Kivanc Dincer

Bio: Kivanc Dincer is an academic researcher from Hacettepe University. The author has contributed to research in topics: High Performance Fortran & Fortran. The author has an hindex of 6, co-authored 21 publications receiving 136 citations. Previous affiliations of Kivanc Dincer include Başkent University & Scientific and Technological Research Council of Turkey.

A ubiquitous message passing interface implementation in Java:jmpi

Abstract: jmpi is a 100% Java-based implementation of the message-passing interface (MPI-1) standard jmpi comes with an efficient and effective MPI implementation in Java and supports a user-friendly Java application programming interface (API) for MPI. We present the implementation details and give some early communication benchmark performance results on a cluster of SUN UltraSparc workstations.

Particle-in-Cell Simulation Codes in High Performance Fortran

Abstract: Particle-in-Cell (PIC) plasma simulation codes model the interaction of charged particles with surrounding electrostatic and magnetic fields. Its computational requirements made it to be classified as one of the grand-challenge problems facing the high performance community. In this paper we present the implementation of 1-D and 2-D electrostatic PIC codes in High Performance Fortran(HPF) on a IBM SP-2. HPF expands Fortran 90 with data distribution and alignment directives and data parallel statements. It is a powerful language for writing portable and high performance programs across many platforms. We used one of the most successful commerical HPF compilers currently available in the market and augmented the compiler's missing HPF functions with extrinsic routines when necessary. We obtained near linear speed-up in all of our test cases. The performance of the HPF programs is comparable to the native message passing implementations of the same codes on the SP-2.

Building a World-Wide Virtual Machine Based on Web and HPCC Technologies

Abstract: In this paper we report on a parallel/distributed virtual machine prototype called World-Wide Virtual Machine (WWVM) that is designed to attack grand challenge problems beyond the capabilities of a single supercomputer. The prototype is based on emerging Web and existing HPCC technologies and utilizes the pool of Web servers on the internet as a flexible, convenient, and inexpensive metacomputing resource. World-Wide web supplies a standard open interface to the world regardless of the machine type. Through this interface Web servers can be used either as computation nodes or coordinators managing other connected nodes of the virtual machine. We have constructed a Web-based parallel/distributed programming environment for solving metaproblems consisting of MPI and PVM message-passing programs and High Performance Fortran programs.

Using Java and JavaScript in the Virtual Programming Laboratory: a Web‐based parallel programming environment

Abstract: The Virtual Programming Laboratory (VPL) is a Web-based virtual programming environment built based on a client–server architecture. The system can be accessed on any platform (Unix, PC or Mac) using a standard Java-enabled browser. Software delivery over the Web imposes a novel set of constraints on design. We outline the tradeoffs in this design space, motivate the choices necessary to deliver an application, and detail the lessons learned in the process. We discuss the role of Java and other Web technologies in the realization of the design. VPL facilitates the development and execution of parallel programs. The initial prototype supports high-level parallel programming based on Fortran 90 and High Performance Fortran (HPF), as well as explicit low-level programming with the MPI message-passing interface. Supplementary Java-based platform-independent tools for data and performance visualization are an integral part of the VPL. Pablo SDDF trace files generated by the Pablo performance instrumentation system are used for post-mortem performance visualization. © 1997 John Wiley & Sons, Ltd.

Design issues in building Web-based parallel programming environments

Abstract: We exploited the recent advances in Internet connectivity and Web technologies for building Web-based parallel programming environments (WPPEs) that facilitate the development and execution of parallel programs on remote high-performance computers. A Web browser running on the user's machine provides a user-friendly interface to server-site user accounts and allows the use of parallel computing platforms and software in a convenient manner. The user may create, edit, and execute files through this Web browser interface. This new Web-based client-server architecture has the potential of being used as a future front-end to high-performance computer systems. We discuss the design and implementation of several prototype WPPEs that are currently in use at the Northeast Parallel Architectures Center and the Cornell Theory Center. These initial prototypes support high-level parallel programming with Fortran 90 and High Performance Fortran (HPF), as well as explicit low-level programming with Message Passing Interface (MPI). We detail the lessons learned during the development process and outline the tradeoffs of various design choices in the realization of the design. We especially concentrate on providing server-site user accounts, mechanisms to access those accounts through the Web, and the Web-related system security issues.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Improved whale optimization algorithm for feature selection in Arabic sentiment analysis

Abstract: To help individuals or companies make a systematic and more accurate decisions, sentiment analysis (SA) is used to evaluate the polarity of reviews. In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. However, WOA suffers from the same problem faced by many other optimization algorithms and tend to fall in local optima. To overcome these problems, two improvements for WOA algorithm are proposed in this paper. The first improvement includes using Elite Opposition-Based Learning (EOBL) at initialization phase of WOA. The second improvement involves the incorporation of evolutionary operators from Differential Evolution algorithm at the end of each WOA iteration including mutation, crossover, and selection operators. In addition, we also used Information Gain (IG) as a filter features selection technique with WOA using Support Vector Machine (SVM) classifier to reduce the search space explored by WOA. To verify our proposed approach, four Arabic benchmark datasets for sentiment analysis are used since there are only a few studies in sentiment analysis conducted for Arabic language as compared to English. The proposed algorithm is compared with six well-known optimization algorithms and two deep learning algorithms. The comprehensive experiments results show that the proposed algorithm outperforms all other algorithms in terms of sentiment analysis classification accuracy through finding the best solutions, while its also minimizes the number of selected features.

Programming, Composing, Deploying for the Grid

Abstract: In summary, the essence of our proposition, presented in this chapter, is as follows: a distributed object-oriented programming model, smoothly extended to get a component-based programming model (in the form of a 100% Java library); moreover this model is “grid-aware” in the sense that it incorporates from the very beginning adequate mechanisms in order to further help in the deployment and runtime phases on all possible kind of infrastructures, notably secure grid systems. This programming framework is intended to be used for large scale grid applications. For instance, we have succeeded to apply it for a numerical simulation of electromagnetic waves propagation, a non embarrassingly parallel application [21], featuring visualization and monitor- ing capabilities for the user. To date, this simulation has successfully been deployed on various infrastructures, ranging from interconnected clusters, to an intranet grid composed of approxi- matively 300 desktop machines. Performances compete with a previous existing version of the application, written in Fortran MPI. The proposed object-oriented approach is more generic and features reusability (the component-oriented version is under development, which may further add dynamicity to the application), and the deployment is very flexible.