7211665
Dynamic data dependence tracking and its application to branch prediction
To continue to improve processor performance, microarchitects seek to increase the effective instruction level parallelism (ILP) that can be exploited in applications. A fundamental limit to improving ILP is data dependences among instructions. If data dependence information is available at run-time, there are many uses to improve ILP. Prior published examples include decoupled branch execution architectures and critical instruction detection. In this paper, we describe an efficient hardware mechanism to dynamically track the data dependence chains of the instructions in the pipeline. This information is available on a cycle-by-cycle basis to the microengine for optimizing its performance. We then use this design in a new value-based branch prediction design using available register value information (ARVI). From the use of data dependence information, the ARVI branch predictor has better prediction accuracy over a comparably sized hybrid branch predictor With ARVI used as the second-level branch predictor the improved prediction accuracy results in a 12.6% performance improvement on average across the SPEC95 integer benchmark suite.
14979419
QoS-aware routing based on bandwidth estimation for mobile ad hoc networks
Routing protocols for mobile ad hoc networks (MANETs) have been explored extensively in recent years. Much of this work is targeted at finding a feasible route from a source to a destination without considering current network traffic or application requirements. Therefore, the network may easily become overloaded with too much traffic and the application has no way to improve its performance under a given network traffic condition. While this may be acceptable for data transfer, many real-time applications require quality-of-service (QoS) support from the network. We believe that such QoS support can be achieved by either finding a route to satisfy the application requirements or offering network feedback to the application when the requirements cannot be met. We propose a QoS-aware routing protocol that incorporates an admission control scheme and a feedback scheme to meet the QoS requirements of real-time applications. The novel part of this QoS-aware routing protocol is the use of the approximate bandwidth estimation to react to network traffic. Our approach implements these schemes by using two bandwidth estimation methods to find the residual bandwidth available at each node to support new streams. We simulate our QoS-aware routing protocol for nodes running the IEEE 802.11 medium access control. Results of our experiments show that the packet delivery ratio increases greatly, and packet delay and energy dissipation decrease significantly, while the overall end-to-end throughput is not impacted, compared with routing protocols that do not provide QoS support.
15943403
Top-K Correlation Sub-graph Search in Graph Databases
Recently, due to its wide applications, (similar) subgraph search has attracted a lot of attentions from database and data mining community, such as [13,18,19,5]. In [8], Ke et al. first proposed correlation sub-graph search problem (CGSearch for short) to capture the underlying dependency between sub-graphs in a graph database, that is CGS algorithm. However, CGS algorithm requires the specification of a minimum correlation threshold *** to perform computation. In practice, it may not be trivial for users to provide an appropriate threshold *** , since different graph databases typically have different characteristics. Therefore, we propose an alternative mining task: top -K c orrelation sub- g raph search (TOP-CGSearh for short). The new problem itself does not require setting a correlation threshold, which leads the previous proposed CGS algorithm inefficient if we apply it directly to TOP-CGSearch problem. To conduct TOP-CGSearch efficiently, we develop a p attern- g rowth algorithm (that is PG-search algorithm) and utilize graph indexing methods to speed up the mining task. Extensive experiment results evaluate the efficiency of our methods.
2909075
Design of Multiple Classifier Systems for Time Series Data
In previous work, we showed that the use of Multiple Input Representation(MIR) for the classification of time series data provides complementary information that leads to better accuracy. [4]. In this paper, we introduce the Static Minimization-Maximization approach to build Multiple Classifier Systems(MCSs) using MIR. SMM consists of two steps. In the minimization step, a greedy algorithm is employed to iteratively select the classifiers from the knowledge space to minimize the training error of MCSs. In the maximization step, a modified version of Behavior Knowledge Space(BKS), Balanced Behavior Knowledge Space(BBKS), is used to maximize the expected accuracy of the whole system given that the training error is minimized. Several popular techniques including AdaBoost, Bagging and Random Subspace are used as the benchmark to evaluate the proposed approach on four time series data sets. The results obtained from our experiments show that the performance of the proposed approach is effective as well as robust for the classification of time series data. In addition, this approach could be further extended to other applications in our future research.
29666362
Algebraic Laws for BSP Programming
The Bulk-Synchronous Parallel (BSP) model of computation reflects the capabilities and costs of communication on a wide range of general-purpose parallel architectuers. Thus it allows general-purpose parallel software and hardware to be developed independently of one another; much as the von Neumann model provides the same facility for sequential computation. This paper presents a simple programming notation for shared-memory programming, based upon BSP constructs. The notation is defined formally by its effect upon process states. Algebraic laws are given which allow program derivation. A further set of laws allow transformation of finite programs to a normal form.
22828362
Construction of low-density parity-check codes by superposition
This paper presents a superposition method for constructing low-density parity-check (LDPC) codes. Several classes of structured LDPC codes are constructed. Codes in these classes perform well with iterative decoding, and their Tanner graphs have girth at least six.
27024485
A new design of multiple classifier system and its application to the classification of time series data
In this paper, we propose the scheme of multiple input representation-adaptive ensemble generation and aggregation(MIR-AEGA) for the classification of time series data. MIR-AEGA employs a set of heterogeneous classifiers, each of which takes a different representation of time series data as the input. MIR-AEGA adopts an "overfitting and selection" strategy. In the training phase, different ensembles of classifiers are adaptively generated by fitting the validation data ' globally in different degrees. The test data are then classified by each of the generated ensembles. The final decision is made by taking consideration into both the ability of each ensemble to fit the validation data locally and the possible overfitting effects. We claim that MIR-AEGA has two advantages (1) By using multiple representations, it exploits the temporal information of time series data as much as possible, thus could improve the overall performance (2) By tweaking the trade-off between the ability to fit the validation data and the overfitting effects, we expect the performance of this method is reliable in different situations. In this paper, the performance of MIR- AEGA is also assessed experimentally in comparison with other benchmark techniques. The experimental results demonstrate the good performance and the reliability of MIR-AEGA for the classification of time series data.