Petroleum Geoscience Big Data and GPU Parallel Computing

2015 ◽  
Author(s):  
Fei Han ◽  
Sam Z. Sun
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Gpu Parallel Computing

PARALLEL COMPUTING OF NUMERICAL SCHEMES AND BIG DATA ANALYTIC FOR SOLVING REAL LIFE APPLICATIONS

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Norma Alias ◽  
Nadia Nofri Yeni Suhari ◽  
Hafizah Farhah Saipan Saipol ◽  
Abdullah Aysh Dahawi ◽  
Masyitah Mohd Saidi ◽  
...  
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Parallel Algorithm ◽  
Sparse Matrices ◽  
Real Life ◽  
Poor Performance ◽  
Equation System ◽  
Numerical Schemes ◽  
Linear Equation System ◽  
Data Analytic

This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance.

Video Synopsis Generation Using GPGPU

2013 ◽  
Vol 433-435 ◽  
pp. 297-300
Author(s):  
Zong Yue Wang
Keyword(s):  
Parallel Computing ◽  
Original Video ◽  
Generation Efficiency ◽  
Generation Algorithm ◽  
Video Representation ◽  
Gpu Parallel Computing ◽  
Video Synopsis ◽  
Video Summaries

Video summaries provide a compact video representation preserving the essential activities of the original video, but the summaries may be confusing when mixing different activities together. Summaries Clustered methodology, showing similar activities simultaneously, enables to view much easier and more efficiently. However, it is very time consuming in generating summaries, especially in calculating motion distance and collision cost. To improve the efficiency of generating summaries, a parallel video synopsis generation algorithm is proposed based on GPGPU. The experiment result shows generation efficiency is improved greatly through GPU parallel computing. The acceleration radio can reach at 5.75 when data size is above 1600*960*30000.

Improve and Optimize Query Recommendation System by MST Algorithm and its MapReduce Implementation

2014 ◽  
Vol 701-702 ◽  
pp. 50-53
Author(s):  
Jian Liang Meng ◽  
Da Wei Li
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Spanning Tree ◽  
Recommendation System ◽  
Minimum Spanning Tree ◽  
Programming Model ◽  
Search Efficiency ◽  
Tree Algorithm ◽  
Query Recommendation ◽  
Parallel Query

Query recommendation as an important tool to enhance the user search efficiency has gradually become a hotspot. In the context of big data, using the MapReduce programming model, combined with distributed minimum spanning tree algorithm, a parallel query recommended method based on MapReduce was proposed in this paper. The final results show that the efficiency of query recommendation was greatly improved through parallel computing.

Effective ACPS-Based Rescheduling of Parallel Batch Processing Machines with MapReduce

2014 ◽  
Vol 575 ◽  
pp. 820-824
Author(s):  
Bin Zhang ◽  
Jia Jin Le ◽  
Mei Wang
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Ant Colony ◽  
Search Problem ◽  
Parallel Search ◽  
Good Efficiency ◽  
Heuristic Scheduling ◽  
Low Efficiency

MapReduce is a highly efficient distributed and parallel computing framework, allowing users to readily manage large clusters in parallel computing. For Big data search problem in the distributed computing environment based on MapReduce architecture, in this paper we propose an Ant colony parallel search algorithm (ACPSMR) for Big data. It take advantage of the group intelligence of ant colony algorithm for global parallel search heuristic scheduling capabilities to solve problem of multi-task parallel batch scheduling with low efficiency in the MapReduce. And we extended HDFS design in MapReduce architecture, which make it to achieve effective integration with MapReduce. Then the algorithm can make the best of the scalability, high parallelism of MapReduce. The simulation experiment result shows that, the new algorithm can take advantages of cloud computing to get good efficiency when mining Big data.

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