes garbage and reuse it. In concurrent languages it is particularly difficult to determine when a data structure is garbage and suitable for destructive update. Dynamic schemes, such as reference counting, incur space and time overheads that can be unacceptable. In contrast, static-analysis techniques identify data objects whose storage may be reused. Information from static analysis can be used by the compiler in generating appropriate instructions for reuse, incurring little or no runtime overhead. In this paper we present a new static-analysis technique for reuse detection. We present empirical performance measurements comparing the new scheme against binary reference counting (MRB). It is shown that, a simple static analysis can achieve most of the benefits of MRB and improve the execution time.

In recent years server applications using Java become popular. However, they have different performance requirements from other applications: high throughput and small response time. One of obstacles for achieving those requirements is a Java Virtual Machine (JVM). Among the services that a JVM provides, garbage collection affects server applications in throughput and latency. Some JVMs have various garbage collectors for server-side Java but they do not still consider the behavior of server applications. We show that the lifetime pattern of objects is distinguished by the thread that allocates them in server applications. Separating objects and applying different collection policies according to threads, we propose that a garbage collector can achieve both high throughput and small pause time. Experiments show that the throughput of our collector is up to 1.7 times greater than that of previous generational collectors with the same pause time and the pause time of minor collection is smaller by almost 10% given the same throughput.

The memory size of Java is very limited, while the Java Card specification does not specify the garbage collection mechanism, resulting in the utilization of system memory resources is not efficiently. In order to solve this problem, we adopted cross-layer design ideas, took Flash physical storage characteristics and the features of Java Card object allocation into consideration, designed the generational adaptive mark-sweep-copy garbage collection algorithm for Java Card. In this paper we use small part of RAM resource, effectively reduced the number of write operation of Flash, and provide a good balance of wear and then prolong the life of the Flash. The Algorithm of the garbage collection significantly improved the system performance. Keywords-component; mark-sweep-copy;Flash; Object allocation; wear balance; Java Card

Some programming languages and computer systems use dynamic memory allocation with garbage collection. It would be useful to understand how the utilization of memory depends on the stochastic parameters describing the size and life distributions of the cells. We consider a class of dynamic storage allocation systems which use a first-fit strategy to allocate cells and perform noncompacting garbage collections to recover free memory space when memory becomes fully occupied. A formula is derived for the expected number of holes (available cells) in memory immediately following a garbage collection which specializes to an analogue of Knuth's 'Fifty Percent' rule for nongarbage-collection systems. Simulations confirm the rule for exponentially distributed cell lifetimes. Other lifetime distributions are discussed. The memory-size requirements for noncompacting garbage collection are also analyzed.