Journal ArticleDOI
GPU-based acceleration of an RNA tertiary structure prediction algorithm
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TLDR
This paper proposes a parallelization methodology for the fragment assembly of RNA (FARNA) algorithm, one of the most effective methods for computational prediction of RNA tertiary structure, and exploits multi-core CPUs and GPUs in harmony to maximize their utilization.About:
This article is published in Computers in Biology and Medicine.The article was published on 2013-09-01. It has received 12 citations till now. The article focuses on the topics: Speedup & General-purpose computing on graphics processing units.read more
Citations
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Journal ArticleDOI
RNA structure through multidimensional chemical mapping.
Siqi Tian,Rhiju Das +1 more
TL;DR: A modify-cross-link-map (MXM) expansion is proposed to overcome current limitations to resolving the in vivo ‘RNA structurome’ and provide detailed information on structurally heterogeneous RNA states, such as ligand-free riboswitches that are functionally important but difficult to resolve with other approaches.
Journal ArticleDOI
The role of real-time in biomedical science
TL;DR: The position that the concept of real-time will continue to play an important role in biomedical systems design is adopted and it is predicted that parallel processing considerations, such as Sp and algorithm scaling, will become more important.
Journal ArticleDOI
GPU-based acceleration of computations in elasticity problems solving by parametric integral equations system
TL;DR: A novel (parallel) approach of numerical implementation of PIES (named GPU-accelerated PIES) is proposed, using processing power of GPU to accelerate computations, and the results were obtained with very high accuracy.
Journal ArticleDOI
Acceleration of integration in parametric integral equations system using CUDA
TL;DR: This paper presents approach to accelerate numerical integration in PIES by nVidia CUDA, where the speed of integration increased up to 250 times whereas high accuracy of solutions was maintained.
Proceedings ArticleDOI
PLB-HeC: A Profile-Based Load-Balancing Algorithm for Heterogeneous CPU-GPU Clusters
TL;DR: PLB-HeC is presented, a Profile-based Load-Balancing algorithm for Heterogeneous CPU-GPU Clusters that performs an online estimation of performance curve models for each GPU and CPU processor and reduces the application execution times in almost all scenarios.
References
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Journal ArticleDOI
OpenMP: an industry standard API for shared-memory programming
TL;DR: At its most elemental level, OpenMP is a set of compiler directives and callable runtime library routines that extend Fortran (and separately, C and C++ to express shared memory parallelism) and leaves the base language unspecified.
The Landscape of Parallel Computing Research: A View from Berkeley
Krste Asanovic,Ras Bodik,Bryan Catanzaro,Joseph Gebis,Parry Husbands,Kurt Keutzer,David A. Patterson,William Plishker,John Shalf,Samuel Williams,Katherine Yelick +10 more
TL;DR: The parallel landscape is frame with seven questions, and the following are recommended to explore the design space rapidly: • The overarching goal should be to make it easy to write programs that execute efficiently on highly parallel computing systems • The target should be 1000s of cores per chip, as these chips are built from processing elements that are the most efficient in MIPS (Million Instructions per Second) per watt, MIPS per area of silicon, and MIPS each development dollar.
Book ChapterDOI
ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules.
Andrew Leaver-Fay,Michael D. Tyka,Steven M. Lewis,Oliver F. Lange,James Thompson,Ron Jacak,Kristian W. Kaufman,P. Douglas Renfrew,Colin A. Smith,William Sheffler,Ian W. Davis,Seth Cooper,Adrien Treuille,Daniel J. Mandell,Florian Richter,Yih-En Andrew Ban,Sarel J. Fleishman,Jacob E. Corn,David E. Kim,Sergey Lyskov,Monica Berrondo,Stuart Mentzer,Zoran Popović,James J. Havranek,John Karanicolas,Rhiju Das,Jens Meiler,Tanja Kortemme,Jeffrey J. Gray,Brian Kuhlman,David Baker,Philip Bradley +31 more
TL;DR: This chapter describes the requirements for the ROSETTA molecular modeling program's new architecture, justifies the design decisions, sketches out central classes, and highlights a few of the common tasks that the new software can perform.
Monte Carlo Methods
TL;DR: In this paper, the Monte Carlo method is not compelling for one dimensional integration, but it is more compelling for a d-dimensional integral evaluated withM points, so that the error in I goes down as 1/ √ M and is smaller if the variance σ 2 f of f is smaller.