Visible to the public Scientific Computing

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Scientific Computing

Scientific computing is concerned with constructing mathematical models and quantitative analysis techniques and using computers to analyze and solve scientific problems. As a practical matter, scientific computing is the use of computer simulation and other forms of computation from numerical analysis and theoretical computer science to solve specific problems such as cybersecurity. The articles presented here cover a range of approaches and applications, as well as theories.

  • Kumar, A.; Grupcev, V.; Yuan, Y.; Huang, J.; Tu, Y.; Shen, G., "Computing Spatial Distance Histograms for Large Scientific Datasets On-the-Fly," Knowledge and Data Engineering, IEEE Transactions on, vol. PP, no.99, pp.1,1, January 2014. (ID#:14-1772) Available at: This paper focuses on an important query in scientific simulation data analysis: the Spatial Distance Histogram (SDH). The computation time of an SDH query using brute force method is quadratic. Often, such queries are executed continuously over certain time periods, increasing the computation time. We propose highly efficient approximate algorithm to compute SDH over consecutive time periods with provable error bounds. The key idea of our algorithm is to derive statistical distribution of distances from the spatial and temporal characteristics of particles. Upon organizing the data into a Quad-tree based structure, the spatiotemporal characteristics of particles in each node of the tree are acquired to determine the particles' spatial distribution as well as their temporal locality in consecutive time periods. We report our efforts in implementing and optimizing the above algorithm in Graphics Processing Units (GPUs) as means to further improve the efficiency. The accuracy and efficiency of the proposed algorithm is backed by mathematical analysis and results of extensive experiments using data generated from real simulation studies. Keywords: (not provided)
  • Jacob, F.; Wynne, A.; Yan Liu; Gray, J., "Domain-Specific Languages for Developing and Deploying Signature Discovery Workflows," Computing in Science & Engineering , vol.16, no.1, pp.52,64, Jan.-Feb. 2014. (ID#:14-1773) Available at: Domain-agnostic signature discovery supports scientific investigation across domains through algorithm reuse. A new software tool defines two simple domain-specific languages that automate processes that support the reuse of existing algorithms in different workflow scenarios. The tool is demonstrated with a signature discovery workflow composed of services that wrap original scripts running high-performance computing tasks. Keywords: parallel processing; software reusability; software tools; specification languages; workflow management software; algorithm reuse; domain-agnostic signature discovery; domain-specific languages; high-performance computing tasks; scientific investigation; scripts; signature discovery workflow; software tool; workflow scenarios; Clustering algorithms; DSL; Domain specific languages; Scientific computing; Software algorithms; Web services; XML; DSL; Taverna; domain-specific languages; scientific computing; signature discovery; workflow
  • Humphrey, Alan; Meng, Qingyu; Berzins, Martin; de Oliveira, Diego Caminha B.; Rakamaric, Zvonimir; Gopalakrishnan, Ganesh, "Systematic Debugging Methods for Large-Scale HPC Computational Frameworks," Computing in Science & Engineering , vol.16, no.3, pp.48,56, May-June 2014. (ID#:14-1774) Available at: Parallel computational frameworks for high-performance computing are central to the advancement of simulation-based studies in science and engineering. Unfortunately, finding and fixing bugs in these frameworks can be extremely time consuming. Left unchecked, these bugs can drastically diminish the amount of new science that can be performed. This article presents a systematic study of the Uintah Computational Framework and approaches to debug it more incisively. A key insight is to leverage the modular structure of Uintah, which lends itself to systematic debugging. In particular, the authors have developed a new approach based on coalesced stack trace graphs (CSTG) that summarize the system behavior in terms of key control flows manifested through function invocation chains. They illustrate several scenarios for how CSTGs could help efficiently localize bugs, and present a case study of how they found and fixed a real Uintah bug using CSTGs. Keywords: Computational modeling; Computer bugs; Debugging; Runtime; Scientific computing; Software development; Systematics; computational modeling and frameworks; debugging aids; parallel programming; reliability; scientific computing
  • Di Pierro, M., "Portable Parallel Programs with Python and OpenCL," Computing in Science & Engineering , vol.16, no.1, pp.34,40, Jan.-Feb. 2014. (ID#:14-1775) Available at: Two Python modules are presented: pyOpenCL, a library that enables programmers to write Open Common Language (OpenCL) code within Python programs; and ocl, a Python-to-C converter that lets developers write OpenCL kernels using the Python syntax. Like CUDA, OpenCL is designed to run on multicore GPUs. OpenCL code can also run on other architectures, including ordinary CPUs and mobile devices, always taking advantage of their multicore capabilities. Combining Python, numerical Python (numPy), pyOpenCL, and ocl creates a powerful framework for developing efficient parallel programs that work on modern heterogeneous architectures. Open Common Language (OpenCL) runs on multicore GPUs, as well as other architectures including ordinary CPUs and mobile devices. Combining OpenCL with numerical Python (numPy) and a new module - ocl, a Python-to-C converter that lets developers use Python to write OpenCL kernels - creates a powerful framework for developing efficient parallel programs for modern heterogeneous architectures. Keywords: high level languages; parallel architectures; parallel programming; CUDA; Open Common Language; Python syntax; Python-to-C converter; numPy; numerical Python; ocl; portable parallel program; pyOpenCL; Computer applications; Graphics processing units;Kernel; Multicore processing; Parallel processing; Programming; Scientific computing; GPU; OpenCL; Python; meta-programming; parallel programming; scientific computing
  • Gao, Shanzhen; Chen, Keh-Hsun, "Tackling Markoff-Hurwitz Equations," Computational Science and Computational Intelligence (CSCI), 2014 International Conference on , vol.1, no., pp.341,346, 10-13 March 2014. (ID#:14-1776) Available at: We present algorithms for searching and generating solutions to the equation x12+x22+ ...+xn2 = kx1x2...xn. Solutions are reported for n = 2, 3,..., 9. Properties of solutions are discussed. We can prove that the solutions do not exist when n=4 and k=2 or 3, n=5 and k=2 or 3. Conjectures based on computational results are discussed. Keywords: Educational institutions; Equations; Indexes; Radio access networks; Scientific computing; Systematics; Time complexity; Markoff and Hurwitz equations; search solution space; solution generator; solution trees
  • Leeser, M.; Mukherjee, S.; Ramachandran, J.; Wahl, T., "Make it real: Effective floating-point reasoning via exact arithmetic," Design, Automation and Test in Europe Conference and Exhibition (DATE), 2014, vol., no., pp.1,4, 24-28 March 2014. (ID#:14-1777) Available at: URL: Floating-point arithmetic is widely used in scientific computing. While many programmers are subliminally aware that floating-point numbers only approximate the reals, few are cognizant of the dangers this entails for programming. Such dangers range from tolerable rounding errors in sequential programs, to unexpected, divergent control flow in parallel code. To address these problems, we present a decision procedure for floating-point arithmetic (FPA) that exploits the proximity to real arithmetic (RA), via a loss-less reduction from FPA to RA. Our procedure does not involve any form of bit-blasting or bit-vectorization, and can thus generate much smaller back-end decision problems, albeit in a more complex logic. This tradeoff is beneficial for the exact and reliable analysis of parallel scientific software, which tends to give rise to large but benignly structured formulas. We have implemented a prototype decision engine and present encouraging results analyzing such software for numerical accuracy. Keywords: floating point arithmetic; parallel programming; software tools; FPA; RA; back-end decision problems; bit blasting; bit vectorization; divergent control flow; floating point arithmetic; floating point reasoning; floating-point-to-real reduction; numerical accuracy; parallel code; parallel scientific software; prototype decision engine ;real arithmetic; rounding errors; scientific computing; sequential programs; structured formulas; Abstracts; Cognition; Encoding; Equations; Floating-point arithmetic; Software; Standards
  • Al-Anzi, Fawaz S.; Salman, Ayed A.; Jacob, Noby K.; Soni, Jyoti, "Towards robust, scalable and secure network storage in Cloud Computing," Digital Information and Communication Technology and it's Applications (DICTAP), 2014 Fourth International Conference on , vol., no., pp.51,55, 6-8 May 2014. (ID#:14-1778) Available at: The term Cloud Computing is not something that appeared overnight, it may come from the time when computer system remotely accessed the applications and services. Cloud computing is Ubiquitous technology and receiving a huge attention in the scientific and industrial community. Cloud computing is ubiquitous, next generation's in-formation technology architecture which offers on-demand access to the network. It is dynamic, virtualized, scalable and pay per use model over internet. In a cloud computing environment, a cloud service provider offers "house of resources" includes applications, data, runtime, middleware, operating system, virtualization, servers, data storage and sharing and networking and tries to take up most of the overhead of client. Cloud computing offers lots of benefits, but the journey of the cloud is not very easy. It has several pitfalls along the road because most of the services are outsourced to third parties with added enough level of risk. Cloud computing is suffering from several issues and one of the most significant is Security, privacy, service availability, confidentiality, integrity, authentication, and compliance. Security is a shared responsibility of both client and service provider and we believe security must be information centric, adaptive, proactive and built in. Cloud computing and its security are emerging study area nowadays. In this paper, we are discussing about data security in cloud at the service provider end and proposing a network storage architecture of data which make sure availability, reliability, scalability and security. Keywords: Availability; Cloud computing; Computer architecture; Data security; Distributed databases; Servers; Cloud Computing; Data Storage; Data security; RAID
  • Pfarr, F.; Buckel, T.; Winkelmann, A., "Cloud Computing Data Protection -- A Literature Review and Analysis," System Sciences (HICSS), 2014 47th Hawaii International Conference on , vol., no., pp.5018,5027, 6-9 Jan. 2014. (ID#:14-1779) Available at: Cloud Computing technologies are gaining increased attention both in academia and practice. Despite of its relevance and potential for more IT flexibility and its beneficial effects on costs, legal uncertainties regarding the data processing especially between large economies still exist on the customer and provider side. Against this background, this contribution aims at providing an overview of privacy issues and legal frameworks for data protection in Cloud environments discussed in recent scientific literature. Due to the overall complexity concerning international law, we decided to primarily focus on data traffic between the United States of America and the European Union. The result of our research revealed significant differences in the jurisdiction and consciousness for data protection in these two economies. As a consequence for further Cloud Computing research we identify a large number of problems that need to be addressed. Keywords: cloud computing; data privacy; law; security of data; European Union ;IT flexibility; United States of America; cloud computing; data processing; data protection; data traffic; international law; legal uncertainties; privacy issues; Cloud computing; Data privacy; Data processing; Europe; Law; Standards; Cloud Computing; Data Protection; Literature Review; Privacy
  • You-Wei Cheah (author)and Beth Plale(advisor)," Quality, Retrieval, and Analysis of Provenance in Large Scale Data," doctoral dissertation, Indiana University, 2014. (ID#:14-1780) Available at: With the popularity of 'Big Data' rising, this paper focuses on provenance (classified by this paper as metadata describing the genealogy of a data product), its role prominent in the reuse and reproduction of scientific results. quality, capture, and representation for large-scale situations. With a framework and method that identifies correctness, completeness, and relevance of data provenance, these dimensions can be analyzed at the node/edge, graph, and multi-graph level. This paper also discusses the creation of a provenance database storing 48,000 provenance traces, including a failure model to address varying types of failures that may occur. Keywords: (not provided)


Articles listed on these pages have been found on publicly available internet pages and are cited with links to those pages. Some of the information included herein has been reprinted with permission from the authors or data repositories. Direct any requests via Email to SoS.Project (at) for removal of the links or modifications to specific citations. Please include the ID# of the specific citation in your correspondence.