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Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, 2011
Recently a GPGPU application had to be redesigned to overcome performance problems. A number of software engineering lessons were learnt from this and other projects. We describe those about obtaining high performance from nVidia GPUs and practical aspects of CUDA C software development.
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Recently a GPGPU application had to be redesigned to overcome performance problems. A number of software engineering lessons were learnt from this and other projects. We describe those about obtaining high performance from nVidia GPUs and practical aspects of CUDA C software development.
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CUDA-BLASTP: Accelerating BLASTP on CUDA-Enabled Graphics Hardware
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2011Scanning protein sequence database is an often repeated task in computational biology and bioinformatics. However, scanning large protein databases, such as GenBank, with popular tools such as BLASTP requires long runtimes on sequential architectures.
Weiguo Liu +2 more
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2023
The paper focuses on the problem of chemical kinetics, calculation of changes in the concentration of substances in the reactions over time, and creation of a mass kinetic solver to solve the problem using modern parallelization technologies. A mathematical model of variation in the concentration of substances in a system with a one-dimensional ...
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The paper focuses on the problem of chemical kinetics, calculation of changes in the concentration of substances in the reactions over time, and creation of a mass kinetic solver to solve the problem using modern parallelization technologies. A mathematical model of variation in the concentration of substances in a system with a one-dimensional ...
????????????????????, ??.C. +1 more
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CUDA-MAFFT: Accelerating MAFFT on CUDA-enabled graphics hardware
2013 IEEE International Conference on Bioinformatics and Biomedicine, 2013Multiple sequence alignment (MSA) constitutes an extremely powerful tool for many biological applications including phylogenetic tree estimation, secondary structure prediction, and critical residue identification. However, aligning large biological sequences with popular tools such as MAFFT requires long runtimes on sequential architectures.
Xiangyuan Zhu, Kenli Li 0001
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ACM SIGGRAPH ASIA 2009 Sketches, 2009
Modern GPUs provide gradually increasing programmability on vertex shader, geometry shader and fragment shader in the past decade. However, many classical problems such as order-independent transparency (OIT), occlusion culling have not yet been efficiently solved using the traditional graphics pipeline.
Fang Liu +3 more
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Modern GPUs provide gradually increasing programmability on vertex shader, geometry shader and fragment shader in the past decade. However, many classical problems such as order-independent transparency (OIT), occlusion culling have not yet been efficiently solved using the traditional graphics pipeline.
Fang Liu +3 more
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Scalable parallel programming with CUDA
ACM SIGGRAPH 2008 classes, 2008The advent of multicore CPUs and manycore GPUs means that mainstream processor chips are now parallel systems. Furthermore, their parallelism continues to scale with Moore's law. The challenge is to develop mainstream application software that transparently scales its parallelism to leverage the increasing number of processor cores, much as 3D graphics
John Nickolls +3 more
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2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS), 2010
NVIDIA's CUDA architecture provides a powerful platform for writing highly parallel programs. By providing simple abstractions for hierarchical thread organization, memories, and synchronization, the CUDA programming model allows programmers to write scalable programs without the burden of learning a multitude of new programming constructs.
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NVIDIA's CUDA architecture provides a powerful platform for writing highly parallel programs. By providing simple abstractions for hierarchical thread organization, memories, and synchronization, the CUDA programming model allows programmers to write scalable programs without the burden of learning a multitude of new programming constructs.
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Evolutionary Clustering on CUDA
2012Unsupervised clustering of large data sets is a complicated task. Due to its complexity, various meta-heuristic machine learning algorithms have been used to automate the clustering process. Genetic and evolutionary algorithms have been deployed to find clusters in data sets with success.
Pavel Krömer +2 more
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Proceedings of the 19th ACM SIGPLAN symposium on Principles and practice of parallel programming, 2014
Parallel programs consist of series of code sections with different thread-level parallelism (TLP). As a result, it is rather common that a thread in a parallel program, such as a GPU kernel in CUDA programs, still contains both se-quential code and parallel loops.
Yi Yang, Huiyang Zhou
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Parallel programs consist of series of code sections with different thread-level parallelism (TLP). As a result, it is rather common that a thread in a parallel program, such as a GPU kernel in CUDA programs, still contains both se-quential code and parallel loops.
Yi Yang, Huiyang Zhou
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Proceedings of the ACM international conference companion on Object oriented programming systems languages and applications companion, 2010
Whereas the fastest supercomputer of 1998 could compute 1.34 trillion double precision floating point operations per second (TFLOPS) [7], today's consumer-level (sub-$500) graphics cards such as the NVidia GeForce GTX 480 can compute 1.35 TFLOPS (single precision) [8].
Nate Anderson +2 more
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Whereas the fastest supercomputer of 1998 could compute 1.34 trillion double precision floating point operations per second (TFLOPS) [7], today's consumer-level (sub-$500) graphics cards such as the NVidia GeForce GTX 480 can compute 1.35 TFLOPS (single precision) [8].
Nate Anderson +2 more
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