16112792
The object behavior of Java object-oriented database management systems
Due to its portability and popularity for Internet applications, Java has become one of the major programming languages. The similar syntax inherited from the C language and the pure object oriented features, compared to the non-pure object-oriented C++ language, have caused Java to be a good candidate as a tool in designing object oriented systems, especially in database servers. However the performance of the Java Virtual Machine (JVM) is the reason that several database designers, such as Oracle, IBM, Sybase and Informix, have not fully transferred their database management system (DBMS) into Java. One of the performance indices is the responsiveness for online transaction processing (OLTP) which may be dominated by the garbage collection system inside the JVM. In this paper eight Java programs, jess, javac, mtrt, compress, db, db4o, smallDB and ozone, are used to study the dynamic memory behavior. The latter four programs are Java object-oriented database management systems and their object behaviors are compared to those of the four regular Java programs. Simulation results such as object size distribution, average object size, object live distribution and total garbage collection cycle are reported.
46189070
Optimal use of timing resources: a crucial step in test program generation
A modified timing set priority strategy and a lookahead register-replacement algorithm is presented for automatic test program generation. These methodologies are introduced to satisfy the testing requirements in complex VLSI chips, and are made possible by the increasing timing flexibility of modern ATE (automatic test equipment). It is shown that by applying optimization algorithms in allocating timing resources and a published timing set determination algorithm, that testing capability of modern ATE can be greatly extended. Further enhancements to these approaches are contingent on further improvements from ATE manufacturers in the number and flexibility of timing resources. However, the development of test programs will be greatly aided by this enhanced hardware.<<ETX>>
27355217
Java Bytecode Optimization with Advanced Instruction Folding Mechanism
The execution performance of Java has been a problem since it was introduced world wide. As one of the solutions, a bytecode instruction folding process for Java processors was developed in a PicoJava model and a Producer, Operator and Consumer (POC) model. Although the instruction folding process in these models saved extra stack operations, it could not handle certain types of instruction sequences. In this paper, a new instruction folding scheme based on a new, advanced POC model is proposed and demonstrates improvement in byte-code execution. The proposed POC model is able to detect and fold all possible instruction sequence types, including a sequence that is separated by other bytecode instructions. SPEC JMV98 benchmark results show that the proposed POC model-based folder can save more than 90% of folding operations. In addition, a design of the proposed POC model-based folding process in hardware is much smaller and more efficient than traditional folding mechanisms. In this research, the proposed instruction folding technique can eliminate most of the stack operations and the use of a physical operand stack, and can thereby achieve the performance of high-end RISC processors.
12416012
An efficient data structure for dynamic memory management
Abstract Advances in software engineering, such as graphical user interface and object-oriented programming, have caused applications to become more memory intensive. The growing popularity of these applications increases the importance of high-performance dynamic memory management. This paper introduces a new data structure intended to increase the efficiency of implementations of common dynamic memory allocation and deallocation algorithms, including First Fit . The data structure, called the Common List , establishes a relationship between the allocated and free list. This strategy increases significantly the deallocation performance. Simulation results based on the traces from publicly available software applications are presented.
1021056
A performance perspective on the Active Memory System
Abstract One major problem of using Java in real-time and embedded devices is the non-deterministic turnaround time of dynamic memory management systems (memory allocation and garbage collection). For the allocation, the non-determinism is often contributed by the time to perform searching, splitting, and coalescing. For the garbage collection, the turnaround time is usually determined by the size of the heap, the number of live objects, the number of object collected, and the amount of garbage collected. Even with the current state-of-the-art garbage collectors (generational and incremental schemes), they may or may not guarantee the worst-case latency. Moreover, such schemes often prolong overall garbage collection time. In this paper, the performance analysis of the proposed Active Memory Module (AMM) for embedded systems is presented. Unlike the software counterparts, the AMM can perform a memory allocation in a predictable and bounded fashion (14 cycles). Moreover, it can also yield a bounded sweeping time regardless of the number of live objects or heap size. By utilizing the proposed system, the overall speedup can be as high as 23% compared to the garbage collection system of the JDK 1.2.2 running in classic mode.
8404104
A study of best-fit memory allocators
Dynamic memory allocation often makes up a large part of program execution time. Different variants of the best-fit allocator are implemented and their space and time costs measured and compared. We found variants of this algorithm that are 3-33% faster than the Doug Lea 2.7.0 allocator.
37599184
Finding frequent itemsets over online data streams
Abstract Conventional data mining methods for finding frequent itemsets require considerable computing time to produce their results from a large data set. Due to this reason, it is almost impossible to apply them to an analysis task in an online data stream where a new transaction is continuously generated at a rapid rate. An algorithm for finding frequent itemsets over an online data stream should support flexible trade-off between processing time and mining accuracy. Furthermore, the most up-to-date resulting set of frequent itemsets should be available quickly at any moment. To satisfy these requirements, this paper proposes a data mining method for finding frequent itemsets over an online data stream. The proposed method examines each transaction one-by-one without any candidate generation process. The count of an itemset that appears in each transaction is monitored by a lexicographic tree resided in main memory. The current set of monitored itemsets in an online data stream is minimized by two major operations: delayed-insertion and pruning . The former is delaying the insertion of a new itemset in recent transactions until the itemset becomes significant enough to be monitored. The latter is pruning a monitored itemset when the itemset turns out to be insignificant. The number of monitored itemsets can be flexibly controlled by the thresholds of these two operations. As the number of monitored itemsets is decreased, frequent itemsets in the online data stream are more rapidly traced while they are less accurate. The performance of the proposed method is analyzed through a series of experiments in order to identify its various characteristics.
14086063
A modular eigen subspace scheme for high-dimensional data classification
In this paper, a novel filter-based greedy modular subspace (GMS) technique is proposed to improve the accuracy of high-dimensional data classification. The proposed approach initially divides the whole set of high-dimensional features into several arbitrary number of highly correlated subgroups by performing a greedy correlation matrix reordering transformation for each class. These GMS can be treated as not only a preprocess of GMS filter-based classifiers but also a unique feature extractor to generate a particular feature subspaces for each different class presented in high-dimensional data. The similarity measures are next calculated by projecting the samples into different modular feature subspaces. Finally, a GMS filter-based architecture based on the mean absolute errors criterion is adopted to build a non-linear multi-class classifier. The proposed GMS filter-based classification scheme is developed to find non-linear boundaries of different classes for high-dimensional data. It not only significantly improves the classification accuracy but also dramatically reduces the computational complexity of feature extraction compared with the conventional principal components analysis. Experimental results demonstrate that the proposed GMS feature extraction method suits the GMS filter-based classifier best as a classification preprocess. It significantly improves the precision of high-dimensional data classification.
9345376
Finding recently frequent itemsets adaptively over online transactional data streams,
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is more likely to be changed as time goes by. Identifying the recent change of a data stream, especially for an online data stream, can provide valuable information for the analysis of the data stream. However, most of mining algorithms or frequency approximation algorithms over a data stream do not differentiate the information of recently generated data elements from the obsolete information of old data elements which may be no longer useful or possibly invalid at present. Therefore, they are not able to extract the recent change of information in a data stream adaptively. This paper proposes a data mining method for finding recently frequent itemsets adaptively over an online transactional data stream. The effect of old transactions on the current mining result of a data steam is diminished by decaying the old occurrences of each itemset as time goes by. Furthermore, several optimization techniques are devised to minimize processing time as well as memory usage. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.
27312959
Sorting a sequence of strong kings in a tournament
A king in a tournament is a player who beats any other player directly or indirectly. According to the existence of a king in every tournament, Wu and Sheng [Inform. Process. Lett. 79 (2001) 297-299] recently presented an algorithm for finding a sorted sequence of kings in a tournament of size n, i.e., a sequence of players u1, u2,....,un such that ui → ui+1 (ui beats ui+1) and ui is a king in the sub-tournament induced by {ui, ui+1,.....,un} for each i = 1, 2,....,n - 1. With each pair u,v of players in a tournament, let b(u,v) denote the number of third players used for u to beat v indirectly. Then, a king u is called a strong king if the following condition is fulfilled: if v → u then b(u, v) > b(v,u). In the sequel, we will show that the algorithm proposed by Wu and Sheng indeed generates a sorted sequence of strong kings, which is more restricted than the previous one.
17166482
Benchmarking the effectiveness of sequential pattern mining methods
Recently, there is an increasing interest in new intelligent mining methods to find more meaningful and compact results. In intelligent data mining research, accessing the quality and usefulness of the results from different mining methods is essential. However, there is no general benchmarking criteria to evaluate whether these new methods are indeed more effective compared to the traditional methods. Here we propose a novel benchmarking criteria that can systematically evaluate the effectiveness of any sequential pattern mining method under a variety of situations. The benchmark evaluates how well a mining method finds known common patterns in synthetic data. Such an evaluation provides a comprehensive understanding of the resulting patterns generated from any mining method empirically. In this paper, the criteria are applied to conduct a detailed comparison study of the support-based sequential pattern model with an approximate pattern model based on sequence alignment. The study suggests that the alignment model will give a good summary of the sequential data in the form of a set of common patterns in the data. In contrast, the support model generates massive amounts of frequent patterns with much redundancy. This suggests that the results of the support model require more post processing before it can be of actual use in real applications.