Low latency services such as credit-card fraud detection and website targeted advertisement rely on Big Data platforms (e.g., Lucene, Graphchi, Cassandra) which run on top of memory managed runtimes, such as the JVM. These platforms, however, suffer from unpredictable and unacceptably high pause times due to inadequate memory management decisions (e.g., allocating objects with very different lifetimes next to each other, resulting in memory fragmentation). This leads to long and frequent application pause times, breaking Service Level Agreements (SLAs). This problem has been previously identified and results show that current memory management techniques are ill-suited for applications that hold in memory massive amounts of middle to long-lived objects (which is the case for a wide spectrum of Big Data applications). Previous works try to reduce such application pauses by allocating objects off-heap or in special allocation regions generations, thus alleviating the pressure on memory management. However, all these solutions require a combination of programmer effort and knowledge, source code access, or off-line profiling, with clear negative impact on programmer productivity and or application performance. This paper presents ROLP, a runtime object lifetime profiling system. ROLP profiles application code at runtime in order to identify which allocation contexts create objects with middle to long lifetimes, given that such objects need to be handled differently (regarding short-lived ones). This profiling information greatly improves memory management decisions, leading to long tail latencies reduction of up to 51 for Lucene, 85 for GraphChi, and 60 for Cassandra, with negligible throughput and memory overhead. ROLP is implemented for the OpenJDK 8 HotSpot JVM and it does not require any programmer effort or source code access.
@cite_2 extends the profile-based pretenuring of Cheng's solution @cite_32 using the Merlin tracing tool @cite_25 . They have a two stage profiling. The first stage happens during the build of the JVM @. Profiling at this stage is used to improve the performance of JVM itself, since Jikes RVM @cite_37 is a meta-circular JVM. reports this is particularly useful for tight heaps (i.e. heaps that are just above the minimum size for a given application, reaching at most 150MB) and not suitable for heaps with Gigabytes of objects. The second stage is an application-specific process, based on the off-line profiling made with Merlin @cite_25 .
@cite_37 @cite_25 @cite_32 @cite_2
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Pretenuring can reduce copying costs in garbage collectors by allocating long-lived objects into regions that the garbage collector with rarely, if ever, collect. We extend previous work on pretenuring as follows. (1) We produce pretenuring advice that is neutral with respect to the garbage collector algorithm and configuration. We thus can and do combine advice from different applications. We find that predictions using object lifetimes at each allocation site in Java prgroams are accurate, which simplifies the pretenuring implementation. (2) We gather and apply advice to applications and the Jalapeno JVM, a compiler and run-time system for Java written in Java. Our results demonstrate that building combined advice into Jalapeno from different application executions improves performance regardless of the application Jalapeno is compiling and executing. This build-time advice thus gives user applications some benefits of pretenuring without any application profiling. No previous work pretenures in the run-time system. (3) We find that application-only advice also improves performance, but that the combination of build-time and application-specific advice is almost always noticeably better. (4) Our same advice improves the performance of generational and Older First colleciton, illustrating that it is collector neutral. Programmers are writing a rapidly growing number of programs in object-oriented languages, such as Java and C#, that require garbage collection. Garbage collection traces and simulation speed up research by enabling deeper understandings of object lifetime behavior and quick exploration and design of new garbage collection algorithms. When generating perfect traces, the brute-force method of computing object lifetimes requires a whole-heap garbage collection at every potential collection point in the program. Because this process is prohibitively expensive, researchers often use granulated traces by collecting only periodically, for example, every 32 KB of allocation.We extend the state of the art for simulating garbage collection algorithms in two ways. First, we develop a systematic methodology for simulation studies of copying garbage collection and present results showing the effects of trace granularity on these simulations. We show that trace granularity often distorts simulated garbage collection results compared with perfect traces. Second, we present and measure the performance of a new algorithm called Merlin for computing object lifetimes. Merlin timestamps objects and later uses the timestamps of dead objects to reconstruct when they died. The Merlin algorithm piggybacks on garbage collections performed by the base system. Experimental results show that Merlin can generate traces over two orders of magnitude faster than the brute-force method which collects after every object allocation. We also use Merlin to produce visualizations of heap behavior that expose new object lifetime behaviors. A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles. While the concept of online profile directed dynamic optimizations using hardware performance monitoring unit (PMU) data is not new, it has seen fairly limited or no use in commercial JVMs. The main reason behind this fact is the set of significant challenges involved in (1) obtaining low overhead and usable profiling support from the underlying platform (2) the complexity of filtering, interpreting and using precise PMU events online in a JVM environment (3) demonstrating the total runtime benefit of PMU data based optimizations above and beyond regular online profile based optimizations. In this paper we address all three challenges by presenting a practical framework for PMU data collection and use within a high performance product JVM on a highly scalable server platform. Our experiments with JavaTM workloads using the SunTM HotspotTM JDK 1.6 JVM on the Intel ® Itanium ® platform indicate that the hardware data collection overhead (less than 0.5 ) is not as significant as the challenge of extracting the precise information for optimization purposes. We demonstrate the feasibility of mapping the instruction IP address based hardware event information to the runtime components as well as the JIT server compiler internal data structures for use in optimizations within a dynamic environment. We also evaluated the additional performance potential of optimizations such as object co-location during garbage collection and global instruction scheduling in the JIT compiler with the use of PMU generated load latency information. Experimental results show performance improvements of up to 14 with an average of 2.2 across select Java server benchmarks such as SPECjbb2005[16], SPECjvm2008[17] and Dacapo[18]. These benefits were observed over and above those provided by profile guided server JVM optimizations in the absence of hardware PMU data. We introduce Elephant Tracks (ET), a dynamic program analysis tool for producing traces of garbage collection events, including object allocations, object deaths, and pointer updates. Like prior work, our tracing tool is based on the Merlin algorithm [2002], but offers several substantial new capabilities. First, it is much more precise than previous tools: it measures time in terms of method entries and exits, allowing it to place events precisely in the context of the program structure. Furthermore, we exploit this notion of time to actually reduce the amount of work required to compute precise object death times. Second, it is implemented using a combination of JVM Tool Interface (JVMTI) Microsystems [2004] callbacks and bytecode rewriting, and works with any standard JVM. Finally, it properly handles Java Reference objects, including weak references, finalization, and multithreaded programs. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Just-in-time (JIT) compilation of running programs provides more optimization opportunities than offline compilation. Modern JIT compilers, such as those in virtual machines like Oracle's HotSpot for Java or Google's V8 for JavaScript, rely on dynamic profiling as their key mechanism to guide optimizations. While these JIT compilers offer good average performance, their behavior is a black box and the achieved performance is highly unpredictable. In this paper, we propose to turn JIT compilation into a precision tool by adding two essential and generic metaprogramming facilities: First, allow programs to invoke JIT compilation explicitly. This enables controlled specialization of arbitrary code at run-time, in the style of partial evaluation. It also enables the JIT compiler to report warnings and errors to the program when it is unable to compile a code path in the demanded way. Second, allow the JIT compiler to call back into the program to perform compile-time computation. This lets the program itself define the translation strategy for certain constructs on the fly and gives rise to a powerful JIT macro facility that enables "smart" libraries to supply domain-specific compiler optimizations or safety checks. We present Lancet, a JIT compiler framework for Java bytecode that enables such a tight, two-way integration with the running program. Lancet itself was derived from a high-level Java bytecode interpreter: staging the interpreter using LMS (Lightweight Modular Staging) produced a simple bytecode compiler. Adding abstract interpretation turned the simple compiler into an optimizing compiler. This fact provides compelling evidence for the scalability of the staged-interpreter approach to compiler construction. In the case of Lancet, JIT macros also provide a natural interface to existing LMS-based toolchains such as the Delite parallelism and DSL framework, which can now serve as accelerator macros for arbitrary JVM bytecode. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . This paper presents two techniques for improving garbage collection performance: generational stack collection and profile-driven pretenuring. The first is applicable to stack-based implementations of functional languages while the second is useful for any generational collector. We have implemented both techniques in a generational collector used by the TIL compiler (Tarditi, Morrisett, Cheng, Stone, Harper, and Lee 1996), and have observed decreases in garbage collection times of as much as 70 and 30 , respectively.Functional languages encourage the use of recursion which can lead to a long chain of activation records. When a collection occurs, these activation records must be scanned for roots. We show that scanning many activation records can take so long as to become the dominant cost of garbage collection. However, most deep stacks unwind very infrequently, so most of the root information obtained from the stack remains unchanged across successive garbage collections. Generational stack collection greatly reduces the stack scan cost by reusing information from previous scans.Generational techniques have been successful in reducing the cost of garbage collection (Ungar 1984). Various complex heap arrangements and tenuring policies have been proposed to increase the effectiveness of generational techniques by reducing the cost and frequency of scanning and copying. In contrast, we show that by using profile information to make lifetime predictions, pretenuring can avoid copying data altogether. In essence, this technique uses a refinement of the generational hypothesis (most data die young) with a locality principle concerning the age of data: most allocations sites produce data that immediately dies, while a few allocation sites consistently produce data that survives many collections. 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. Modern object-oriented applications commonly suffer from severe performance problems that need to be optimized away for increased efficiency and user satisfaction. Many existing optimization techniques (such as object pooling and pretenuring) require precise identification of object lifetimes. However, it is particularly challenging to obtain object lifetimes both precisely and efficiently: precise profiling techniques such as Merlin introduce several hundred times slowdown even for small programs while efficient approximation techniques often sacrifice precision and produce less useful lifetime information. This paper presents a tunable profiling technique, called Resurrector, that explores the middle ground between high precision and high efficiency to find the precision-efficiency sweetspot for various livenessbased optimization techniques. Our evaluation shows that Resurrector is both more precise and more efficient than the GC-based approximation, and it is orders-of-magnitude faster than Merlin. To demonstrate Resurrector's usefulness, we have developed client analyses to find allocation sites that create large data structures with disjoint lifetimes. By inspecting program source code and reusing data structures created from these allocation sites, we have achieved significant performance gains. We have also improved the precision of an existing optimization technique using the lifetime information collected by Resurrector. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... Markov chains with Labelled Transitions can be used to generate test cases in a model-based approach. These test cases are generated by random walks on the model according to probabilities associated with transitions. When these probabilities correspond to a usage profile, reliability may be estimated. However, in early stages of development, such probabilities are not easy to determine, thus default profiles must be considered. In such a case it may be interesting to target some coverage criteria rather to use classical uniform probability generation approach. In this paper we enrich an existing industrial tool based on usage profile with 3 possibilities to create default profiles that improve transition coverage. We report experiments that compare the improvement of the coverage rates by our approaches with respect to uniform probabilities on transitions from a given state, which is the current default profile. Web browsers have become a de facto universal operating system, and JavaScript its instruction set. Unfortunately, running other languages in the browser is not generally possible. Translation to JavaScript is not enough because browsers are a hostile environment for other languages. Previous approaches are either non-portable or require extensive modifications for programs to work in a browser. This paper presents Doppio, a JavaScript-based runtime system that makes it possible to run unaltered applications written in general-purpose languages directly inside the browser. Doppio provides a wide range of runtime services, including a file system that enables local and external (cloud-based) storage, an unmanaged heap, sockets, blocking I O, and multiple threads. We demonstrate DOPPIO's usefulness with two case studies: we extend Emscripten with Doppio, letting it run an unmodified C++ application in the browser with full functionality, and present DoppioJVM, an interpreter that runs unmodified JVM programs directly in the browser. While substantially slower than a native JVM (between 24X and 42X slower on CPU-intensive benchmarks in Google Chrome), DoppioJVM makes it feasible to directly reuse existing, non compute-intensive code. Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance. Historically, papers have been physically bound to the journal in which they were published; but in the digital age papers are available individually, no longer tied to their respective journals. Hence, papers now can be read and cited based on their own merits, independently of the journal's physical availability, reputation, or impact factor (IF). We compare the strength of the relationship between journals' IFs and the actual citations received by their respective papers from 1902 to 2009. Throughout most of the 20th century, papers' citation rates were increasingly linked to their respective journals' IFs. However, since 1990, the advent of the digital age, the relation between IFs and paper citations has been weakening. This began first in physics, a field that was quick to make the transition into the electronic domain. Furthermore, since 1990 the overall proportion of highly cited papers coming from highly cited journals has been decreasing and, of these highly cited papers, the proportion not coming from highly cited journals has been increasing. Should this pattern continue, it might bring an end to the use of the IF as a way to evaluate the quality of journals, papers, and researchers. © 2012 Wiley Periodicals, Inc. In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps. In this paper we describe experiments conducted for CLEF- IP 2011 Prior Art Retrieval track. We examined the impact of 1) us- ing key phrase extraction to generate queries from input patent and 2) the use of citation network and (International Patent Classification) IPC class vector in ranking patents. Variations of a popular key phrase extrac- tion technique were explored for extracting and scoring terms of query patent. These terms are used as queries to retrieve similar patents. In the second approach, we use a two stage retrieval model to find similar patents. Each patent is represented as an IPC class vector. Citation net- work of patents is used to propagate these vectors from a node (patent) to its neighbors (cited patents). Similar patents are found by comparing query vector with vectors of patents in the corpus. Text based search is used to re-rank this solution set to improve precision. Two-stage sys- tem is used to retrieve and rank patents. Finally, we also extract and add citations present within the text of a query patent to the result set. Adding these citations (present in query patent text) to the results shows significant improvement in Mean Average Precision (MAP).