scholarly journals A COMPUTER CLUSTER SYSTEM FOR PSEUDO-PARALLEL EXECUTION OF GEANT4 SERIAL APPLICATION

2013 ◽  
Vol 53 (A) ◽  
pp. 829-831
Author(s):  
Memmo Federici ◽  
Bruno L. Martino
Keyword(s):  
Cluster Computing ◽  
Computing System ◽  
Parallel Execution ◽  
X Rays ◽  
Computer Cluster ◽  
Single Instance ◽  
Medium Quality ◽  
The Times ◽  
Cluster Computer ◽  
Computing Speed

Simulation of the interactions between particles and matter in studies for developing X-rays detectors generally requires very long calculation times (up to several days or weeks). These times are often a serious limitation for the success of the simulations and for the accuracy of the simulated models. One of the tools used by the scientific community to perform these simulations is Geant4 (Geometry And Tracking) [2, 3]. On the best of experience in the design of the AVES cluster computing system, Federici et al. [1], the IAPS (Istituto di Astrofisica e Planetologia Spaziali INAF) laboratories were able to develop a cluster computer system dedicated to Geant 4. The Cluster is easy to use and easily expandable, and thanks to the design criteria adopted it achieves an excellent compromise between performance and cost. The management software developed for the Cluster splits the single instance of simulation on the cores available, allowing the use of software written for serial computation to reach a computing speed similar to that obtainable from a native parallel software. The simulations carried out on the Cluster showed an increase in execution time by a factor of 20 to 60 compared to the times obtained with the use of a single PC of medium quality.

scholarly journals Tomosaic: efficient acquisition and reconstruction of teravoxel tomography data using limited-size synchrotron X-ray beams

2018 ◽  
Vol 25 (5) ◽  
pp. 1478-1489 ◽  
Author(s):  
Rafael Vescovi ◽  
Ming Du ◽  
Vincent de Andrade ◽  
William Scullin ◽  
Dogˇa Gürsoy ◽  
...  
Keyword(s):  
Imaging System ◽  
Three Dimensional ◽  
Computing System ◽  
X Rays ◽  
X Ray ◽  
High Penetration ◽  
X Ray Imaging ◽  
Software Modules ◽  
Dimensional Reconstruction ◽  
Tomography Data

X-rays offer high penetration with the potential for tomography of centimetre-sized specimens, but synchrotron beamlines often provide illumination that is only millimetres wide. Here an approach is demonstrated termed Tomosaic for tomographic imaging of large samples that extend beyond the illumination field of view of an X-ray imaging system. This includes software modules for image stitching and calibration, while making use of existing modules available in other packages for alignment and reconstruction. The approach is compatible with conventional beamline hardware, while providing a dose-efficient method of data acquisition. By using parallelization on a distributed computing system, it provides a solution for handling teravoxel-sized or larger datasets that cannot be processed on a single workstation in a reasonable time. Using experimental data, the package is shown to provide good quality three-dimensional reconstruction for centimetre-sized samples with sub-micrometre pixel size.

A Fast Design Space Exploration Method for Reconfigurable Architecture Based on Loop Optimization

2011 ◽  
Vol 467-469 ◽  
pp. 812-817 ◽  
Author(s):  
Dan Zhang ◽  
Rong Cai Zhao ◽  
Lin Han ◽  
Wei Fang Liang ◽  
Jin Qu ◽  
...  
Keyword(s):  
High Performance ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
General Purpose ◽  
Parallel Execution ◽  
Data Reuse ◽  
Loop Optimization ◽  
Fpga Chip ◽  
The Times

Using FPGA for general-purpose computation has become a hot research topic in high-performance computing technologies. However, the complexity of design and resource of FPGA make applying a common approach to solve the problem with mixed constraints impossible. Aiming at familiar loop structure of the applications, a design space exploration method based on FPGA hardware constrains is proposed according to the FPGA chip features, which combines the features of the corresponding application to perform loop optimization for reducing the demand of memory. Experimental results show that the method significantly improves the rate of data reuse, reduces the times of external memory access, achieves parallel execution of multiple pipelining, and effectively improves the performance of applications implemented on FPGA.

HAMR: A dataflow-based real-time in-memory cluster computing engine

2016 ◽  
Vol 31 (5) ◽  
pp. 361-374 ◽  
Author(s):  
Yao Wu ◽  
Long Zheng ◽  
Brian Heilig ◽  
Guang R Gao
Keyword(s):  
Big Data ◽  
Memory Management ◽  
High Performance ◽  
Cluster Computing ◽  
Programming Model ◽  
Distributed Processing ◽  
Large Data ◽  
Computing System ◽  
Fine Grain ◽  
Execution Model

As the attention given to big data grows, cluster computing systems for distributed processing of large data sets become the mainstream and critical requirement in high performance distributed system research. One of the most successful systems is Hadoop, which uses MapReduce as a programming/execution model and takes disks as intermedia to process huge volumes of data. Spark, as an in-memory computing engine, can solve the iterative and interactive problems more efficiently. However, currently it is a consensus that they are not the final solutions to big data due to a MapReduce-like programming model, synchronous execution model and the constraint that only supports batch processing, and so on. A new solution, especially, a fundamental evolution is needed to bring big data solutions into a new era. In this paper, we introduce a new cluster computing system called HAMR which supports both batch and streaming processing. To achieve better performance, HAMR integrates high performance computing approaches, i.e. dataflow fundamental into a big data solution. With more specifications, HAMR is fully designed based on in-memory computing to reduce the unnecessary disk access overhead; task scheduling and memory management are in fine-grain manner to explore more parallelism; asynchronous execution improves efficiency of computation resource usage, and also makes workload balance across the whole cluster better. The experimental results show that HAMR can outperform Hadoop MapReduce and Spark by up to 19x and 7x respectively, in the same cluster environment. Furthermore, HAMR can handle scaling data size well beyond the capabilities of Spark.

scholarly journals A GPU Accelerated Continuous-Based Discrete Element Method for Elastodynamics Analysis

2011 ◽  
Vol 320 ◽  
pp. 329-334 ◽  
Author(s):  
Zhao Song Ma ◽  
Chun Feng ◽  
Tian Ping Liu ◽  
Shi Hai Li
Keyword(s):  
Discrete Element Method ◽  
Gpu Computing ◽  
Discrete Element ◽  
Parallel Execution ◽  
Computing Algorithm ◽  
Speedup Ratio ◽  
Data Independence ◽  
Computing Speed ◽  
Node Group ◽  
Element Method

This paper presents a GPU computing algorithm, used to accelerate the Continuous-based Discrete Element Method (CDEM). Using a NVIDIA GTX VGA card, the computing speed achieved an average 650 times speedup ratio vs. Intel Core-Dual 2.66 GHz CPU. To parallelize the CDEM algorithm, the clone node force refreshing process is separated from the elemental calculation, and is replaced by a “Node Group” force assignment process, which ensures the data independence in parallel execution.

Fault Tolerance in Cluster Computing System

2011 ◽  
Author(s):  
Ashwini Patil ◽  
Ankit Shah ◽  
Sheetal Gaikwad ◽  
Akassh A. Mishra ◽  
Simranjit Singh Kohli ◽  
...  
Keyword(s):  
Fault Tolerance ◽  
Cluster Computing ◽  
Computing System

Environmental Examination of Data Mining Algorithms Based on Cloud-Computing

2013 ◽  
Vol 380-384 ◽  
pp. 2911-2914
Author(s):  
Yi Zhuo Guo ◽  
Tao Dai
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Cluster Computing ◽  
Programming Model ◽  
Test System ◽  
Parallel Execution ◽  
Apriori Algorithm ◽  
Data Mining Algorithms ◽  
Parallel Programming Model ◽  
Mining Algorithms

This article on cloud computing and data mining to a more comprehensive study to introduce the concept of cloud computing and data mining, pointed out that the traditional data mining techniques in the case of network test system of massive data mining, processing speed is slow, the load is not balancing and node efficiency is not high enough, Apriori algorithm based on the Map/Reduce parallel programming model, the distributed nature of cloud computing environments, make full use of cluster computing resources to support the parallel execution of algorithms by examples of cloud computing after Apriori algorithm in cloud computing environment to get higher efficiency of frequent itemsets mining algorithm performance than traditional data mining.

A Case Study of Teaching Parallel and Distributed Computing Topics on a Computer Cluster

2014 ◽  
Vol 16 (2) ◽  
pp. 58-71
Author(s):  
Hong Lin
Keyword(s):  
Operating System ◽  
Distributed Computing ◽  
Computer Science ◽  
Cluster Computing ◽  
Teaching Effectiveness ◽  
Science Curriculum ◽  
Parallel And Distributed Computing ◽  
Computer Cluster ◽  
Computer Science Curriculum

This paper presents the establishment of cluster computing lab at a minority serving institution that aims to provide computing resources to support undergraduate computer science curriculum. The computing resources of the cluster are managed by a job distribution environment that allows the users to upload, compile, and run their jobs. The job distribution software distributes the submitted jobs to the computing nodes of the cluster. The authors will present a case study of using this platform to teach parallel and distributed computing topics in the operating system course. The evaluation of the teaching effectiveness is presented thereafter.

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