302118
A Hierarchical Model of Reference Affinity
To improve performance, data reorganization needs locality models to identify groups of data that have reference affinity. Much past work is based on access frequency and does not consider accessing time directly. In this paper, we propose a new model of reference affinity. This model considers the distance between data accesses in addition to the frequency. Affinity groups defined by this model are consistent and have a hierarchical structure. The former property ensures the profitability of data packing, while the latter supports data packing for storage units of different sizes. We then present a statistical clustering method that identifies affinity groups among structure fields and data arrays by analyzing training runs of a program. When used by structure splitting and array regrouping, the new method improves the performance of two test programs by up to 31%. The new data layout is significantly better than that produced by the programmer or by static compiler analysis.
6253791
Array regrouping and structure splitting using whole-program reference affinity
While the memory of most machines is organized as a hierarchy, program data are laid out in a uniform address space. This paper defines a model of reference affinity, which measures how close a group of data are accessed together in a reference trace. It proves that the model gives a hierarchical partition of program data. At the top is the set of all data with the weakest affinity. At the bottom is each data element with the strongest affinity. Based on the theoretical model, the paper presents k-distance analysis, a practical test for the hierarchical affinity of source-level data. When used for array regrouping and structure splitting, k-distance analysis consistently outperforms data organizations given by the programmer, compiler analysis, frequency profiling, statistical clustering, and all other methods we have tried.
9685849
Adaptive data partition for sorting using probability distribution
Many computing problems benefit from dynamic partition of data into smaller chunks with better parallelism and locality. However, it is difficult to partition all types of inputs with the same high efficiency. This paper presents a new partition method in sorting scenario based on probability distribution, an idea first studied by Janus and Lamagna in early 1980's on a mainframe computer. The new technique makes three improvements. The first is a rigorous sampling technique that ensures accurate estimate of the probability distribution. The second is an efficient implementation on modern, cache-based machines. The last is the use of probability distribution in parallel sorting. Experiments show 10-30% improvement in partition balance and 20-70% reduction in partition overhead, compared to two commonly used techniques. The new method reduces the parallel sorting time by 33-50% and outperforms the previous fastest sequential sorting technique by up to 30%.
16080938
Locality phase prediction
As computer memory hierarchy becomes adaptive, its performance increasingly depends on forecasting the dynamic program locality. This paper presents a method that predicts the locality phases of a program by a combination of locality profiling and run-time prediction. By profiling a training input, it identifies locality phases by sifting through all accesses to all data elements using variable-distance sampling, wavelet filtering, and optimal phase partitioning. It then constructs a phase hierarchy through grammar compression. Finally, it inserts phase markers into the program using binary rewriting. When the instrumented program runs, it uses the first few executions of a phase to predict all its later executions.Compared with existing methods based on program code and execution intervals, locality phase prediction is unique because it uses locality profiles, and it marks phase boundaries in program code. The second half of the paper presents a comprehensive evaluation. It measures the accuracy and the coverage of the new technique and compares it with best known run-time methods. It measures its benefit in adaptive cache resizing and memory remapping. Finally, it compares the automatic analysis with manual phase marking. The results show that locality phase prediction is well suited for identifying large, recurring phases in complex programs.
31888135
An automatic seal imprint verification approach
The automatic verification of seal imprints is a difficult pattern recognition problem and has a lot of applications in the oriental countries. Several approaches have been proposed in the past 10 years, but all of them were hard to implement in practice because of their drawbacks in the adaptability, reliability and practicality. In this paper, a new automatic seal imprint verification approach is presented. Due to the efficient and accurate registration method, a set of effective features and the elaborate implementations, this approach has satisfied most practical requirements and has been used for a practical check verification system in a Chinese bank.
40278707
Modeling and Analysis of WAP Performance over Wireless Links
In this paper, an analytical model for studying the performance behavior of wireless application protocol (WAP) over wireless links is proposed. A Rayleigh fading channel model is used to characterize the behavior of the wireless channel. Mathematical expressions that represent the performance of WAP as a function of the protocol and the channel parameters are derived. Computer simulation results are presented to validate the analytical results. It is shown that WAP performs better in a bursty error environment than in a random error environment. The goodput of WAP can be improved by increasing the WAP packet group size, but the significance of the improvement depends on the underlying channel condition.
6579321
Discovering interesting patterns through user's interactive feedback
In this paper, we study the problem of discovering interesting patterns through user's interactive feedback. We assume a set of candidate patterns (ie, frequent patterns) has already been mined. Our goal is to help a particular user effectively discover interesting patterns according to his specific interest. Without requiring a user to explicitly construct a prior knowledge to measure the interestingness of patterns, we learn the user's prior knowledge from his interactive feedback. We propose two models to represent a user's prior: the log linear model and biased belief model. The former is designed for item-set patterns, whereas the latter is also applicable to sequential and structural patterns. To learn these models, we present a two-stage approach, progressive shrinking and clustering, to select sample patterns for feedback. The experimental results on real and synthetic data sets demonstrate the effectiveness of our approach.
30859719
Rearrangeable Graphs
44324303
Equivalent permutation capabilities between time division optical omega network and non-optical extra stage omega network
Since optical networks must support crosstalk-free (CF) connections, regular N/spl times/N multistage interconnection networks (MINs) cannot be directly used to provide connections between N inputs and N outputs. A solution is to use a 2N/spl times/2N Cube-Type MIN to provide the N/spl times/N connections. C. Qiao (1993) proposed a time domain approach that divides the N optical inputs into several groups such that CF connections can be provided by an N/spl times/N regular MIN in several time slots, one for each group. The set of N-permutations realizable in two slots is defined as class /spl theta/. Qiao proved that the size of /spl theta/ is larger than the size of class /spl Omega/, where /spl Omega/ consists of all N-permutations passible through a regular N/spl times/N Omega network. The paper first presents an optimal O(NlgN) time algorithm for identifying whether a given permutation belongs to class /spl theta/ or not. From this algorithm, the paper proves an interesting fact that the class /spl theta/ is identical to the class /spl Omega/+1 which represents the set of N-permutations admissible by a non-optical N/spl times/N one-extra stage Omega network.