Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
ISBN:
(纸本)9781538655566;9781538655559
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
DEPSO-Scout is a hybrid optimization algorithm combining Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The solution convergence is balanced between exploration of PSO ...
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ISBN:
(纸本)9781538623183;9781538623176
DEPSO-Scout is a hybrid optimization algorithm combining Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The solution convergence is balanced between exploration of PSO and exploitation from DE. The suboptimal solution has reduced by the scout bee property of ABC. DEPSO-Scout outperforms traditional DE, PSO, and ABC. However, in a higher dimension of search space, the accuracy of DEPSO-Scout is maintained while the search speed is significantly decreased. From the experiment, the computational time varies depending on the complexity of the problem. To improve the time-performance of DEPSO-Scout, the parallelization techniques becomes an interest. By modifying the DEPSO-Scout algorithm with the parallel approach, the speed of algorithm significantly improved while the correctness of solutions is maintained. The experiment and analysis of speedup and algorithm efficiency are discussed. The improvement opportunity of parallel DEPSO-Scout is also discussed in the last section.
This paper presents a proposal of a new load balancer which aims reduce the runtime and power consumption of parallel applications when these are runned in shared memory environments. The algorithm of the balancer col...
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ISBN:
(纸本)9781728137735
This paper presents a proposal of a new load balancer which aims reduce the runtime and power consumption of parallel applications when these are runned in shared memory environments. The algorithm of the balancer collects system and application information in real time and then use it to make task migration decisions. For the implementation of strategy was used the Charm++ parallel programming. Preliminary results show reductions of up to 35.36% of runtime and energy consumption for three benchmarks used in the tests.
One-sided communication is a useful paradigm for irregular parallel applications, but most one-sided programming environments, including MPI’s one-sided interface and PGAS programming languages, lack application-leve...
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The importance of optimization and NP-problem solving cannot be overemphasized. The usefulness and popularity of evolutionary computing methods are also well established. There are various types of evolutionary method...
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The importance of optimization and NP-problem solving cannot be overemphasized. The usefulness and popularity of evolutionary computing methods are also well established. There are various types of evolutionary methods;they are mostly sequential but some of them have parallel implementations as well. We propose a multi-population method to parallelize the Imperialist Competitive Algorithm. The algorithm has been implemented with the Message Passing Interface on 2 computer platforms, and we have tested our method based on shared memory and message passing architectural models. An outstanding performance is obtained, demonstrating that the proposed method is very efficient concerning both speed and accuracy. In addition, compared with a set of existing well-known parallel algorithms, our approach obtains more accurate results within a shorter time period.
Many scientific applications handle large size sparse matrices which can be stored using special compression formats to reduce memory space and processing time. The choice of the Optimal Compression Format (OCF) is a ...
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ISBN:
(纸本)9781538678800
Many scientific applications handle large size sparse matrices which can be stored using special compression formats to reduce memory space and processing time. The choice of the Optimal Compression Format (OCF) is a critical process that involves several criteria. In this paper, we propose to use machine learning approach to predict the OCF (among CSR, CSC, ELL and COO) for SMVP kernel on multiprocessor platform. Our goal is not only to reach high accuracy values but also to minimize the LUBS (Loss Under Best Selection). Our main contribution consists in using data parallel model to extract features dataset. Experimental results show that we achieve more than 95% accuracy.
Technology improvements as well as demographic expansion implies an increase on the amount of information that needs processing. With this necessity, it becomes apparent that the use of algorithms capable of handle su...
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Technology improvements as well as demographic expansion implies an increase on the amount of information that needs processing. With this necessity, it becomes apparent that the use of algorithms capable of handle such amount of information is a must have, as well as algorithms capable of taking the maximum advantage of the current processing *** work presents the use of the decision tree to analyze numeric data, and further more we will explore the massive parallelization of the algorithm using the CUDA technology and the PyCUDA module for an easy integration and the meta-programming capabilities it provides, showing the optimizations made in the process. A results comparison between the original algorithm, and the optimized implementation will be presented, and conclusions will be drawn *** algorithm to employ is the decision tree. This algorithm was selected for its simplicity, inherit partition of the data and the results. Unlike other machine learning algorithms, the decision tree provides a clear description of the process made to reach a certain classification which is a desired property for further *** technology will be exploited using CUDA's programming interface, achieving an improvement over 17000 x over the classic serial implementation and their bounds and limitations.
Context: Software developers face complex, connected, and large software projects. The development of such systems involves design decisions that directly impact the quality of the software. For an early decision maki...
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ISBN:
(纸本)9781538658628
Context: Software developers face complex, connected, and large software projects. The development of such systems involves design decisions that directly impact the quality of the software. For an early decision making, software developers can use model-based prediction approaches for (non-)functional quality properties. Unfortunately, the accuracy of these approaches is challenged by newly introduced hardware features like multiple cores within a single CPU (multicores) and their dependence on shared memory and other shared resources. Objectives: Our goal is to understand whether and how existing model-based performance prediction approaches face this challenge. We plan to use gained insights as foundation for enriching existing prediction approaches with capabilities to predict systems running on multicores. Methods: We perform a Systematic Literature Review (SLR) to identify current model-based prediction approaches in the context of multicores. Results: Our SLR covers the software engineering, embedded systems, High Performance Computing, and Software Performance Engineering domains for which we examined 34 sources in detail. We found various performance prediction approaches which tries to increase prediction accuracy for multicore systems by including shared memory designs to the prediction models. Conclusion: However, our results show that the memory designs models are only in an initial phase. Further research has to be done to improve cache, memory, and memory bandwidth model as well as to include auto tuner support.
Bioinformatics is an interdisciplinary field that applies techniques from computer science, statistics and engineering to guide in the study of large biological data. Protein structure and sequence analysis is very im...
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ISBN:
(纸本)9781509042432
Bioinformatics is an interdisciplinary field that applies techniques from computer science, statistics and engineering to guide in the study of large biological data. Protein structure and sequence analysis is very important in bioinformatics mainly in understanding cellular processes which helps in simplifying the development of drugs for metabolic pathways. Protein sequence alignment is a technique that is concerned with identifying the similarities among different protein structures in order to discover the relationships among them. These kinds of techniques are computationally extensive which hinders their applicability. In this paper, we propose a parallel approach to speed up the computational time of two sequence alignment algorithms using a hybrid implementation that combines the power of multicore CPUs and that of contemporary GPUs. Our study shows that the hybrid approach solves the problem much faster than its sequential counterpart.
The rapid progress of multi/many-core architectures has caused data-intensive parallel applications not yet fully optimized to deliver the best performance. In the advent of concurrent programming, frameworks offering...
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The rapid progress of multi/many-core architectures has caused data-intensive parallel applications not yet fully optimized to deliver the best performance. In the advent of concurrent programming, frameworks offering structured patterns have alleviated developers' burden adapting such applications to multithreaded architectures. While some of these patterns are implemented using synchronization primitives, others avoid them by means of lock-free data mechanisms. However, lock-free programming is not straightforward, ensuring an appropriate use of their interfaces can be challenging, since different memory models plus instruction reordering at compiler/processor levels can interfere in the occurrence of data races. The benefits of race detectors are formidable in this sense;however, they may emit false positives if are unaware of the underlying lock-free structure semantics. To mitigate this issue, this paper extends ThreadSanitizer, a race detection tool, with the semantics of 2 lock-free data structures: the single-producer/single-consumer and the multiple-producer/multiple-consumer queues. With it, we are able to drop false positives and detect potential semantic violations. The experimental evaluation, using different queue implementations on a set of benchmarks and real applications, demonstrates that it is possible to reduce, on average, 60% the number of data race warnings and detect wrong uses of these structures.
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