High Performance Computing Codes Research Articles

Development of High Performance Computing Codes in Direct Boundary Element Method Using Hybrid Method Combined with CPU and GPU

전통적인 음향 직접 경계 요소법에서 해석 시간을 단축하려는 여러 가지 방법들을 시도하였다. 벡터연산, 병렬 연산 등을 통하여 전체적인 해석 시간을 줄일 수 있었다. 시스템 방정식을 구성할 때에 불필요하게 반복되는 부분을 별도로 처리하여 해석 속도를 증가시켰다. 시스템 방정식의 해를 구하기 위해 CPU에서 실행하도록 LINPACK, LAPACK 그리고 MKL 전용 라이브러리를 사용하여 각각의 경우에 대한 계산속도를 비교하였다. 또한, CPU와 GPU를 사용하는 혼성 프로그래밍을 하여 시스템 방정식의 해를 구할 때에 기존의 다중 CPU를 사용한 경우보다 수백 배 이상 빠른 결과를 얻을 수 있었다. 시스템 행렬이 복소, 비대칭 그리고 꽉찬 행렬형태를 갖고 있어서 직접 해법을 사용하였다. 포트란 언어와 C 언어를 사용하여 혼성으로 작성된 시스템 행렬 방정식 해법을 GPU의 CUDA 코어에서 실행시켜 고속 연산을 할 수 있었다. 개발한 직접 경계 요소법 코드를 사용하여 콘 혼의 입력 음향 임피던스를 예측한 결과는 시험결과와 잘 일치함을 나타냈다. 개발한 음향 직접 경계 요소법 프로그램을 사용하면 차 한잔 마실 시간에 수천 자유도의 음향 해석이 가능하다. 20 000 자유도 이상 그리고 200개 주파수 점에서 계산하는 음향 경계요소 문제들도 수 시간 내에 가능함을 나타냈다.

SoAx: A generic C++ Structure of Arrays for handling particles in HPC codes

The numerical study of physical problems often require integrating the dynamics of a large number of particles evolving according to a given set of equations. Particles are characterized by the information they are carrying such as an identity, a position other. There are generally speaking two different possibilities for handling particles in high performance computing (HPC) codes. The concept of an Array of Structures (AoS) is in the spirit of the object-oriented programming (OOP) paradigm in that the particle information is implemented as a structure. Here, an object (realization of the structure) represents one particle and a set of many particles is stored in an array. In contrast, using the concept of a Structure of Arrays (SoA), a single structure holds several arrays each representing one property (such as the identity) of the whole set of particles.The AoS approach is often implemented in HPC codes due to its handiness and flexibility. For a class of problems, however, it is known that the performance of SoA is much better than that of AoS. We confirm this observation for our particle problem. Using a benchmark we show that on modern Intel Xeon processors the SoA implementation is typically several times faster than the AoS one. On Intel’s MIC co-processors the performance gap even attains a factor of ten. The same is true for GPU computing, using both computational and multi-purpose GPUs.Combining performance and handiness, we present the library SoAx that has optimal performance (on CPUs, MICs, and GPUs) while providing the same handiness as AoS. For this, SoAx uses modern C++ design techniques such template meta programming that allows to automatically generate code for user defined heterogeneous data structures. Program summaryProgram Title: SoAxProgram Files doi:http://dx.doi.org/10.17632/m463pc4mv8.1Licensing provisions: GPLv3Programming language: C++Nature of problem: Structures of arrays (SoA) are generally faster than arrays of structures (AoS) while AoS are more handy. This library (SoAx) combines the advantages of both. By means of C++(11) meta-template programming SoAx achieves maximal performance (efficient use of vector units and cache of modern CPUs) while providing a very convenient user interface (including object-oriented element handling) and flexibility. It has been designed to handle list-like sets of particles (similar to struct int id; double[3] pos; float[3] vel;;) in the context of high-performance numerical simulations. It can be applied to many other problems.Solution method: Template Metaprogramming, Expression Templates

Shared genomics: high performance computing for distributed insights in genomic medical research.

The study of the genetics of diseases is entering a new era. Increasingly, genome-wide association studies are being used to identify positions within the human genome that have a link with a disease condition. The number of genomic locations studied means that High Performance Computing (HPC) solutions will have to increasingly be used in the statistical analysis of these data sets. Understanding the biomedical implications of the statistical analysis will also require heavy use of bioinformatics annotation tools. In this paper we report the outcome of developing HPC statistical genetics analysis codes for use by clinical researchers. Statistical results are automatically annotated with relevant biological information by calling multiple web-services orchestrated via pre-existing scientific workflows. Access to the HPC codes and bioinformatics annotation processes is via a client Workbench which hides as much as possible from the user the HPC infrastructure and bioinformatics annotation processes, whilst aiding the exchange of ideas and results between stakeholders.

A distributed computing environment for interdisciplinary applications

AbstractPractical applications are generally interdisciplinary in nature. Current technology is well matured for addressing individual disciplines but not for interdisciplinary applications. Hence, there is a need to couple the capabilities of several different computational disciplines to address these interdisciplinary applications. One approach is to use coupled or multi‐physics software, which typically involves developing and validating the entire software spectrum for a specific application. This will be extremely time consuming thus delaying the delivery of crucial capability to the end‐user. The other approach is to integrate individual well‐matured computational technology discipline's software thus taking advantage of the existing scalable software, validation investments, and tremendous developments in computational science. This integrated approach requires a consistent data model, data format, data management, seamless data movement, and robust, modular, scalable coupling algorithms. To address these requirements, we have developed a new flexible data exchange mechanism for high‐performance computing (HPC) codes and tools, known as the eXtensibleData Model and Format (XDMF). XDMF is part of a larger effort known as the ‘Interdisciplinary Computing Environment’ (ICE). ICE provides computational engines with the data management, visualizations, and user interface tools necessary to exist in a modern computing environment. Instead of imposing a new programming paradigm on HPC codes, XDMF uses the existing concept of file I/O for distributed coordination. XDMF incorporates Network Distributed Global Memory (NDGM), Hierarchical Data Format version 5 (HDF5), and eXtensible Markup Language (XML) to provide a flexible yet efficient data exchange mechanism. This paper discusses the development and implementation of a distributed computing environment for interdisciplinary applications utilizing the concept of a common data hub. Also, the implementation of XDMF is demonstrated for a typical blast–structure interaction interdisciplinary application. Published in 2002 by John Wiley & Sons, Ltd.

Supercomputational environmental acoustics

In the language of computer hardware engineers, supercomputing means using advanced, highly specialized technologies, to build super fast machines; one can call the super fast computing machines supercomputers. In the language of computer software developers, supercomputing may have another meaning. It may mean using advanced, highly specialized knowledge and techniques, to develop high-performance computer codes, or in another terminology, computer models. Such computer models could be designed, at will, to solve different real problems arising in all branches of science. This paper centers the attention at solving environmental acoustics problems by high-performance computer codes involving supercomputing. There are a number of phases to go through in order to produce an applicable high-performance computer code. One word that can be used to describe the whole task is MODELLING. The initial phase involved in modelling is mathematical. It requires the formulation of environmental phenomena into a mathematical model. The next phase is to develop efficient solution techniques for the mathematical model which are implement able into a computer code. These efficient solution techniques are usually numerical; thus, a lot of computations are required. The next phase is the construction of accurate and applicable computer models. Desirably, by using the computer model to solve large-scale environmental acoustics problems the required computations are not only accurate but also the computation speed is fast. This is where supercomputing can play an important role. Mathematical modelling considerations and its requirements are called to the modellers attention and discussions are also given to emphasize the importance of developing accurate solutions. How can supercomputing play an important role? The question is answered by using a realistic large-scale ocean acoustic propagation problem for discussion. It can be easily seen that this realistic problem motivated meaningful mathematical modelling, called for the development of efficient numerical solutions, challenged the construction