the Volume Ray-Casting rendering algorithm, often used to produce medical imaging, is a well-known algorithm and the underlying computation can be easily executed in parallel. this is due to the fact that the huge num...
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ISBN:
(数字)9783642387180
ISBN:
(纸本)9783642387180;9783642387173
the Volume Ray-Casting rendering algorithm, often used to produce medical imaging, is a well-known algorithm and the underlying computation can be easily executed in parallel. this is due to the fact that the huge number of rays, used to sample the volumetric data, can be processed independently. However, the algorithm's performance may drop substantially when the complexity/size of the volumetric dataset increases. In this paper, we present three implementations of our parallel volume ray-casting algorithm in different multi-core architectures, such as CMPs, GPUs and MPSoCs. Furthermore, we show that using multi-GPUs, that perform in parallel, we can almost halve the rendering time. the performance and aspects of the three implementations are discussed.
Multi-color ordering is a parallel ordering that allows programs to be parallelized by application to sequentially executed parts of the programs. While multi-color ordering parallelizes sequentially executed parts wi...
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ISBN:
(数字)9783642387180
ISBN:
(纸本)9783642387180;9783642387173
Multi-color ordering is a parallel ordering that allows programs to be parallelized by application to sequentially executed parts of the programs. While multi-color ordering parallelizes sequentially executed parts with data dependences and increases the number of parts executed in parallel, improved performance by multi-color ordering is sensitive to differences in the architectures and systems on which the programs are executed. this sensitivity requires us to tune the numbers of colors;i.e., modify programs for each architecture and system. In this work, we develop a code generator based on multi-color ordering and automatically tune the number of colors using a job-level parallel scripting language Xcrypt. Furthermore, we support block multi-color ordering that avoids the disadvantage of stride accesses in the original multi-color ordering, and evaluate and clarify the effectiveness of block multi-color ordering.
Turning large volumes of data into actionable knowledge is a top challenge in high performance computing. Our previous work in this area demonstrated algorithmic techniques for massively parallel graph analysis on mul...
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Frequent Itemset Mining (FIM) is one of the most investigated fields of data mining. the goal of Frequent Itemset Mining (FIM) is to find the most frequently-occurring subsets from the transactions within a database. ...
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ISBN:
(纸本)9783642408199;9783642408205
Frequent Itemset Mining (FIM) is one of the most investigated fields of data mining. the goal of Frequent Itemset Mining (FIM) is to find the most frequently-occurring subsets from the transactions within a database. Many methods have been proposed to solve this problem, and the Apriori algorithm is one of the best known methods for frequent Itemset mining (FIM) in a transactional database. In this paper, a parallel Frequent Itemset Mining Algorithm, called Accelerating parallel Frequent Itemset Mining on Graphic Processors with Sorting (APFMS), is presented. this algorithm utilizes new-generation graphic processing units (GPUs) to accelerate the mining process. In it, massive processing units of GPU were used to speed up the frequent item verification procedure on the OpenCL platform. the experimental results demonstrated that the proposed algorithm had dramatically reduced computation time compared with previous methods.
the performance of parallel distributed data management systems becomes increasingly important withthe rise of Big Data. parallel joins have been widely studied both in the parallelprocessing and the database commun...
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the performance of parallel distributed data management systems becomes increasingly important withthe rise of Big Data. parallel joins have been widely studied both in the parallelprocessing and the database communities. Nevertheless, most of the algorithms so far developed do not consider the data skew, which naturally exists in various applications. State of the art methods designed to handle this problem are based on extensions to either of the two prevalent conventional approaches to parallel joins - the hash-based and duplication-based frameworks. In this paper, we introduce a novel parallel join framework, query-based distributed join (QbDJ), for handling data skew on distributed architectures. Further, we present an efficient implementation of the method based on the asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate the performance of our approach on a cluster of 192 cores (16 nodes) and datasets of 1 billion tuples with different skews. the results show that the method is scalable, and also runs faster with less network communication compared to state-of-art PRPD approach in [1] under high data skew.
the number of nodes inside supercomputers is continuously increasing. As detailed in the TOP500 list, there are now systems that include more than one million nodes;for instance China's Tianhe-2. To cope withthis...
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We present in this paper a security-driven solution for scheduling of N independent jobs on M parallel machines that minimizes three different objectives simultaneously, namely the failure probability, the total compl...
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Performance and efficiency became recently key requirements of computer architectures. Modern computers incorporate Graphics processing Units (GPUs) into running data mining algorithms, as well as other general purpos...
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ISBN:
(纸本)9783642396403
Performance and efficiency became recently key requirements of computer architectures. Modern computers incorporate Graphics processing Units (GPUs) into running data mining algorithms, as well as other general purpose computations. In this paper, different parallelization methods are analyzed and compared in order to understand their applicability. From multi-threading on shared memory to using NVIDIA's GPU accelerators for increasing performance and efficiency on parallel computing, this work discusses the parallelization of data mining algorithms considering performance and efficiency issues. the performance is compared on both many-core systems and GPU accelerators on a distance measure algorithm using a relatively big data set. We optimize the way we deal with GPUs in heterogeneous systems to make them more suitable for big data mining applications with heavy distance calculations. Moreover, we focus on achieving a higher utilization of GPU resources and a better reuse of data. Our implementation of the content-based similarity algorithm SQFD on the GPU outperforms by up to 50x CPU counterparts, and up to 15x CPU multi-threaded implementations.
Timeliness, accuracy and effectiveness of manufacturing information in manufacturing and business process management have become important factors of constraint to business growth. Single RFID (Radio Frequency Identif...
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the proceedings contain 23 papers. the topics discussed include: efficient parallelalgorithms for XML filtering with structural and value constraints;a simulation-based method for eliciting requirements of online CIB...
ISBN:
(纸本)9783642366079
the proceedings contain 23 papers. the topics discussed include: efficient parallelalgorithms for XML filtering with structural and value constraints;a simulation-based method for eliciting requirements of online CIB systems;reducing latency and network load using location-aware memcache architectures;modeling capabilities as attribute-featured entities;governance policies for verification and validation of service choreographies;real-text dictionary for topic-specific web searching;evaluating cross-platform development approaches for mobile applications;information gathering tasks on the web: attempting to identify the user search behavior;web-based exploration of photos with time and geospace;mixed-initiative management of online calendars;knowledge discovery: data mining by self-organizing maps;and ranking location-dependent keywords to extract geographical characteristics from microblogs.
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