Showing papers by "Francesco Silvestri published in 2010"
19 Apr 2010
TL;DR: This work introduces a multicore-oblivious (MO) approach to algorithms and schedulers for HM, and presents efficient MO algorithms for several fundamental problems including matrix transposition, FFT, sorting, the Gaussian Elimination Paradigm, list ranking, and connected components.
Abstract: We address the design of algorithms for multicores that are oblivious to machine parameters. We propose HM, a multicore model consisting of a parallel shared-memory machine with hierarchical multi-level caching, and we introduce a multicore-oblivious (MO) approach to algorithms and schedulers for HM. An MO algorithm is specified with no mention of any machine parameters, such as the number of cores, number of cache levels, cache sizes and block lengths. However, it is equipped with a small set of instructions that can be used to provide hints to the run-time scheduler on how to schedule parallel tasks. We present efficient MO algorithms for several fundamental problems including matrix transposition, FFT, sorting, the Gaussian Elimination Paradigm, list ranking, and connected components. The notion of an MO algorithm is complementary to that of a network-oblivious (NO) algorithm, recently introduced by Bilardi et al. for parallel distributed-memory machines where processors communicate point-to-point. We show that several of our MO algorithms translate into efficient NO algorithms, adding to the body of known efficient NO algorithms.
70 citations
TL;DR: This work proposes a novel data structure for LC-MS datasets, called mzRTree, which embodies a scalable index based on the R-tree data structure, which can be efficiently created from the XML-based data formats and suitable for handling very large datasets.
Abstract: As an emerging field, MS-based proteomics still requires software tools for efficiently storing and accessing experimental data. In this work, we focus on the management of LC-MS data, which are typically made available in standard XML-based portable formats. The structures that are currently employed to manage these data can be highly inefficient, especially when dealing with high-throughput profile data. LC-MS datasets are usually accessed through 2D range queries. Optimizing this type of operation could dramatically reduce the complexity of data analysis. We propose a novel data structure for LC-MS datasets, called mzRTree, which embodies a scalable index based on the R-tree data structure. mzRTree can be efficiently created from the XML-based data formats and it is suitable for handling very large datasets. We experimentally show that, on all range queries, mzRTree outperforms other known structures used for LC-MS data, even on those queries these structures are optimized for. Besides, mzRTree is also more space efficient. As a result, mzRTree reduces data analysis computational costs for very large profile datasets.
6 citations
TL;DR: In this paper, a scalable index based on the R-tree data structure is proposed for MS-based proteomics data, called mzRTree, which can be efficiently created from the XML-based data formats and is suitable for handling very large datasets.
6 citations
24 Feb 2010
TL;DR: This paper proposes a novel framework for the dynamic allocation of jobs in grid-like environments, in which such jobs are dispatched to the machines of the grid by a centralized scheduler, applying a new, full resource-driven approach to the scheduling task.
Abstract: In this paper we propose a novel framework for the dynamic allocation of jobs in grid-like environments, in which such jobs are dispatched to the machines of the grid by a centralized scheduler. We apply a new, full resource-driven approach to the scheduling task: jobs are allocated and (possibly) relocated on the basis of the matching between their resource requirements and the characteristics of the machines in the grid. We provide experimental evidence that our approach effectively exploits the computational resources at hand, successfully keeping the completion time of the jobs low, even without having knowledge of the actual running times of the jobs.
2 citations