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 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

Alinhamento primário e secundário de sequências biológicas em arquiteturas de alto desempenho

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2017.O alinhamento múltiplo primário de sequências biológicas é um problema muito importante em Biologia Molecular, pois permite que sejam detectadas similaridades e diferenças entre um conjunto de sequências. Esse problema foi provado NP-Completo e, por essa razão, geralmente algoritmos heurísticos são usados para resolvê-lo. No entanto, a obtenção da solução ótima é bastante desejada e, por essa razão, existem alguns algoritmos exatos que solucionam esse problema para um número reduzido de sequências. As sequências de RNA, diferente do DNA, não possuem dupla-hélice e podem dobrar-se, pois seus nucleotídeos podem formar pares de bases. É conhecido na Biologia Molecular que a função dessa estrutura está ligada à sua conformação espacial, e não à composição de seus nucleotídeos. Obter a estrutura secundária (2D) de uma sequência de RNA também exige uma grande quantidade de recursos computacionais, até mesmo para um pequeno número de sequências. Desta forma, as arquiteturas de alto desempenho são muito importantes para a obtenção dos resultados em um tempo factível. A presente tese visa investigar os problemas do alinhamento múltiplo primário e do alinhamento em pares secundário, utilizando arquiteturas de alto desempenho para acelerar a obtenção de resultados. Para o alinhamento primário ótimo de múltiplas sequências, propusemos na presente Tese o PA-Star, uma estratégia multithreaded baseada no algoritmo A-Star que usa uma política sensível à localidade de atribuição de trabalho às threads. De modo a lidar com o alto uso de memória, nossa estratégia PA-Star usa tanto memória RAM como disco. Para o alinhamento estrutural (2D) de sequências de RNA, propusemos o Foldalign 2.5, que é uma estratégia multithreaded heurística baseada no algoritmo exato de Sankoff, capaz de obter o alinhamento estrutural de grandes sequências em tempo reduzido. Finalmente, propusemos o CUDA-Sankoff, que é capaz de obter o alinhamento estrutural ótimo entre duas sequências de RNA em GPU (Graphics Processing Unit).Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).The primary multiple sequence Alignment is a very important problem in Molecular Biology since it is able to detect similarities and differences in a set of sequences. This problem has been proven NP-Hard and, for this reason, heuristic algorithms are usually used to solve it. Nevertheless, obtaining the optimal solution is highly desirable and there are indeed some exact algorithms that solve this problem for a reduced number of sequences. The RNA sequences are different than the DNA, they do not have double helix, their nucleotides can form base pairs and the sequence can fold on itself. It is known in the Molecular Biology that, the function of the RNA is related to its spatial structure. Calculating the secondary structure of RNA sequences also demand a high amount of computational resources, even for a small number of sequences. The High Performance Computing (HPC) Platforms can be used in order to produce results faster. The current thesis aims to investigate the primary multiple sequence alignment and the secondary pairwise sequence alignment, using High Performance Architectures to accelerate and obtaining results in reasonable time. For the primary multiple sequence alignment, we propose the PA-Star, a multithreaded solution based on the A-Star algorithm using a locality sensitive hash to distribute the workload among the threads. Due to the high RAM memory usage required by the algorithm, our strategy can also uses disk. For the RNA structural alignment, we proposed the Foldalign 2.5, a multithreaded solution that uses heuristics to reduce the Sankoff Algorithm complexity, and can obtain the pairwise structural alignment of large sequences in reduced time. Finally, we proposed CUDASankoff, that obtains the optimal pairwise structural alignment for RNA sequences using a GPU (Graphics Processing Unit)