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High Throughput Computing plays an important role in the field of computational science. The Compressed Baryonic Matter (CBM) experiment is being planned at Facility for Antiproton and Ion Research (FAIR) accelerator complex, which is under construction at GSI laboratory in Darmstadt, Germany. This experiment is going to produce substantial data which requires high throughput computing to perform...
Sequential Monte Carlo simulation of pore networks, according to the Dual Site-Bond Model (DSBM) has been used successfully in the study of the structure and properties of porous media; these studies have a variety of applications (e.g. enhanced oil recovery, expedient gas storage, faster catalytic reactions, etc.) In the simplest form of DSBM, each pore is classified as a site or as a bond; the sites...
MapReduce is a very popular programming model to support parallel and distributed large-scale data processing. There have been a lot of efforts to implement this model on commodity GPU-based systems. However, most of these implementations can only work on a single GPU. And they can not be used to process large-scale datasets. In this paper, we present a new approach to design the MapReduce framework...
Amplicon Noise [1], an updated version of Py-ronoise [2], is a tool for removing noise from metagenomic data recorded by a 454 pyrosequencer. Amplicon Noise has shown to be effective in reducing overestimation of operational taxonomic units (OTUs) and chimera detection. Amplicon-Noise's noise removal method relies on clustering a large set of short sequences read by the sequencer. The DNA sequencing...
This paper introduces a novel implementation of the genetic algorithm exploiting a multi-GPU cluster. The proposed implementation employs an island-based genetic algorithm where every GPU evolves a single island. The individuals are processed by CUDA warps, which enables the solution of large knapsack instances and eliminates undesirable thread divergence. The MPI interface is used to exchange genetic...
Hybrid CPU/GPU computing architecture recently has become an alternative platform for high performance computing. This architecture provides massive computational power with lower energy consumption and less economic cost than the traditional one using only CPUs. However, the complexity of the GPU programming is too high for users to move their applications toward this hybrid computing architecture...
The current trend in medical research for the discovery of new drugs is the use of Virtual Screening (VS) methods. In these methods, the calculation of the non-bonded interactions, such as electrostatics or van der Waals forces, plays an important role, representing up to 80% of the total execution time. These kernels are computational intensive and massively parallel in nature, and thus they are...
Driven by the market demand for high-definition 3D graphics, commodity graphics processing units (GPUs) have evolved into highly parallel, multi-threaded, many-core processors, which are ideal for data parallel computing. Many applications have been ported to run on a single GPU with tremendous speedups using general C-style programming languages such as CUDA. However, large applications require multiple...
Graphics Processing Units (GPUs) are having a transformational effect on numerical lattice quantum chromo- dynamics (LQCD) calculations of importance in nuclear and particle physics. The QUDA library provides a package of mixed precision sparse matrix linear solvers for LQCD applications, supporting single GPUs based on NVIDIA's Compute Unified Device Architecture (CUDA). This library, interfaced...
Nowadays, NVIDIA's CUDA is a general purpose scalable parallel programming model for writing highly parallel applications. It provides several key abstractions - a hierarchy of thread blocks, shared memory, and barrier synchronization. This model has proven quite successful at programming multithreaded many core GPUs and scales transparently to hundreds of cores: scientists throughout industry and...
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