Applications on emerging paradigms in parallel computing

The area of computing is seeing parallelism increasingly being incorporated at various levels: from the lowest levels of vector processing units following Single Instruction Multiple Data (SIMD) processing, Simultaneous Multi-threading (SMT) architectures, and multi/many-cores with thread-level shared memory and SIMT parallelism, to the higher levels of distributed memory parallelism as in supercomputers and clusters, and scaling them to large distributed systems as server farms and clouds. All together these form a large hierarchy of parallelism. Developing high-performance parallel algorithms and efficient software tools, which make use of the available parallelism, is inevitable in order to harness the raw computational power these emerging systems have to offer. In the work presented in this thesis, we develop architecture-aware parallel techniques on such emerging paradigms in parallel computing, specifically, parallelism offered by the emerging multi- and many-core architectures, as well as the emerging area of cloud computing, to target large scientific applications. First, we develop efficient parallel algorithms to compute optimal pairwise alignments of genomic sequences on heterogeneous multi-core processors, and demonstrate them on the IBM Cell Broadband Engine. Then, we develop parallel techniques for scheduling all-pairs computations on heterogeneous systems, including clusters of Cell processors, and NVIDIA graphics processors. We compare the performance of our strategies on Cell, GPU and Intel Nehalem multi-core processors. Further, we apply our algorithms to specific applications taken from the areas of systems biology, fluid dynamics and materials science: pairwise Mutual Information computations for reconstruction of gene regulatory networks; pairwise Lp-norm distance computations for coherent structures discovery in the design of flapping-wing Micro Air Vehicles, and construction of stochastic models for a set of properties of heterogeneous materials. Lastly, in the area of cloud computing, we propose and develop an abstract framework to enable computations in parallel on large tree structures, to facilitate easy development of a class of scientific applications based on trees. Our framework, in the style of Google\u27s MapReduce paradigm, is based on two generic user-defined functions through which a user writes an application. We implement our framework as a generic programming library for a large cluster of homogeneous multi-core processor, and demonstrate its applicability through two applications: all-k-nearest neighbors computations, and Fast Multipole Method (FMM) based simulations

Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets

<p>Abstract</p> <p>Background</p> <p>Solving bioinformatics tasks often requires extensive computational power. Recent trends in processor architecture combine multiple cores into a single chip to improve overall performance. The Cell Broadband Engine (CBE), a heterogeneous multi-core processor, provides power-efficient and cost-effective high-performance computing. One application area is image analysis and visualisation, in particular registration of 2D cross-sections into 3D image datasets. Such techniques can be used to put different image modalities into spatial correspondence, for example, 2D images of histological cuts into morphological 3D frameworks.</p> <p>Results</p> <p>We evaluate the CBE-driven PlayStation 3 as a high performance, cost-effective computing platform by adapting a multimodal alignment procedure to several characteristic hardware properties. The optimisations are based on partitioning, vectorisation, branch reducing and loop unrolling techniques with special attention to 32-bit multiplies and limited local storage on the computing units. We show how a typical image analysis and visualisation problem, the multimodal registration of 2D cross-sections and 3D datasets, benefits from the multi-core based implementation of the alignment algorithm. We discuss several CBE-based optimisation methods and compare our results to standard solutions. More information and the source code are available from <url>http://cbe.ipk-gatersleben.de</url>.</p> <p>Conclusions</p> <p>The results demonstrate that the CBE processor in a PlayStation 3 accelerates computational intensive multimodal registration, which is of great importance in biological/medical image processing. The PlayStation 3 as a low cost CBE-based platform offers an efficient option to conventional hardware to solve computational problems in image processing and bioinformatics.</p

PLAST: parallel local alignment search tool for database comparison

Background: Sequence similarity searching is an important and challenging task in molecular biology and next-generation sequencing should further strengthen the need for faster algorithms to process such vast amounts of data. At the same time, the internal architecture of current microprocessors is tending towards more parallelism, leading to the use of chips with two, four and more cores integrated on the same die. The main purpose of this work was to design an effective algorithm to fit with the parallel capabilities of modern microprocessors. Results: A parallel algorithm for comparing large genomic banks and targeting middle-range computers has been developed and implemented in PLAST software. The algorithm exploits two key parallel features of existing and future microprocessors: the SIMD programming model (SSE instruction set) and the multithreading concept (multicore). Compared to multithreaded BLAST software, tests performed on an 8-processor server have shown speedup ranging from 3 to 6 with a similar level of accuracy. Conclusions: A parallel algorithmic approach driven by the knowledge of the internal microprocessor architecture allows significant speedup to be obtained while preserving standard sensitivity for similarity search problems.

Revisiting the Speed-versus-Sensitivity Tradeoff in Pairwise Sequence Search

The Smith-Waterman algorithm is a dynamic programming method for determining optimal local alignments between nucleotide or protein sequences. However, it suffers from quadratic time and space complexity. As a result, many algorithmic and architectural enhancements have been proposed to solve this problem, but at the cost of reduced sensitivity in the algorithms or significant expense in hardware, respectively. Hence, there exists a need to evaluate the tradeoffs between the different solutions. This motivation, coupled with the lack of an evaluation metric to quantify these tradeoffs leads us to formally define and quantify the sensitivity of homology search methods so that tradeoffs between sequence-search solutions can be evaluated in a quantitative manner. As an example, though the BLAST algorithm executes significantly faster than Smith-Waterman, we find that BLAST misses 80% of the significant sequence alignments. This paper then presents a highly efficient parallelization of the Smith-Waterman algorithm on the Cell Broadband Engine, a novel hybrid multicore architecture that drives the PlayStation 3 (PS3) game consoles, and emulates BLAST by repeatedly executing the parallelized Smith-Waterman algorithm to search for a query in a given sequence database. Through an innovative mapping of the optimal Smith-Waterman algorithm onto a cluster of PlayStation 3 nodes, our implementation delivers a 10-fold speed-up over a high-end multicore architecture and an 88-fold speed-up over a non-accelerated PS3. Finally, we compare the performance of our implementation of the Smith-Waterman algorithm to that of BLAST and the canonical Smith-Waterman implementation, based on a combination of three factors — execution time (speed), sensitivity, and the actual cost of de-ploying each solution. In the end, our parallelized Smith-Waterman algorithm approaches the speed of BLAST while maintaining ideal sensitivity and achieving low cost through the use of PlayStation 3 game consoles

Accelerated large-scale multiple sequence alignment

<p>Abstract</p> <p>Background</p> <p>Multiple sequence alignment (MSA) is a fundamental analysis method used in bioinformatics and many comparative genomic applications. Prior MSA acceleration attempts with reconfigurable computing have only addressed the first stage of progressive alignment and consequently exhibit performance limitations according to Amdahl's Law. This work is the first known to accelerate the third stage of progressive alignment on reconfigurable hardware.</p> <p>Results</p> <p>We reduce subgroups of aligned sequences into discrete profiles before they are pairwise aligned on the accelerator. Using an FPGA accelerator, an overall speedup of up to 150 has been demonstrated on a large data set when compared to a 2.4 GHz Core2 processor.</p> <p>Conclusions</p> <p>Our parallel algorithm and architecture accelerates large-scale MSA with reconfigurable computing and allows researchers to solve the larger problems that confront biologists today. Program source is available from <url>http://dna.cs.byu.edu/msa/</url>.</p

Manycore high-performance computing in bioinformatics

Mining the increasing amount of genomic data requires having very efficient tools. Increasing the efficiency can be obtained with better algorithms, but one could also take advantage of the hardware itself to reduce the application runtimes. Since a few years, issues with heat dissipation prevent the processors from having higher frequencies. One of the answers to maintain Moore's Law is parallel processing. Grid environments provide tools for effective implementation of coarse grain parallelization. Recently, another kind of hardware has attracted interest: multicore processors. Graphic processing units (GPUs) are a first step towards massively multicore processors. They allow everyone to have some teraflops of cheap computing power in its personal computer. The CUDA library (released in 2007) and the new standard OpenCL (specified in 2008) make programming of such devices very convenient. OpenCL is likely to gain a wide industrial support and to become a standard of choice for parallel programming. In all cases, the best speedups are obtained when combining precise algorithmic studies with a knowledge of the computing architectures. This is especially true with the memory hierarchy: the algorithms have to find a good balance between using large (and slow) global memories and some fast (but small) local memories. In this chapter, we will show how those manycore devices enable more efficient bioinformatics applications. We will first give some insights into architectures and parallelism. Then we will describe recent implementations specifically designed for manycore architectures, including algorithms on sequence alignment and RNA structure prediction. We will conclude with some thoughts about the dissemination of those algorithms and implementations: are they today available on the bookshelf for everyone

Smith-Waterman Protein Search with OpenCL on an FPGA

The well-known Smith-Waterman (SW) algorithm is a high-sensitivity method for local alignments. Unfortunately, SW is expensive in terms of both execution time and memory usage, which makes it impractical in many scenarios. Previous research has shown that massively parallel architectures such as GPUs and FPGAs are able to mitigate the computational problems and achieve impressive speedups. In this paper we explore SW acceleration on an FPGA with OpenCL. We efficiently exploit data and thread-level parallelism on an Altera Stratix V FPGA, obtaining up to 39 GCUPS with less than 25 watt of power consumption.Facultad de Informátic

Smith-Waterman Protein Search with OpenCL on an FPGA

The well-known Smith-Waterman (SW) algorithm is a high-sensitivity method for local alignments. Unfortunately, SW is expensive in terms of both execution time and memory usage, which makes it impractical in many scenarios. Previous research has shown that massively parallel architectures such as GPUs and FPGAs are able to mitigate the computational problems and achieve impressive speedups. In this paper we explore SW acceleration on an FPGA with OpenCL. We efficiently exploit data and thread-level parallelism on an Altera Stratix V FPGA, obtaining up to 39 GCUPS with less than 25 watt of power consumption.Facultad de Informátic