18341353
Alias verification for Fortran code optimization
Abstract Alias analysis for Fortran is less complicated than for programming languages with pointers but many real Fortran programs violate the standard: a formal parameter or a common variable that is aliased with another formal parameter is modified. Compilers, assuming standard-conforming programs, consider that an assignment to one variable will not change the value of any other variable, allowing optimizations involving the aliased variables. Higher performance results but anything may happen: the program may appear to run normally, or may produce incorrect answers, or may behave unpredictably. The results may depend on the compiler and the optimization level. To guarantee the standard conformance of programs and to maintain the referential transparency in order to make program analyses exact and program optimizations safe, precise alias information retrieval at a reasonable cost, especially the determination of overlaps between arrays are studied in this paper. Static analyses and code instrumentation are used to find all violations of the prohibitions against aliasing in Fortran code. Alias violation tests are inserted only at places where it cannot be proved statically that they are useless in order to reduce the number of dynamic checks at run-time. A specific memory location naming technique is used to compact representation and enhance the precision of alias analysis. Modifications on the dependence graph created by aliasing are also studied to show the impact of aliases on some program optimizing transformations. Experimental results on SPEC95 benchmark are presented and some related issues are also discussed.
15188179
Advanced Program Analyses and Verifications
Software validation and verification are critical for almost systems whose failure is unacceptable. This is a very expensive process, typically costing 50% of the total softwar e development costs. We show in this paper how program analyses can be used automatically to perform efficient and effective ver ifications, e.garray bound checking and uninitialized variable checking. These verifications are based onarray regions, the analyses that collect information about the way array elements used and de fined by programs. With useful knowledge of the program properties provided by these static analyses, the amount of additional code necess ary to instrument the application is reduced to a minimum. Run-time checks are generated only when we do not know how to prove that they are useless. Dynamic errors that may cause potentially failure are caught and their consequences can be reduced by exceptio n handling. Information provided by our analyses is helpful for the debugging process of large scale programs. All verification s are implemented in PIPS, the research compiler developed at́ Ecole des Mines de Paris. Qualitative and quantitative experimen ts have done with industrial applications up to hundreds of thousand lines of code.
9934244
Alias Verification for Fortran Code Optimization
Alias analysis for Fortran is less complicated than for programming languages with pointers but many real Fortran programs violate the standard: a formal parameter or a common variable that is aliased with another formal parameter is modified. Compilers, assuming standard-conforming programs, consider that an assignment to one variable will not change the value of any other variable, allowing optimizations involving the aliased variables. Higher performance results but anything may happen: the program may appear to run normally, or produce incorrect answers, or behave unpredictably. The results may depend on the compiler and the optimization level. To guarantee the standard conformance of programs in order to make program analyses exact and program optimizations safe, precise alias information, i.e the determination of overlaps among arrays is studied in this paper. Static analyses and code instrumentation are used to find all violations of the aliasing rules in Fortran code. Alias violation tests are inserted only at places where it cannot be proved statically that they are useless in order to reduce the number of dynamic checks at run-time. A specific memory location naming technique is used to obtain compact representation and to enhance the precision of alias analysis. Modifications on the dependence graph created by aliasing are also studied to show the impact of aliases on the correctness of some program optimizing transformations. Experimental results on SPEC95 CFP benchmark are presented and some related issues are also discussed.
37264651
Rough Set Approach to Sunspot Classification Problem
This paper presents an application of hierarchical learning method based rough set theory to the problem of sunspot classification from satellite images. The Modified Zurich classification scheme [3] is defined by a set of rules containing many complicated and unprecise concepts, which cannot be determined directly from solar images. The idea is to represent the domain knowledge by an ontology of concepts – a treelike structure that describes the relationship between the target concepts, intermediate concepts and attributes. We show that such ontology can be constructed by a decision tree algorithm and demonstrate the proposed method on the data set containing sunspot extracted from satellite images of solar disk.
35277097
Maximum-likelihood parameter estimation for image ringing-artifact removal
At low bit rates, image compression codecs based on overlapping transforms introduce spurious oscillations known as ringing artifacts in the vicinity of major edges. Unlike previous works, we present a maximum-likelihood approach to the ringing-artifact removal problem. Our approach employs a parameter estimation method based on the k-means algorithm with the number of clusters determined by a cluster-separation measure. The proposed algorithm and its simplified approximation are applied to JPEG2000 compressed images. Our results show effective and efficient removal of ringing artifacts.
15918536
Protocols for distributed video streaming
With the explosive growth of video applications over the packet switched networks, many approaches have been proposed to stream video effectively over packet switched, best-effort networks. In our previous work, we proposed a framework with a receiver driven protocol to coordinate simultaneous video streaming from multiple senders to a single receiver in order to achieve higher throughput, and to increase tolerance to packet loss and delay due to network congestion. The receiver-driven protocol employs two algorithms: the rate allocation and packet partition. The rate allocation algorithm determines the sending rate for each sender while the packet partition algorithm ensures no sender sends the same packets, and at the same time, minimizes the probability of late packets. We extend the rate allocation scheme to be used with forward error correction (FEC) in order to minimize the probability of packet loss in a bursty loss environment such as one due to network congestion. Using both simulations and actual Internet experiments, we demonstrate the effectiveness of our rate allocation scheme in reducing packet loss, and hence, achieving higher visual quality for the streamed video.
11160591
Learning Sunspot Classification
This paper describes automatic sunspot recognition and classification from satellite images. Some experimental results on learning sunspot classification using data mining techniques are presented. The classification scheme used was the seven-class Modified Zurich scheme. Daily images of the solar disk were taken from the NASA SOHO satellite’s MDI instrument and sunspots extracted using image processing techniques. Two data mining tools, WEKA and RSES, were used for learning sunspot classification. In the training dataset sunspots were manually classified by comparing extracted sunspots with corresponding active region maps.
15193485
Towards the Stream Analysis Model in Grid-based Zero-Latency Data Stream Warehouse
Recent emerging applications increasingly generate continuous, larger amounts of valuable data. The demand of conducting advanced analysis over fast and huge data streams to capture trends, patterns, and exceptions become crucial. However, fully extracting the latent knowledge within the data stream is a challenging task because of insufficient technology. While Data Warehouse (DWH) technologies have resulted in considerable information processing efficiencies, there is still a significant delay in the time to deliver mission critical information to data consumers. Traditional data stream processing focuses on statistical approaches hence produces approximate results. In this paper, we introduce the Stream Analysis Model with a Grid-based ZeroLatency Data Stream Warehouse (GZLDSWH) framework which allows to perform analytical processing on continuous data streams and to trigger relevant actions depending on patterns discovered in event streams without using statistical approximation. Essential data stream elements are captured, analysed on the fly and finally evaluated to detect abnormalities while the entire data streams are stored within a Grid and integrated into a virtual DWH for further analysis in the case of ambiguity or uncertainty.