high performance computing on graphics processing units: hgpu.org

The island model for evolutionary algorithms allows to delay the global convergence of the evolution process and encourage diversity. However, solving large size and time-intensive combinatorial optimization problems with the island model requires a large amount of computational resources. GPU computing is recently revealed as a powerful way to harness these resources. In this paper, […]

In this paper an optimized k-means implementation on the graphics processing unit (GPU) is presented. NVIDIApsilas compute unified device architecture (CUDA), available from the G80 GPU family onwards, is used as the programming environment. Emphasis is placed on optimizations directly targeted at this architecture to best exploit the computational capabilities available. Additionally drawbacks and limitations […]

The release of general purpose GPU programming environments has garnered universal access to computing performance that was once only available to super-computers. The availability of such computational power has fostered the creation and re-deployment of algorithms, new and old, creating entirely new classes of applications. In this paper, a GPU implementation of the Center-Surround Distribution […]

We present and evaluate the TILA-rin GPU microarchitecture for embedded systems using the ATTILA GPU simulation framework. We use a trace from an execution of the Unreal Tournament 2004 PC game to eval uate and compare the performance of the proposed embedded GPU against a baseline GPU architecture for the PC. We evaluate the different […]

Recent years have seen a trend in using graphic processing units (GPU) as accelerators for general-purpose computing. The inexpensive, single-chip, massively parallel architecture of GPU has evidentially brought factors of speedup to many numerical applications. However, the development of a high-quality GPU application is challenging, due to the large optimization space and complex unpredictable effects […]

This paper presents new software speed records for AES-128 encryption for architectures at both ends of the performance spectrum. On the one side we target the low-end 8-bit AVR microcontrollers and 32-bit ARM microprocessors, while on the other side of the spectrum we consider the high-performing Cell broadband engine and NVIDIA graphics processing units (GPUs). […]

Smith-Waterman algorithm is a classic dynamic programming algorithm to solve the problem of biological sequence alignment. However, with the rapid increment of the number of DNA and protein sequences, the originally sequential algorithm is very time consuming due to there existing the same computing task computed repeatedly on large-scale data. Today’s GPU (graphics processor unit) […]

We report a parallel Monte Carlo algorithm accelerated by graphics processing units (GPU) for modeling time-resolved photon migration in arbitrary 3D turbid media. By taking advantage of the massively parallel threads and low-memory latency, this algorithm allows many photons to be simulated simultaneously in a GPU. To further improve the computational efficiency, we explored two […]

Many bioinformatics studies require the analysis of RNA or DNA structures. More specifically, extensive work is done to elaborate efficient algorithms able to predict the 2-D folding structures of RNA or DNA sequences. However, the high computational complexity of the algorithms, combined with the rapid increase of genomic data, triggers the need of faster methods. […]

Algebraic Dynamic Programming (ADP) is a framework to encode a broad range of optimization problems, including common bioinformatics problems like RNA folding or pairwise sequence alignment. The ADP compiler translates such ADP programs into C. As all the ADP problems have similar data dependencies in the dynamic programming tables, a generic parallelization is possible. We […]

In this paper, the attractive features of evolutionary algorithm and Markov Chain Monte Carlo are combined into a new Differential Evolutionary Markov Chain Monte Carlo (DE-MCMC) method for multi-objective optimization problems with continuous variables. The DE-MCMC evolves a population of Markov chains through differential evolution (DE) toward a diversified set of solutions at the Pareto […]

As the most accurate model for simulating light propagation in heterogeneous tissues, Monte Carlo (MC) method has been widely used in the field of optical molecular imaging. However, MC method is time-consuming due to the calculations of a large number of photons propagation in tissues. The structural complexity of the heterogeneous tissues further increases the […]