6096323
Quantifying instruction criticality for shared memory multiprocessors
Recent research on processor microarchitecture suggests using instruction criticality as a metric to guide hardware control policies. Fields et al. [3, 4] have proposed a directed acyclic graph (DAG) model for characterizing program microexecutions on uniprocessors. Under such a model, critical path analysis can be applied and instructions' slack values can be used to quantify instruction criticality. In this paper, we extend the uniprocessor DAG model to characterize parallel program executions on shared memory multiprocessor systems. We describe how critical path analysis can be applied, at a fine grain, in a multiprocessor system running both finite and continuous workloads. We provide detailed evaluations for various aspects of multiprocessor executions under the DAG model. To enable efficient offline critical path analysis, we propose a novel graph reduction technique that reduces a DAG to an equivalent but significantly smaller DAG.
10443056
A large, fast instruction window for tolerating cache misses
Instruction window size is an important design parameter for many modern processors. Large instruction windows offer the potential advantage of exposing large amounts of instruction level parallelism. Unfortunately naively scaling conventional window designs can significantly degrade clock cycle time, undermining the benefits of increased parallelism.This paper presents a new instruction window design targeted at achieving the latency tolerance of large windows with the clock cycle time of small windows. The key observation is that instructions dependent on a long latency operation (e.g., cache miss) cannot execute until that source operation completes. These instructions are moved out of the conventional, small, issue queue to a much larger waiting instruction buffer (WIB). When the long latency operation completes, the instructions are reinserted into the issue queue. In this paper, we focus specifically on load cache misses and their dependent instructions. Simulations reveal that, for an 8-way processor, a 2K-entry WIB with a 32-entry issue queue can achieve speedups of 20%, 84%, and 50% over a conventional 32-entry issue queue for a subset of the SPEC CINT2000, SPEC CFP2000, and Olden benchmarks, respectively.
2949246
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain "identifying" attributes) contains at least k records. Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure. The notion of l-diversity has been proposed to address this; l-diversity requires that each equivalence class has at least l well-represented values for each sensitive attribute. In this paper we show that l-diversity has a number of limitations. In particular, it is neither necessary nor sufficient to prevent attribute disclosure. We propose a novel privacy notion called t-closeness, which requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t). We choose to use the earth mover distance measure for our t-closeness requirement. We discuss the rationale for t-closeness and illustrate its advantages through examples and experiments.
14562465
Bioinformatics on Embedded Systems: A Case Study of Computational Biology Applications on VLIW Architecture
Bioinformatics applications represent the increasingly important workloads. Their characteristics and implications on the underlying hardware design, however, are largely unknown. Currently, biological data processing ubiquitously relies on the high-end systems equipped with expensive, general-purpose processors. The future generation of bioinformatics requires the more flexible and cost-effective computing platforms to meet its rapidly growing market. The programmable, application-specific embedded systems appear to be an attractive solution in terms of easy of programming, design cost, power, portability and time-to-market. The first step towards such systems is to characterize bioinformatics applications on the target architecture. Such studies can help in understanding the design issues and the trade-offs in specializing hardware and software systems to meet the needs of bioinformatics market. This paper evaluates several representative bioinformatics tools on the VLIW based embedded systems. We investigate the basic characteristics of the benchmarks, impact of function units, the efficiency of VLIW execution, cache behavior and the impact of compiler optimizations. The architectural implications observed from this study can be applied to the design optimizations. To the best of our knowledge, this is one of the first such studies that have ever been attempted.
10257142
Regularized clustering for documents
In recent years, document clustering has been receiving more and more attentions as an important and fundamental technique for unsupervised document organization, automatictopic extraction, and fast information retrieval or filtering. In this paper, we propose a novel method for clustering documents using regularization. Unlike traditional globally regularized clustering methods, our method first construct a local regularized linear label predictor for each document vector, and then combine all those local regularizers with a global smoothness regularizer. So we call our algorithm Clustering with Local and Global Regularization (CLGR). We will show that the cluster memberships of the documents can be achieved by eigenvalue decomposition of a sparse symmetric matrix, which can be efficiently solved by iterative methods. Finally our experimental evaluations on several datasets are presented to show the superiorities of CLGR over traditional document clustering methods.
52059378
A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression
This paper studies the problem of building multiclass classifiers for tissue classification based on gene expression. The recent development of microarray technologies has enabled biologists to quantify gene expression of tens of thousands of genes in a single experiment. Biologists have begun collecting gene expression for a large number of samples. One of the urgent issues in the use of microarray data is to develop methods for characterizing samples based on their gene expression. The most basic step in the research direction is binary sample classification, which has been studied extensively over the past few years. This paper investigates the next step-multiclass classification of samples based on gene expression. The characteristics of expression data (e.g. large number of genes with small sample size) makes the classification problem more challenging. The process of building multiclass classifiers is divided into two components: (i) selection of the features (i.e. genes) to be used for training and testing and (ii) selection of the classification method. This paper compares various feature selection methods as well as various state-of-the-art classification methods on various multiclass gene expression datasets. Our study indicates that multiclass classification problem is much more difficult than the binary one for the gene expression datasets. The difficulty lies in the fact that the data are of high dimensionality and that the sample size is small. The classification accuracy appears to degrade very rapidly as the number of classes increases. In particular, the accuracy was very low regardless of the choices of the methods for large-class datasets (e.g. NCI60 and GCM). While increasing the number of samples is a plausible solution to the problem of accuracy degradation, it is important to develop algorithms that are able to analyze effectively multiple-class expression data for these special datasets.
17768597
A New Model for Dynamic Intrusion Detection
Building on the concepts and the formal definitions of self, nonself, antigen, and detector introduced in the research of network intrusion detection, the dynamic evolution models and the corresponding recursive equations of self, antigen, immune-tolerance, lifecycle of mature detectors, and immune memory are presented. Following that, an immune-based model, referred to as AIBM, for dynamic intrusion detection is developed. Simulation results show that the proposed model has several desirable features including self-learning, self-adaption and diversity, thus providing a effective solution for network intrusion detection.