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NVIDIA's Graphics Processing Units (GPUs) have been widely adopted in many application domains to shorten the execution time by parallel processing and the Compute Unified Device Architecture (CUDA) platform enables high-performance, many-core parallel programming for NVIDIA GPUs. Various kinds of metaheuristic algorithms, aiming at finding an acceptable good solution rather than the optimum solution...
Graph Processing Units (GPUs) have recently evolved into a super multi-core and a fully programmable architecture. In the CUDA programming model, the programmers can simply implement parallelism ideas of a task on GPUs. The purpose of this paper is to accelerate Ant Colony Optimization (ACO) for Traveling Salesman Problems (TSP) with GPUs. In this paper, we propose a new parallel method, which is...
Most human resource websites apply SQL queries to find jobs or candidates, and users must state definite conditions to conduct database queries. We apply fuzzy logic theory and the service-oriented architecture to develop human resource web services. We propose a storing mechanism store fuzzy data into conventional database management systems without modifying DBMS models and a fuzzy query language...
Our previous study focused on accelerating an important category of DP problems, called nonserial polyadic dynamic programming (NPDP), on a graphics processing unit (GPU). In NPDP applications, the degree of parallelism varies significantly in different stages of computation, making it difficult to fully utilize the compute power of hundreds of pro-cessing cores in a GPU. To address this challenge,...
Dynamic programming (DP) is an important computational method for solving a wide variety of discrete optimization problems such as scheduling, string editing, packaging, and inventory management. In general, DP is classified into four categories based on the characteristics of the optimization equation. Because applications that are classified in the same category of DP have similar program behavior,...
Most of existing search engines retrieve web pages by means of finding exact keywords. Traditional keyword-based search engines suffer several problems. First, synonyms and terms similar to keywords are not taken into consideration to search web pages. Users may need to input several similar keywords individually to complete a search. Second, traditional search engines treat all keywords as the same...
The Fuzzy-Go search engine develops a fuzzy ontology to capture the similarities of terms in the ontology for accomplishing the semantic search of keywords, a web crawler to gather and classify web pages, and a fuzzy search mechanism to aggregate all fuzzy factors based on their degrees of importance and degrees of satisfaction. In this paper, we apply the genetic algorithm to propose a self-adaptation...
CLIPS is a non-algorithmic language designed especially for developing expert systems. To address the problem that CLIPS suffers from long execution time because of the characteristics of rule-based language, previously we have proposed a Grid-enabled parallel CLIPS language and a dynamic load balancing programming model that can parallelize the execution of a CLIPS program automatically if the data...
Predicting protein mutant stability changes is important for protein design. Although many methods have tried to improve prediction accuracy by various models, it will be difficult to employ them when the required input information is incomplete. Therefore, we have proposed a fuzzy query method previously to predict stability changes upon single mutations using partial input information. However,...
Recently, more and more studies investigated the is-sue of dealing with the heterogeneity problem on heterogeneous cluster systems consisting of multi-core computing nodes. Previously we have proposed a hybrid MPI and OpenMP based loop self-scheduling approach for this kind of system. The allocation functions of several well-known schemes have been modified for better performance. Though the previous...
Multicore computers have been widely included in cluster systems. They are shared memory architecture. However, previous research on parallel loop self-scheduling did not consider the feature of multicore computers. It is more suitable for shared-memory multiprocessors to adopt OpenMP for parallel programming. Therefore, in this paper, we propose to adopt hybrid programming model MPI+OpenMP to design...
This work mainly aims at the designs of the genetic algorithm based scheduling strategies by considering four different fault tolerance techniques in the grid environment, including retry, migration, checkpoint, replication. We also take into account the risk relationship between jobs and nodes to improve the system reliability in the scheduling algorithm. According to the simulation results, we can...
FuzzyCLIPS is a knowledge-base programming language designed especially for developing fuzzy expert systems. However, it usually requires much longer execution time than algorithmic languages. To address this problem, we propose to design a parallel version of FuzzyCLIPS to efficiently utilize the computing resources in emerging cluster and grid systems. MPI, the de-facto standard for parallel programming,...
Branch prediction is a key mechanism to boost the system performance of a superscalar processor. Though the prediction accuracy rate becomes higher and higher, the mispredicitons still lead to significant performance losses in a wide-issue deep-pipelined superscalar. To address the problem, the technique of multipath execution has been proposed previously, which is capable of executing both paths...
This paper proposes a compiler-time scheduling algorithm, called the dynamic critical path duplication (DCPD) scheduling algorithm, to exploit all of a program's available parallelism in distributed heterogeneous computing systems. This algorithm could exploit the potential of parallel processing, allowing for system heterogeneities and network bandwidth. It is compared favorably with other related...
According to the characteristics of chip multiprocessors, we propose a loop optimization technique to improve the system performance by reducing the occurrences of dependence violations
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