26,373 research outputs found
Optimizing Splicing Junction Detection in Next Generation Sequencing Data on a Virtual-GRID Infrastructure
The new protocol for sequencing the messenger RNA in a cell, named RNA-seq produce millions of short sequence fragments. Next Generation Sequencing technology allows more accurate analysis but increase needs in term of computational resources. This paper describes the optimization of a RNA-seq analysis pipeline devoted to splicing variants detection, aimed at reducing computation time and providing a multi-user/multisample environment. This work brings two main contributions. First, we optimized a well-known algorithm called TopHat by parallelizing some sequential mapping steps. Second, we designed and implemented a hybrid virtual GRID infrastructure allowing to efficiently execute multiple instances of TopHat running on different samples or on behalf of different users, thus optimizing the overall execution time and enabling a flexible multi-user environmen
Breadth First Search Vectorization on the Intel Xeon Phi
Breadth First Search (BFS) is a building block for graph algorithms and has
recently been used for large scale analysis of information in a variety of
applications including social networks, graph databases and web searching. Due
to its importance, a number of different parallel programming models and
architectures have been exploited to optimize the BFS. However, due to the
irregular memory access patterns and the unstructured nature of the large
graphs, its efficient parallelization is a challenge. The Xeon Phi is a
massively parallel architecture available as an off-the-shelf accelerator,
which includes a powerful 512 bit vector unit with optimized scatter and gather
functions. Given its potential benefits, work related to graph traversing on
this architecture is an active area of research.
We present a set of experiments in which we explore architectural features of
the Xeon Phi and how best to exploit them in a top-down BFS algorithm but the
techniques can be applied to the current state-of-the-art hybrid, top-down plus
bottom-up, algorithms.
We focus on the exploitation of the vector unit by developing an improved
highly vectorized OpenMP parallel algorithm, using vector intrinsics, and
understanding the use of data alignment and prefetching. In addition, we
investigate the impact of hyperthreading and thread affinity on performance, a
topic that appears under researched in the literature. As a result, we achieve
what we believe is the fastest published top-down BFS algorithm on the version
of Xeon Phi used in our experiments. The vectorized BFS top-down source code
presented in this paper can be available on request as free-to-use software
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