[신간안내] Computational Electrodynamics (the finite difference time - domain method)

We present the development of a novel high-performance face detection system using a neural network-based classification algorithm and an efficient parallelization with OpenMP. We discuss the design of the system in detail along with experimental assessment. Our parallelization strategy starts with one level of threads and moves to the exploitation of nested parallel regions in order to further improve, by up to 19p, the image-processing capability. The presented system is able to process images in real time (38 images-sec) by sustaining almost linear speedups on a system with a quad-core processor and a particular OpenMP runtime library. Copyright © 2009 John Wiley & Sons, Ltd.

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A high-performance face detection system using OpenMP

In the 1990s the Message Passing Interface Forum defined MPI bindings for Fortran, C, and C++. With the success of MPI these relatively conservative languages have continued to dominate in the parallel computing community. There are compelling arguments in favour of more modern languages like Java. These include portability, better runtime error checking, modularity, and multi-threading. But these arguments have not converted many HPC programmers, perhaps due to the scarcity of full-scale scientific Java codes, and the lack of evidence for performance competitive with C or Fortran. This paper tries to redress this situation by porting two scientific applications to Java. Both of these applications are parallelized using our thread-safe Java messaging system—MPJ Express. The first application is the Gadget-2 code, which is a massively parallel structure formation code for cosmological simulations. The second application uses the finite-domain time-difference method for simulations in the area of computational electromagnetics. We evaluate and compare the performance of the Java and C versions of these two scientific applications, and demonstrate that the Java codes can achieve performance comparable with legacy applications written in conventional HPC languages. Copyright © 2009 John Wiley & Sons, Ltd.

A comparative study of Java and C performance in two large-scale parallel applications

Background: The huge quantity of data produced in Biomedical research needs sophisticated algorithmic methodologies for its storage, analysis, and processing. High Performance Computing (HPC) appears as a magic bullet in this challenge. However, several hard to solve parallelization and load balancing problems arise in this context. Here we discuss the HPC-oriented implementation of a general purpose learning algorithm, originally conceived for DNA analysis and recently extended to treat uncertainty on data (U BRAIN). The U-BRAIN algorithm is a learning algorithm that finds a Boolean formula in disjunctive normal form (DNF), of approximately minimum complexity, that is consistent with a set of data (instances) which may have missing bits. The conjunctive terms of the formula are computed in an iterative way by identifying, from the given data, a family of sets of conditions that must be satisfied by all the positive instances and violated by all the negative ones; such conditions allow the computation of a set of coefficients (relevances) for each attribute (literal), that form a probability distribution, allowing the selection of the term literals. The great versatility that characterizes it, makes U-BRAIN applicable in many of the fields in which there are data to be analyzed. However the memory and the execution time required by the running are of O(n3) and of O(n5) order, respectively, and so, the algorithm is unaffordable for huge data sets.

Towards a HPC-oriented parallel implementation of a learning algorithm for bioinformatics applications

In the 1990s the Message Passing Interface Forum defined MPI bindings for Fortran, C, and C++. With the success of MPI these relatively conservative languages have continued to dominate in the parallel computing community. There are compelling arguments in favour of more modern languages like Java. These include portability, better runtime error checking, modularity, and multi‐threading. But these arguments have not converted many HPC programmers, perhaps due to the scarcity of full‐scale scientific Java codes, and the lack of evidence for performance competitive with C or Fortran. This paper tries to redress this situation by porting two scientific applications to Java. Both of these applications are parallelized using our thread‐safe Java messaging system—MPJ Express. The first application is the Gadget‐2 code, which is a massively parallel structure formation code for cosmological simulations. The second application uses the finite‐domain time‐difference method for simulations in the area of computational electromagnetics. We evaluate and compare the performance of the Java and C versions of these two scientific applications, and demonstrate that the Java codes can achieve performance comparable with legacy applications written in conventional HPC languages. Copyright © 2009 John Wiley & Sons, Ltd.

A comparative study of Java and C performance in two large‐scale parallel applications

This paper presents and evaluates a parallel Java implementation of the Finite-Difference Time-Domain (FDTD) method, which is a widely used numerical technique in computational electrodynamics The Java version is parallelized using MPJ Express - a thread-safe messaging library MPJ Express provides a full implementation of the mpiJava 12 API specification This specification defines a MPI-like binding for the Java language This paper describes our experiences of implementing the Java version of the FDTD method Towards the end of this paper, we evaluate and compare the performance of the Java version against its C counterpart on a 32 processing core Linux cluster of eight compute nodes

A parallel implementation of the Finite-Domain Time-Difference algorithm using MPJ express

This paper discusses a parallel implementation of the Finite-Difference Time-Domain (FDTD) method using Unified Parallel C (UPC) UPC is an extension of ANSI C — primarily designed for programming distributed memory parallel hardware The FDTD method is a powerful and widely used numerical technique in computational electromagnetics community It is a resource — CPU cycles and memory — hungry technique Consequently, a parallel implementation is highly desirable to decrease time-to-solution and be able to study larger problems with limited resources To the best of our knowledge, this is the first attempt to exploit UPC to parallelize the FDTD method We evaluate the performance of our implementation on a cluster of four SunFire v890 compute nodes — each equipped with 16 processing cores — and report impressive speedups

Using unified parallel C to parallelize the FDTD method of computational electrodynamics

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A parallel implementation of the Finite-Domain Time-Difference algorithm using MPJ express

Using unified parallel C to parallelize the FDTD method of computational electrodynamics