VS
V.M. Sima
Authored
20 records found
Comparative Analysis of System-Level Acceleration Techniques in Bioinformatics
A Case Study of Accelerating the Smith-Waterman Algorithm for BWA-MEM
Bioinformatics workloads are characterized by huge data sets and complex algorithms, requiring enormous data processing and making high performance heterogeneous computation platforms such as FPGAs and GPUs highly relevant. We compare three accelerated implementations of the wide
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We present our work on hardware accelerated genomics pipelines, using either FPGAs or GPUs to accelerate execution of BWA-MEM, a widely-used algorithm for genomic short read mapping. The mapping stage can take up to 40% of overall processing time for genomics pipelines. Our imple
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We are rapidly entering the era of genomics. The dramatic cost reduction of DNA sequencing due to the introduction of Next Generation Sequencing (NGS) techniques has resulted in an exponential growth of genetics data. The amount of data generated, and its associated processing in
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We present the first accelerated implementation of BWA-MEM, a popular genome sequence alignment algorithm widely used in next generation sequencing genomics pipelines. The Smith-Waterman-like sequence alignment kernel requires a significant portion of overall execution time. We p
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Next Generation Sequencing techniques have dramatically reduced the cost of sequencing genetic material, resulting in huge amounts of data being sequenced. The processing of this data poses huge challenges, both from a performance perspective, as well as from a power-efficiency p
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Many DNA sequence analysis tools have been developed to turn the massive raw DNA sequencing data generated by NGS (Next Generation Sequencing) platforms into biologically meaningful information. The pair-HMMs forward algorithm is widely used to calculate the overall alignment pro
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The Smith-Waterman algorithm is widely used in bioinformatics and is often used as a benchmark of FPGA performance. Here we present our highly optimized Smith- Waterman implementation on Intel FPGAs using OpenCL.
Our implementation is both faster and more efficient than other cur
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The fast decrease in cost of DNA sequencing has resulted in an enormous growth in available genome data, and hence led to an increasing demand for fast DNA analysis algorithms used for diagnostics of genetic disorders, such as cancer. One of the most computationally intensive ste
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We propose a novel FPGA-accelerated BWA-MEM implementation, a popular tool for genomic data mapping. The performance and power-efficiency of the FPGA implementation on the single Xilinx Virtex-7 Alpha Data add-in card is compared
against a software-only baseline system. By offloa
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Next Generation Sequencing techniques have resulted in an exponential growth in the generation of genetics data, the amount of which will soon rival, if not overtake, other Big Data elds, such as astronomy and streaming video services.
To become useful, this data requires process
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High-level synthesis (HLS) is increasingly popular for the design of high-performance and energy-efficient heterogeneous systems, shortening time-to-market and addressing today’s system complexity. HLS allows designers to work at a higherlevel of abstraction by using a software p
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