A task scheduling problem is to arrange the tasks of an application on computing resources so as to achieve minimum schedule length. Many effective scheduling algorithms have been proposed, but most of them assumes th...
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A task scheduling problem is to arrange the tasks of an application on computing resources so as to achieve minimum schedule length. Many effective scheduling algorithms have been proposed, but most of them assumes that network is fully connected and contention free. In order to make this problem more practical, the link contention constraints are considered. In this paper we proposed an effective and efficient scheduling algorithm called Migration scheduling algorithm (MSA) based on Tabu search extended from list scheduling algorithm. The edges among the tasks are also scheduled by treating communication links between the processors as resources. To present the effectiveness of the proposed algorithm, we compared it with the Dynamic level scheduling algorithm (DLS) and List scheduling without contention. The proposed algorithm has admissible time complexity and suitable for regular as well as irregular task graph structures. Experimental results show that algorithm with tabu search produce optimal schedules in reasonable time.
We present Sequoia, a programming language designed to facilitate the development of memory hierarchy aware parallel programs that remain portable across modern machines featuring different memory hierarchy configurat...
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We present Sequoia, a programming language designed to facilitate the development of memory hierarchy aware parallel programs that remain portable across modern machines featuring different memory hierarchy configurations. Sequoia abstractly exposes hierarchical memory in the programming model and provides language mechanisms to describe communication vertically through the machine and to localize computation to particular memory locations within it. We have implemented a complete programming system, including a compiler and runtime systems for cell processor-based blade systems and distributed memory clusters, and demonstrate efficient performance running Sequoia programs on both of these platforms
Security is often an afterthought in software development, sometimes even bolted on during deployment or in maintenance through add-on security software and penetrate-and-patch maintenance. We think that security need...
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ISBN:
(纸本)9780769531021
Security is often an afterthought in software development, sometimes even bolted on during deployment or in maintenance through add-on security software and penetrate-and-patch maintenance. We think that security needs to be an integral part of software development and that preventing vulnerabilities by addressing their causes is as important as detecting and fixing them. In this paper we present a method for determining how to prevent vulnerabilities from being introduced during software development. Our method allows developers to select the set of activities that suits them best while being assured that those activities will prevent vulnerabilities. Our method is based on formal modeling of vulnerability causes and is independent of the software development process being used.
作者：
B. KrysztopH. KrawczykFaculty of Electronics
Telecommunication and Informatics Computer Architecture Department Technical University of GdaDsk GdaDsk Poland Faculty of Electronics
elecommunication and Informatics Computer Architecture Department Technical University of GdaDsk GdaDsk Poland
A new approach for developing efficient and flexible component-based distributed applications is proposed. It is based on a new programming platform TL (Transformation Language) which allows to express both abstract s...
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A new approach for developing efficient and flexible component-based distributed applications is proposed. It is based on a new programming platform TL (Transformation Language) which allows to express both abstract sequential code and parallel processing model of an application. To minimize execution cost and maximize flexibility, Distributed Partial Executor (DPE) tool and optimization algorithm is introduced. The example of the distributed image processing application is considered and its optimization in TL is analyzed. The obtained results confirm usability of the proposed methodology.
This paper reports on the design, implementation and performance evaluation of a suite of GridRPC programming middleware called Ninf-G Version 2 (Ninf-G2). Ninf-G2 is a reference implementation of the GridRPC API, a p...
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This paper reports on the design, implementation and performance evaluation of a suite of GridRPC programming middleware called Ninf-G Version 2 (Ninf-G2). Ninf-G2 is a reference implementation of the GridRPC API, a proposed GGF standard. Ninf-G2 has been designed so that it provides 1) high performance in a large-scale computational Grid, 2) the rich functionalities which are required to adapt to compensate for the heterogeneity and unreliability of a Grid environment, and 3) an API which supports easy development and execution of Grid applications. Ninf-G2 is implemented to work with basic Grid services, such as GSI, GRAM, and MDS in the Globus Toolkit version 2. The performance ofNinf-G2 was evaluated using a weather forecasting system which was developed using Ninf-G2. The experimental results indicate that high performance can be attained even in relatively fine-grained task-parallel applications on hundreds of processors in a Grid environment.
parallel/distributed application development is a very difficult task for non-expert programmers, and therefore support tools are needed for all phases of this kind of application development cycle. This means that de...
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parallel/distributed application development is a very difficult task for non-expert programmers, and therefore support tools are needed for all phases of this kind of application development cycle. This means that developing applications using predefined programming structures (frameworks) should be easier than doing it from scratch. We propose to take advantage of the knowledge about the structure of the application in order to develop a dynamic and automatic tuning tool. In this sense, we have designed POETRIES, which is a dynamic performance tuning tool based on the idea that a performance model could be associated to the high-level structure of the application. This way, the tool could efficiently make better tuning decisions. Specifically, we focus this work on the definition of the performance model associated to applications developed with the master-worker framework.
Moving infrared small target detection is widely used in areas such as target surveillance, precise guidance, etc. It has a high demand for the real-time and the probability of detection. Multiband simultaneous detect...
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Moving infrared small target detection is widely used in areas such as target surveillance, precise guidance, etc. It has a high demand for the real-time and the probability of detection. Multiband simultaneous detection can get a high detection probability, but it leads to substantial growth in computing time, which cannot meet the real-time requirement in practical applications. To solve this problem, this paper presents a parallel hierarchical approach based on MPI(Message Passing Interface) and OpenMP, which makes full use of the advantages of both message-passing model and shared storage model. It is based on the cluster system of multiprocessor nodes for testing. Experimental results show that, this parallel method can achieve a speedup of 7.8 compared with the serial method in the condition of the same detection probability. And its computational performance is also superior to the methods of pure OpenMP parallel and pure MPI parallel. This method has strong scalability.
With the advent of high-performance COTS clusters, there is a need for a simple, scalable and fault-tolerant parallel programming and execution paradigm. In this paper, we show that the popular MapReduce programming m...
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With the advent of high-performance COTS clusters, there is a need for a simple, scalable and fault-tolerant parallel programming and execution paradigm. In this paper, we show that the popular MapReduce programming model can be utilized to solve many interesting scientific simulation problems with much higher performance than regular cluster computers by leveraging GPGPU accelerators in cluster nodes. We use the Massive Unordered Distributed (MUD) formalism and establish a one-to-one correspondence between it and general Monte Carlo simulation methods. Our architecture, MITHRA, leverages NVIDIA CUDA technology along with Apache Hadoop to produce scalable performance gains using the MapReduce programming model. The evaluation of our proposed architecture using the Black Scholes option pricing model shows that a MITHRA cluster of 4 GPUs can outperform a regular cluster of 62 nodes, achieving a speedup of about 254 times in our testbed, while providing scalable near linear performance with additional nodes.
In this article is described the development of soft and hard environment for integrating individual cluster systems in a single, integrate parallel HPC systems. The elaborated applications can be ported to the resour...
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In this article is described the development of soft and hard environment for integrating individual cluster systems in a single, integrate parallel HPC systems. The elaborated applications can be ported to the resources of the integrated HPC system. To acquire the necessary theoretical and practical skills on using regional HPC clusters, currently it is preparing an interactive educational course for teaching students in the area of parallel programming, HPC clusters, and use of parallel software.
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