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.
12198410
Scalable hardware-algorithm for mark-sweep garbage collection
The memory-intensive nature of object-oriented languages such as C++ and Java has created the need for high-performance dynamic memory management. Object-oriented applications often generate higher memory intensity in the heap region. Thus, a high-performance memory manager is needed to cope with such applications. As today's VLSI technology advances, it becomes increasingly attractive to map software algorithms such as malloc(), free() and garbage collection into hardware. This paper presents a hardware design of a sweeping function (for mark-and-sweep garbage collection) that fully utilizes the advantages of combinational logic. In our scheme, the bit sweep can detect and sweep the garbage in a constant time. Bit-map marking in software can improve the cache performance and reduce number of page faults; however, it often requires several instructions to perform a single mark. In our scheme, only one hardware instruction is required per mark. Moreover, since the complexity of the sweeping phase is often higher than the marking phase, the garbage collection time may be substantially improved. The hardware complexity of the proposed scheme (bit-sweeper) is O(n), where n represents the size of the bit map.
18001825
Architectural support for dynamic memory management
Recent advances in software engineering, such as graphical user interfaces and object-oriented programming, have caused applications to become more memory intensive. These applications tend to allocate dynamic memory prolifically. Moreover, automatic dynamic memory reclamation (garbage collection, GC) has become a popular feature in modern programming languages. As a result, the time consumed by dynamic storage management can be up to one-third of the program execution time. This illustrates the need for a high-performance memory management scheme. This paper presents a top-level design and evaluation of the proposed instruction extensions to facilitate heap management.
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.
9645285
Using scratchpad to exploit object locality in Java
Performance of modern computers is tied closely to the effective use of cache because of the continually increasing speed discrepancy between processors and main memory. We demonstrate that generational garbage collection employed by a system with cache and scratchpad memory can take advantage of the locality of small short-lived objects in Java and reduce memory traffic by as much as 20% when compared to a cache-only configuration. Converting half of the cache to scratchpad can be more effective at reducing memory traffic than doubling or even quadrupling the size of the cache for several of the applications in SPECjvm98.
39527990
FPGA-based reconfigurable computing
2829100
GAPSCORE: finding gene and protein names one word at a time
MOTIVATION
New high-throughput technologies have accelerated the accumulation of knowledge about genes and proteins. However, much knowledge is still stored as written natural language text. Therefore, we have developed a new method, GAPSCORE, to identify gene and protein names in text. GAPSCORE scores words based on a statistical model of gene names that quantifies their appearance, morphology and context.


RESULTS
We evaluated GAPSCORE against the Yapex data set and achieved an F-score of 82.5% (83.3% recall, 81.5% precision) for partial matches and 57.6% (58.5% recall, 56.7% precision) for exact matches. Since the method is statistical, users can choose score cutoffs that adjust the performance according to their needs.


AVAILABILITY
GAPSCORE is available at http://bionlp.stanford.edu/gapscore/
7532286
Finding recent frequent itemsets adaptively over online 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, specially for an online data stream, can provide valuable information for the analysis of the data stream. In addition, monitoring the continuous variation of a data stream enables to find the gradual change of embedded knowledge. However, most of mining algorithms over a data stream do not differentiate the information of recently generated transactions from the obsolete information of old transactions which may be no longer useful or possibly invalid at present. This paper proposes a data mining method for finding recent frequent itemsets adaptively over an online data stream. The effect of old transactions on the mining result of the 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 main memory usage. Finally, the proposed method is analyzed by a series of experiments.
29352440
Induced matchings in asteroidal triple-free graphs
An induced matching M of a graph G is a set of pairwise nonadjacent edges such that no two edges of M are joined by an edge in G. The problem of finding a maximum induced matching is known to be NP-hard even for bipartite graphs of maximum degree four. In this paper, we study the maximum induced matching problem on classes of graphs related to AT-free graphs. We first define a wider class of graphs called the line-asteroidal triple-free (LAT-free) graphs which contains AT-free graphs as a subclass. By examining the square of line graph of LAT-free graphs, we give a characterization of them and apply this for showing that the maximum induced matching problem and a generalization, called the maximum δ-separated matching problem, on LAT-free graphs can be solved in polynomial time. In fact, our result can be extended to the classes of graphs with bounded asteroidal index. Next, we propose a linear-time algorithm for finding a maximum induced matching in a bipartite permutation (bipartite AT-free) graph using the greedy approach. Moreover, we show that using the same technique the minimum chain subgraph cover problem on bipartite permutation graphs can be solved with the same time complexity.
35558441
Using Interface Refinement to Integrate Formal Verification into the Design Cycle
We present a practical compositional interface refinement methodology which helps to integrate formal verification with the design process. One of the main verification challenges is to keep up with the changes to the specifications as the design evolves, and in particular, the transformations to the interfaces between the components. Interface changes are usually incremental, and therefore, the verification efforts after each change should also be incremental in order to be scalable. This paper presents a compositional interface refinement methodology which addresses all of these issues.
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.