Search results for "Software_PROGRAMMINGTECHNIQUES"
showing 9 items of 9 documents
CUSHAW2-GPU: Empowering Faster Gapped Short-Read Alignment Using GPU Computing
2014
We present CUSHAW2-GPU to accelerate the CUSHAW2 algorithm using compute unified device architecture (CUDA)-enabled GPUs. Two critical GPU computing techniques, namely intertask hybrid CPU-GPU parallelism and tile-based Smith-Waterman map backtracking using CUDA, are investigated to facilitate fast alignments. By aligning both simulated and real reads to the human genome, our aligner yields comparable or better performance compared to BWA-SW, Bowtie2, and GEM. Furthermore, CUSHAW2-GPU with a Tesla K20c GPU achieves significant speedups over the multithreaded CUSHAW2, BWA-SW, Bowtie2, and GEM on the 12 cores of a high-end CPU for both single-end and paired-end alignment.
Reverse inheritance in statically typed object-oriented programming languages
2010
Reverse inheritance is a new class reuse mechanism, an experimental implementation of which we have built for Eiffel. It enables a more natural design approach, factorization of common features (members), insertion of classes into an existing hierarchy etc. Due to its reuse potential in Eiffel we consider exploring its capabilities in other industrial-strength programming languages like C++, Java and C#.
Monitoring a descriptive panel-Consistence and agreement evaluation by both univariate and multivariante techniques
1993
Special Issue Rose Marie Pangborn Memorial Symposium; International audience
CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions
2013
Background The maximal sensitivity for local alignments makes the Smith-Waterman algorithm a popular choice for protein sequence database search based on pairwise alignment. However, the algorithm is compute-intensive due to a quadratic time complexity. Corresponding runtimes are further compounded by the rapid growth of sequence databases. Results We present CUDASW++ 3.0, a fast Smith-Waterman protein database search algorithm, which couples CPU and GPU SIMD instructions and carries out concurrent CPU and GPU computations. For the CPU computation, this algorithm employs SSE-based vector execution units as accelerators. For the GPU computation, we have investigated for the first time a GPU …
Unified Parallel C++
2018
Abstract Although MPI is commonly used for parallel programming on distributed-memory systems, Partitioned Global Address Space (PGAS) approaches are gaining attention for programming modern multi-core CPU clusters. They feature a hybrid memory abstraction: distributed memory is viewed as a shared memory that is partitioned among nodes in order to simplify programming. In this chapter you will learn about Unified Parallel C++ (UPC++), a library-based extension of C++ that gathers the advantages of both PGAS and Object Oriented paradigms. The examples included in this chapter will help you to understand the main features of PGAS languages and how they can simplify the task of programming par…
Large-scale genome-wide association studies on a GPU cluster using a CUDA-accelerated PGAS programming model
2015
[Abstract] Detecting epistasis, such as 2-SNP interactions, in genome-wide association studies (GWAS) is an important but time consuming operation. Consequently, GPUs have already been used to accelerate these studies, reducing the runtime for moderately-sized datasets to less than 1 hour. However, single-GPU approaches cannot perform large-scale GWAS in reasonable time. In this work we present multiEpistSearch, a tool to detect epistasis that works on GPU clusters. While CUDA is used for parallelization within each GPU, the workload distribution among GPUs is performed with Unified Parallel C++ (UPC++), a novel extension of C++ that follows the Partitioned Global Address Space (PGAS) model…
Faster GPU-Accelerated Smith-Waterman Algorithm with Alignment Backtracking for Short DNA Sequences
2014
In this paper, we present a GPU-accelerated Smith-Waterman (SW) algorithm with Alignment Backtracking, called GSWAB, for short DNA sequences. This algorithm performs all-to-all pairwise alignments and retrieves optimal local alignments on CUDA-enabled GPUs. To facilitate fast alignment backtracking, we have investigated a tile-based SW implementation using the CUDA programming model. This tiled computing pattern enables us to more deeply explore the powerful compute capability of GPUs. We have evaluated the performance of GSWAB on a Kepler-based GeForce GTX Titan graphics card. The results show that GSWAB can achieve a performance of up to 56.8 GCUPS on large-scale datasets. Furthermore, ou…
Guide pour l'ergonomie des interfaces homme-machine
1998
Rapport de contrat Bull INRIA; Spécifications ergonomiques pour la conception des interfaces logicielles Bull
Évaluation ergonomique du serveur intranet Bull
1997
Rapport de contrat Bull INRIA; Évaluation par inspection du serveur intranet Bull