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Background: Accurate and efficient RNA secondary structure prediction remains an important open problem in computational molecular biology. Historically, advances in computing technology have enabled faster and more accurate RNA secondary structure predictions. Previous parallelized prediction programs achieved significant improvements in runtime, but their implementations were not portable from niche high-performance computers or easily accessible to most RNA researchers. With the increasing prevalence of multi-core desktop machines, a new parallel prediction program is needed to take full advantage of today's computing technology., Findings: We present here the first implementation of RNA secondary structure prediction by thermodynamic optimization for modern multi-core computers. We show that GTfold predicts secondary structure in less time than UNAfold and RNAfold, without sacrificing accuracy, on machines with four or more cores., Conclusions: GTfold supports advances in RNA structural biology by reducing the timescales for secondary structure prediction. The difference will be particularly valuable to researchers working with lengthy RNA sequences, such as RNA viral genomes.

Maximum parsimony (MP) methods aim to reconstruct the phylogeny of extant species by finding the most parsimonious evolutionary scenario using the species' genome data. MP methods are considered to be accurate, but they are also computationally expensive especially for a large number of species. Several disk-covering methods (DCMs), which decompose the input species to multiple overlapping subgroups (or disks), have been proposed to solve the problem in a divide-and-conquer way. We design a new DCM based on the spectral method and also develop the COGNAC (Comparing Orders of Genes using Novel Algorithms and high-performance Computers) software package. COGNAC uses the new DCM to reduce the phylogenetic tree search space and selects an output tree from the reduced search space based on the MP principle. We test the new DCM using gene order data and inversion distance. The new DCM not only reduces the number of candidate tree topologies but also excludes erroneous tree topologies which can be selected by original MP methods. Initial labeling of internal genomes affects the accuracy of MP methods using gene order data, and the new DCM enables more accurate initial labeling as well. COGNAC demonstrates superior accuracy as a consequence. We compare COGNAC with FastME and the combination of the state of the art DCM (Rec-I-DCM3) and GRAPPA. COGNAC clearly outperforms FastME in accuracy. COGNAC--using the new DCM--also reconstructs a much more accurate tree in significantly shorter time than GRAPPA with Rec-I-DCM3.

The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard (istcbigdata.org/GraphBlas) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.

Background Diagnosis of late onset sepsis (LOS) in very low birth weight (VLBW) preterm infants relies mainly on clinical suspicion, whereas prognosis depends on early initiation of antibiotic treatment. RALIS is a mathematical algorithm for early detection of LOS incorporating six vital signs measured every 2 hours. Objective The aim of this study is to study RALIS ability to detect LOS before clinical suspicion. Study Design A total of 118 VLBWpreterm infants (gestational age < 33 weeks, birth weight < 1,500 g) were enrolled in a prospective multicentered study. Vital signs were recorded prospectively up to day 21 of life in a blinded manner, with no effect on standard care. The primary end point was comparison of the rates and timing of detection of LOS between RALIS and clinical/culture evidence of LOS. Results Of the 2,174 monitoring days, RALIS indicated sepsis in 590 days, and LOS was positively diagnosed in 229 days. Sensitivity, specificity, positive, and negative predictive values were 74.6, 80.7, 38.8, and 95.1%, respectively. RALIS provided an indication for sepsis 3 days on the average before clinical suspicion. Conclusion RALIS has a promising potential as an easy to implement noninvasive early indicator of LOS, especially for ruling out LOS in VLBW high-risk infants. [ABSTRACT FROM AUTHOR]

In this paper, we present a GPU-based sorting algorithm, GPUMemSort, which achieves high performance in sorting large-scale in-memory data by take advantage of GPU processors. It consists of two algorithms: an in-core algorithm, which is responsible for sorting data in GPU global memory efficiently, and an out-of-core algorithm, which is responsible for dividing large-scale data into multiple chunks that fit GPU global memory. GPUMemSort is implemented based on NVIDIA's CUDA framework and some critical and detailed optimization methods are also presented. The tests of different algorithms have been run on multiple data sets. The experimental results show that our in-core sorting can outperform other comparison-based algorithms and GPUMemSort is highly effective in sorting large-scale in- memory data. [ABSTRACT FROM AUTHOR]

Abstract: Discrete transforms are of primary importance and fundamental kernels in many computationally intensive scientific applications. In this paper, we investigate the performance of two such algorithms; Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT), on the Sony–Toshiba–IBM Cell Broadband Engine (Cell/B.E.), a heterogeneous multicore chip architected for intensive gaming applications and high performance computing. We design an efficient parallel implementation of Fast Fourier Transform (FFT) to fully exploit the architectural features of the Cell/B.E. Our FFT algorithm uses an iterative out-of-place approach and for 1K to 16K complex input samples outperforms all other parallel implementations of FFT on the Cell/B.E. including FFTW. Our FFT implementation obtains a single-precision performance of 18.6 GFLOP/s on the Cell/B.E., outperforming Intel Duo Core (Woodcrest) for inputs of greater than 2K samples. We also optimize Discrete Wavelet Transform (DWT) in the context of JPEG2000 for the Cell/B.E. DWT has an abundant parallelism, however, due to the low temporal locality of the algorithm, memory bandwidth becomes a significant bottleneck in achieving high performance. We introduce a novel data decomposition scheme to achieve highly efficient DMA data transfer and vectorization with low programming complexity. Also, we merge the multiple steps in the algorithm to reduce the bandwidth requirement. This leads to a significant enhancement in the scalability of the implementation. Our optimized implementation of DWT demonstrates 34 and 56 times speedup using one Cell/B.E. chip to the baseline code for the lossless and lossy transforms, respectively. We also provide the performance comparison with the AMD Barcelona (Quad-core Opteron) processor, and the Cell/B.E. excels the AMD Barcelona processor. This highlights the advantage of the Cell/B.E. over general purpose multicore processors in processing regular and bandwidth intensive scientific applications. [Copyright &y& Elsevier]

Abstract: Minimum spanning tree (MST) is one of the most studied combinatorial problems with practical applications in VLSI layout, wireless communication, and distributed networks, recent problems in biology and medicine such as cancer detection, medical imaging, and proteomics, and national security and bioterrorism such as detecting the spread of toxins through populations in the case of biological/chemical warfare. Most of the previous attempts for improving the speed of MST using parallel computing are too complicated to implement or perform well only on special graphs with regular structure. In this paper we design and implement four parallel MST algorithms (three variations of Borůvka plus our new approach) for arbitrary sparse graphs that for the first time give speedup when compared with the best sequential algorithm. In fact, our algorithms also solve the minimum spanning forest problem. We provide an experimental study of our algorithms on symmetric multiprocessors such as IBMs pSeries and Sun''s Enterprise servers. Our new implementation achieves good speedups over a wide range of input graphs with regular and irregular structures, including the graphs used by previous parallel MST studies. For example, on an arbitrary random graph with vertices and edges, our new approach achieves a speedup of 5 using 8 processors. The source code for these algorithms is freely available from our web site. [Copyright &y& Elsevier]

Abstract: The ability to provide uniform shared-memory access to a significant number of processors in a single SMP node brings us much closer to the ideal PRAM parallel computer. Many PRAM algorithms can be adapted to SMPs with few modifications. Yet there are few studies that deal with the implementation and performance issues of running PRAM-style algorithms on SMPs. Our study in this paper focuses on implementing parallel spanning tree algorithms on SMPs. Spanning tree is an important problem in the sense that it is the building block for many other parallel graph algorithms and also because it is representative of a large class of irregular combinatorial problems that have simple and efficient sequential implementations and fast PRAM algorithms, but these irregular problems often have no known efficient parallel implementations. Experimental studies have been conducted on related problems (minimum spanning tree and connected components) using parallel computers, but only achieved reasonable speedup on regular graph topologies that can be implicitly partitioned with good locality features or on very dense graphs with limited numbers of vertices. In this paper we present a new randomized algorithm and implementation with superior performance that for the first time achieves parallel speedup on arbitrary graphs (both regular and irregular topologies) when compared with the best sequential implementation for finding a spanning tree. This new algorithm uses several techniques to give an expected running time that scales linearly with the number p of processors for suitably large inputs (). As the spanning tree problem is notoriously hard for any parallel implementation to achieve reasonable speedup, our study may shed new light on implementing PRAM algorithms for shared-memory parallel computers. The main results of this paper are [1.] A new and practical spanning tree algorithm for symmetric multiprocessors that exhibits parallel speedups on graphs with regular and irregular topologies; and [2.] an experimental study of parallel spanning tree algorithms that reveals the superior performance of our new approach compared with the previous algorithms. The source code for these algorithms is freely-available from our web site. [Copyright &y& Elsevier]

Data from emerging applications, such as cybersecurity and social networking, can be abstracted as graphs whose edges are updated sequentially in the form of a stream. The challenging problem of interactive graph stream analytics is the quick response of the queries on terabyte and beyond graph stream data from end users. In this paper, a succinct and efficient double index data structure is designed to build the sketch of a graph stream to meet general queries. A single pass stream model, which includes general sketch building, distributed sketch based analysis algorithms and regression based approximation solution generation, is developed, and a typical graph algorithm—triangle counting—is implemented to evaluate the proposed method. Experimental results on power law and normal distribution graph streams show that our method can generate accurate results (mean relative error less than 4%) with a high performance. All our methods and code have been implemented in an open source framework, Arkouda, and are available from our GitHub repository, Bader-Research. This work provides the large and rapidly growing Python community with a powerful way to handle terabyte and beyond graph stream data using their laptops. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including papers from the 14th Meeting on Algorithm Engineering and Experiments (ALENEX 12), graph clustering and user-constrained multimodal route planning.

The recent switch to multicore processors brought a dramatic change that affects a large spectrum of systems from embedded and general-purpose to high-end computing systems. Parallelism is forcing major changes in software development. The aim of this issue is to discuss the challenges that parallelism brings to the design and implementation of algorithms and data structures. This special issue arose out of discussions held at the Dagstuhl Seminar 10261, on Algorithm Engineering held June 27--July 2, 2010, in Germany, and organized by Giuseppe F. Italiano (Università di Roma “Tor Vergata,” Italy), David S. Johnson (AT&T Research, Florham Park, NJ), Petra Mutzel (Technical University of Dortmund, Germany), and Peter Sanders (Karlsruhe Institute of Technology, Germany). We conceived a special issue of the ACM Journal on Experimental Algorithmics with a call for original submissions that address implementation and performance issues of multicore algorithms and data structures for any multicore processor, for example, Intel Nehalem, Single-Chip Cloud, NVIDIA and AMD GPUs. An experimental study typically includes an implementation, a series of experiments designed to understand the behavior of the algorithm(s) under study, and a critical discussion of the experiments and their results. We welcomed experimental submissions and encouraged authors to include test data from previously published studies to enable critical comparisons. A total of nine submissions were received, and four were accepted for this special issue. All manuscripts had at least three extensive reviews, and most received five to six reviews. We thank all of the authors for their submissions, and especially the 16 reviewers of these manuscripts. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we address the problem of multiple sequence alignment (MSA) for handling very large number of proteins sequences on mesh-based multiprocessor architectures. As the problem has been conclusively shown to be computationally complex, we employ divisible load paradigm (also, referred to as divisible load theory, DLT) to handle such large number of sequences. We design an efficient computational engine that is capable of conducting MSAs by exploiting the underlying parallelism embedded in the computational steps of multiple sequence algorithms. Specifically, we consider the standard Smith–Waterman (SW) algorithm in our implementation, however, our approach is by no means restrictive to SW class of algorithms alone. The treatment used in this paper is generic to a class of similar dynamic programming problems. Our approach is recursive in the sense that the quality of solutions can be refined continuously till an acceptable level of quality is achieved. After first phase of computation, we design a heuristic scheme that renders the final solution for MSA. We conduct rigorous simulation experiments using several hundreds of homologous protein sequences derived from the Rattus Norvegicus and Mus Musculus databases of olfactory receptors. We quantify the performance based on speed-up metric. We compare our algorithms to serial or single machine processing approaches. We testify our findings by comparing with conventional equal load partitioning (ELP) strategy that is commonly used in the parallel processing literature. Based on our extensive simulation study, we observe that DLT paradigm offers an excellent speed-up characteristics and provides avenues for its use in several other biological sequence processing related problem. This study is a first time attempt in using the DLT paradigm to devise efficient strategies to handle large scale multiple protein sequence alignment problem on mesh-based multiprocessor systems. [Copyright &y& Elsevier]