Targeting the development of new biochip platforms capable of autonomously sequencing and aligning biological sequences, a new multicore processing structure is proposed in this manuscript. This multicore structure makes use of a shared memory model and multiple instantiations of a novel application-specific instruction-set processor (ASIP) to simultaneously exploit both fine and coarse-grained parallelism and to achieve high performance levels at low-power consumption. The proposed ASIP is built by extending the instruction set architecture of a synthesizable processor, including both general and special-purpose single-instruction multiple-data instructions. This allows an efficient exploitation of fine-grained parallelism on the alignment of biological sequences, achieving over $30\times $ speedup when compared with sequential algorithmic implementations. The complete system was prototyped on different field-programmable gate array platforms and synthesized with a 90-nm CMOS process technology. Experimental results demonstrate that the multicore structure scales almost linearly with the number of instantiated cores, achieving performances similar to a quad-core Intel Core i7 3820 processor, while using $25\times $ less energy.

Multicore SIMD ASIP for Next-Generation Sequencing and Alignment Biochip Platforms

Biological sequence comparison is one of the most important tasks in Bioinformatics Due to the growth of biological databases, sequence comparison is becoming an important challenge for high performance computing, especially when very long sequences are compared The Smith-Waterman (SW) algorithm is an exact method based on dynamic programming to quantify local similarity between sequences The inherent large parallelism of the algorithm makes it ideal for architectures supporting multiple dimensions of parallelism (TLP, DLP and ILP) In this work, we show how long sequences comparison takes advantage of current and future multicore architectures We analyze two different SW implementations on the CellBE and use simulation tools to study the performance scalability in a multicore architecture We study the memory organization that delivers the maximum bandwidth with the minimum cost Our results show that a heterogeneous architecture is an valid alternative to execute challenging bioinformatic workloads

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Long DNA sequence comparison on multicore architectures

A new Application-Specific Instruction-set Processor (ASIP) architecture for biological sequences alignment is proposed in this manuscript. This architecture achieves high processing throughputs by exploiting both fine and coarse-grained parallelism. The former is achieved by extending the Instruction Set Architecture (ISA) of a synthesizable processor to include multiple specialized SIMD instructions that implement vector-vector and vector-scalar arithmetic, logic, load/store and control operations. Coarse-grained parallelism is achieved by using multiple cores to cooperatively align multiple sequences in a shared memory architecture, comprising proper hardware-specific synchronization mechanisms. To ease the programming, a compilation framework based on an adaptation of the GCC back-end was also implemented. The proposed system was prototyped and evaluated on a Xilinx Virtex-7 FPGA, achieving a 200MHz working frequency. A sequential and a state-of-theart SIMD implementations of the Smith-Waterman algorithm were programmed in both the proposed ASIP and an Intel Core i7 processor. When comparing the achieved speedups, it was observed that the proposed ISA achieves a 40x speedup, which contrasts with the 11x speedup provided by SSE2 in the Intel Core i7 processor. The scalability of the multi-core system was also evaluated and proved to scale almost linearly with the number of cores.

/pdf/bioblaze-multi-core-simd-asip-for-dna-sequence-alignment-4lpgs6j3sf.pdf

BioBlaze: Multi-core SIMD ASIP for DNA sequence alignment

Sequence alignment is a fundamental instrument in Bioinformatics. In recent years, numerous proposals have been addressing the problem of accelerating this class of applications. This, due to the rapid growth of sequence databases in combination with the high computational demands imposed by the algorithms. In this paper we focus on the analysis of the progressive alignment in ClustalW, a widely used program for performing multiple sequence alignment. We have parallelized ClustalW for the Cell processor architecture and have carefully analyzed the scalability of its different phases with both the number of cores used and the input size. Experimental results show that computing profile scores scales well up to 16 SPE cores. With the increase of the input size, profiles initialization in the PPE core becomes the predominant bottleneck.

/pdf/scalability-analysis-of-progressive-alignment-on-a-multicore-12bcjyw481.pdf

Scalability Analysis of Progressive Alignment on a Multicore

Biological sequence comparison is one of the most important tasks in Bioinformatics. Owing to the fast growth of databases that contain biological information, sequence comparison represents an important challenge for high-performance computing, especially when very long sequences are compared, i.e. the complete genome of several organisms. The Smith–Waterman (SW) algorithm is an exact method based on dynamic programming to quantify local similarity between sequences. The inherent large parallelism of the algorithm makes it ideal for architectures supporting multiple dimensions of parallelism (TLP, DLP and ILP). Concurrently, there is a paradigm shift towards chip multiprocessors in computer architecture, which offer a huge amount of potential performance that can only be exploited efficiently if applications are effectively mapped and parallelized. In this work, we analyze how large-scale biology sequence comparison takes advantage of the current and future multicore architectures. Our starting point is the performance analysis of the current multicore IBM Cell B.E. processor; we analyze two different SW implementations on the Cell B.E. Then, using simulation tools, we study the performance scalability when a many-core architecture is used for performing long DNA sequence comparison. We investigate the efficient memory organization that delivers the maximum bandwidth with the minimum cost. Our results show that a heterogeneous architecture can be an efficient alternative to execute challenging bioinformatic workloads. Copyright © 2011 John Wiley & Sons, Ltd.

Scalable multicore architectures for long DNA sequence comparison

The exponential growth of databases that contains biological information (such as protein and DNA data) demands great efforts to improve the performance of computational platforms. In this work we investigate how bioinformatics applications benefit from parallel architectures that combine different alternatives to exploit coarse- and fine-grain parallelism. As a case of analysis we study the performance behavior of the Ssearch application that implements the Smith-Waterman algorithm, which is a dynamic programing approach that explores the similarity between a pair of sequences. The inherent large parallelism of the algorithm makes it ideal for architectures supporting multiple dimensions of parallelism (TLP, DLP and ILP). We study how this algorithm can take advantage of different parallel machines like the SGI Altix, IBM Power6, Cell BE machines and MareNostrum. Our results show that a share memory architecture like the PowerPC 970MP of Marenostrum can surpass a heterogeneous machine like the current Cell BE. Our quantitative analysis includes not only a study of scalability of the performance in terms of speedup, but also includes the analysis of bottlenecks in the execution of the application. This analysis is carried out through the study of the execution phases that the application presents.

Friman Sánchez Castaño

Papers

Quantitative analysis of sequence alignment applications on multiprocessor architectures