Fine-grained parallel RNAalifold algorithm for RNA secondary structure prediction on FPGA

<p>Abstract</p> <p>Background</p> <p>In the field of RNA secondary structure prediction, the RNAalifold algorithm is one of the most popular methods using free energy minimization. However, general-purpose computers including parallel computers or multi-core computers exhibit parallel efficiency of no more than 50%. Field Programmable Gate-Array (FPGA) chips provide a new approach to accelerate RNAalifold by exploiting fine-grained custom design.</p> <p>Results</p> <p>RNAalifold shows complicated data dependences, in which the dependence distance is variable, and the dependence direction is also across two dimensions. We propose a systolic array structure including one master Processing Element (PE) and multiple slave PEs for fine grain hardware implementation on FPGA. We exploit data reuse schemes to reduce the need to load energy matrices from external memory. We also propose several methods to reduce energy table parameter size by 80%.</p> <p>Conclusion</p> <p>To our knowledge, our implementation with 16 PEs is the only FPGA accelerator implementing the complete RNAalifold algorithm. The experimental results show a factor of 12.2 speedup over the RNAalifold (<it>ViennaPackage </it>– 1.6.5) software for a group of aligned RNA sequences with 2981-residue running on a Personal Computer (PC) platform with Pentium 4 2.6 GHz CPU.</p

A Comparative Taxonomy of Parallel Algorithms for RNA Secondary Structure Prediction

RNA molecules have been discovered playing crucial roles in numerous biological and medical procedures and processes. RNA structures determination have become a major problem in the biology context. Recently, computer scientists have empowered the biologists with RNA secondary structures that ease an understanding of the RNA functions and roles. Detecting RNA secondary structure is an NP-hard problem, especially in pseudoknotted RNA structures. The detection process is also time-consuming; as a result, an alternative approach such as using parallel architectures is a desirable option. The main goal in this paper is to do an intensive investigation of parallel methods used in the literature to solve the demanding issues, related to the RNA secondary structure prediction methods. Then, we introduce a new taxonomy for the parallel RNA folding methods. Based on this proposed taxonomy, a systematic and scientific comparison is performed among these existing methods

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

Parallelization of dynamic programming recurrences in computational biology

The rapid growth of biosequence databases over the last decade has led to a performance bottleneck in the applications analyzing them. In particular, over the last five years DNA sequencing capacity of next-generation sequencers has been doubling every six months as costs have plummeted. The data produced by these sequencers is overwhelming traditional compute systems. We believe that in the future compute performance, not sequencing, will become the bottleneck in advancing genome science. In this work, we investigate novel computing platforms to accelerate dynamic programming algorithms, which are popular in bioinformatics workloads. We study algorithm-specific hardware architectures that exploit fine-grained parallelism in dynamic programming kernels using field-programmable gate arrays: FPGAs). We advocate a high-level synthesis approach, using the recurrence equation abstraction to represent dynamic programming and polyhedral analysis to exploit parallelism. We suggest a novel technique within the polyhedral model to optimize for throughput by pipelining independent computations on an array. This design technique improves on the state of the art, which builds latency-optimal arrays. We also suggest a method to dynamically switch between a family of designs using FPGA reconfiguration to achieve a significant performance boost. We have used polyhedral methods to parallelize the Nussinov RNA folding algorithm to build a family of accelerators that can trade resources for parallelism and are between 15-130x faster than a modern dual core CPU implementation. A Zuker RNA folding accelerator we built on a single workstation with four Xilinx Virtex 4 FPGAs outperforms 198 3 GHz Intel Core 2 Duo processors. Furthermore, our design running on a single FPGA is an order of magnitude faster than competing implementations on similar-generation FPGAs and graphics processors. Our work is a step toward the goal of automated synthesis of hardware accelerators for dynamic programming algorithms

Throughput-optimal systolic arrays from recurrence equations

Many compute-bound software kernels have seen order-of-magnitude speedups on special-purpose accelerators built on specialized architectures such as field-programmable gate arrays (FPGAs). These architectures are particularly good at implementing dynamic programming algorithms that can be expressed as systems of recurrence equations, which in turn can be realized as systolic array designs. To efficiently find good realizations of an algorithm for a given hardware platform, we pursue software tools that can search the space of possible parallel array designs to optimize various design criteria. Most existing design tools in this area produce a design that is latency-space optimal. However, we instead wish to target applications that operate on a large collection of small inputs, e.g. a database of biological sequences. For such applications, overall throughput rather than latency per input is the most important measure of performance. In this work, we introduce a new procedure to optimize throughput of a systolic array subject to resource constraints, in this case the area and bandwidth constraints of an FPGA device. We show that the throughput of an array is dependent on the maximum number of lattice points executed by any processor in the array, which to a close approximation is determined solely by the array’s projection vector. We describe a bounded search process to find throughput-optimal projection vectors and a tool to perform automated design space exploration, discovering a range of array designs that are optimal for inputs of different sizes. We apply our techniques to the Nussinov RNA folding algorithm to generate multiple mappings of this algorithm into systolic arrays. By combining our library of designs with run-time reconfiguration of an FPGA device to dynamically switch among them, we predict significant speedup over a single, latency-space optimal array

Fine-grained parallelization of fitness functions in bioinformatics optimization problems: gene selection for cancer classification and biclustering of gene expression data

ANTECEDENTES: las metaheurísticas se utilizan ampliamente para resolver grandes problemas de optimización combinatoria en bioinformática debido al enorme conjunto de posibles soluciones. Dos problemas representativos son la selección de genes para la clasificación del cáncer y el agrupamiento de los datos de expresión génica. En la mayoría de los casos, estas metaheurísticas, así como otras técnicas no lineales, aplican una función de adecuación a cada solución posible con una población de tamaño limitado, y ese paso involucra latencias más altas que otras partes de los algoritmos, lo cual es la razón por la cual el tiempo de ejecución de las aplicaciones dependerá principalmente del tiempo de ejecución de la función de aptitud. Además, es habitual encontrar formulaciones aritméticas de punto flotante para las funciones de fitness. De esta manera, una paralelización cuidadosa de estas funciones utilizando la tecnología de hardware reconfigurable acelerará el cálculo, especialmente si se aplican en paralelo a varias soluciones de la población. RESULTADOS: una paralelización de grano fino de dos funciones de aptitud de punto flotante de diferentes complejidades y características involucradas en el biclustering de los datos de expresión génica y la selección de genes para la clasificación del cáncer permitió obtener mayores aceleraciones y cómputos de potencia reducida con respecto a los microprocesadores habituales. CONCLUSIONES: Los resultados muestran mejores rendimientos utilizando tecnología de hardware reconfigurable en lugar de los microprocesadores habituales, en términos de tiempo de consumo y consumo de energía, no solo debido a la paralelización de las operaciones aritméticas, sino también gracias a la evaluación de aptitud concurrente para varios individuos de la población en La metaheurística. Esta es una buena base para crear soluciones aceleradas y de bajo consumo de energía para escenarios informáticos intensivos.BACKGROUND: Metaheuristics are widely used to solve large combinatorial optimization problems in bioinformatics because of the huge set of possible solutions. Two representative problems are gene selection for cancer classification and biclustering of gene expression data. In most cases, these metaheuristics, as well as other non-linear techniques, apply a fitness function to each possible solution with a size-limited population, and that step involves higher latencies than other parts of the algorithms, which is the reason why the execution time of the applications will mainly depend on the execution time of the fitness function. In addition, it is usual to find floating-point arithmetic formulations for the fitness functions. This way, a careful parallelization of these functions using the reconfigurable hardware technology will accelerate the computation, specially if they are applied in parallel to several solutions of the population. RESULTS: A fine-grained parallelization of two floating-point fitness functions of different complexities and features involved in biclustering of gene expression data and gene selection for cancer classification allowed for obtaining higher speedups and power-reduced computation with regard to usual microprocessors. CONCLUSIONS: The results show better performances using reconfigurable hardware technology instead of usual microprocessors, in computing time and power consumption terms, not only because of the parallelization of the arithmetic operations, but also thanks to the concurrent fitness evaluation for several individuals of the population in the metaheuristic. This is a good basis for building accelerated and low-energy solutions for intensive computing scenarios.• Ministerio de Economía y Competitividad y Fondos FEDER. Contrato TIN2012-30685 (I+D+i) • Gobierno de Extremadura. Ayuda GR15011 para grupos TIC015 • CONICYT/FONDECYT/REGULAR/1160455. Beca para Ricardo Soto Guzmán • CONICYT/FONDECYT/REGULAR/1140897. Beca para Broderick CrawfordpeerReviewe