Search results for "cud"

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.

Las victorias de Bush

2001

Esperienze di accudimento ed abuso in campioni clinici e non clinici: rilevazioni attraverso l’intervista clinica CECA (Childhood Experience of Care …

2011

La CECA (Childhood Experience of Care and Abuse: Bifulco et al. 1994) è un’intervista retrospettiva semistrutturata, utilizzabile con soggetti giovani e adulti, che esplora le esperienze di accudimento vissute con i genitori ed altre figure significative nell’infanzia e nell’adolescenza. L’intervista CECA, recentemente validata in Italia (Giannone, Schimmenti et al. 2011), permette di ottenere misurazioni affidabili e utili, in termini clinici e di ricerca, sui contesti di sviluppo, le cure affettive e materiali ricevute e le eventuali esperienze di maltrattamento e abuso. Si tratta di uno strumento behavioral oriented che risponde all’esigenza di andare oltre le percezioni soggettive dell’…

A Fast GPU-Based Motion Estimation Algorithm for H.264/AVC

2012

H.264/AVC is the most recent predictive video compression standard to outperform other existing video coding standards by means of higher computational complexity. In recent years, heterogeneous computing has emerged as a cost-efficient solution for high-performance computing. In the literature, several algorithms have been proposed to accelerate video compression, but so far there have not been many solutions that deal with video codecs using heterogeneous systems. This paper proposes an algorithm to perform H.264/AVC inter prediction. The proposed algorithm performs the motion estimation, both with full-pixel and sub-pixel accuracy, using CUDA to assist the CPU, obtaining remarkable time …

Gossip

2019

Nowadays, a growing number of servers and workstations feature an increasing number of GPUs. However, slow communication among GPUs can lead to poor application performance. Thus, there is a latent demand for efficient multi-GPU communication primitives on such systems. This paper focuses on the gather, scatter and all-to-all collectives, which are important operations for various algorithms including parallel sorting and distributed hashing. We present two distinct communication strategies (ring-based and flow-oriented) to generate transfer plans for their topology-aware implementation on NVLink-connected multi-GPU systems. We achieve a throughput of up to 526 GB/s for all-to-all and 148 G…

CRiSPy-CUDA: Computing Species Richness in 16S rRNA Pyrosequencing Datasets with CUDA

2011

Pyrosequencing technologies are frequently used for sequencing the 16S rRNA marker gene for metagenomic studies of microbial communities. Computing a pairwise genetic distance matrix from the produced reads is an important but highly time consuming task. In this paper, we present a parallelized tool (called CRiSPy) for scalable pairwise genetic distance matrix computation and clustering that is based on the processing pipeline of the popular ESPRIT software package. To achieve high computational efficiency, we have designed massively parallel CUDA algorithms for pairwise k-mer distance and pairwise genetic distance computation. We have also implemented a memory-efficient sparse matrix clust…

COMPARISON OF CPML IMPLEMENTATIONS FOR THE GPU-ACCELERATED FDTD SOLVER

2011

Three distinctively difierent implementations of convolu- tional perfectly matched layer for the FDTD method on CUDA enabled graphics processing units are presented. All implementations store ad- ditional variables only inside the convolutional perfectly matched lay- ers, and the computational speeds scale according to the thickness of these layers. The merits of the difierent approaches are discussed, and a comparison of computational performance is made using complex real-life benchmarks.

CUSHAW Suite: Parallel and Efficient Algorithms for NGS Read Alignment

2017

Next generation sequencing (NGS) technologies have enabled cheap, large-scale, and high-throughput production of short DNA sequence reads and thereby have promoted the explosive growth of data volume. Unfortunately, the produced reads are short and prone to contain errors that are incurred during sequencing cycles. Both large data volume and sequencing errors have complicated the mapping of NGS reads onto the reference genome and have motivated the development of various aligners for very short reads, typically less than 100 base pairs (bps) in length. As read length continues to increase, propelled by advances in NGS technologies, these longer reads tend to have higher sequencing error rat…

Parallelized Clustering of Protein Structures on CUDA-Enabled GPUs

2014

Estimation of the pose in which two given molecules might bind together to form a potential complex is a crucial task in structural biology. To solve this so-called "docking problem", most algorithms initially generate large numbers of candidate poses (or decoys) which are then clustered to allow for subsequent computationally expensive evaluations of reasonable representatives. Since the number of such candidates ranges from thousands to millions, performing the clustering on standard CPUs is highly time consuming. In this paper we analyze and evaluate different approaches to parallelize the nearest neighbor chain algorithm to perform hierarchical Ward clustering of protein structures usin…

SAUCE: A Web-Based Automated Assessment Tool for Teaching Parallel Programming

2015

Many curricula for undergraduate studies in computer science provide a lecture on the fundamentals of parallel programming like multi-threaded computation on shared memory architectures using POSIX threads or OpenMP. The complex structure of parallel programs can be challenging, especially for inexperienced students. Thus, there is a latent need for software supporting the learning process. Subsequent lectures may cover more advanced parallelization techniques such as the Message Passing Interface (MPI) and the Compute Unified Device Architecture (CUDA) languages. Unfortunately, the majority of students cannot easily access MPI clusters or modern hardware accelerators in order to effectivel…