BackgroundDue the computational complexity of sequence alignment algorithms, various accelerated solutions have been proposed to speedup this analysis. NVBIO is the only available GPU library that accelerates sequence alignment of high-throughput NGS data, but has limited performance. In this article we present GASAL2, a GPU library for aligning DNA and RNA sequences that outperforms existing CPU and GPU libraries.ResultsThe GASAL2 library provides specialized, accelerated kernels for local, global and all types of semi-global alignment. Pairwise sequence alignment can be performed with and without traceback. GASAL2 outperforms the fastest CPU-optimized SIMD implementations such as SeqAn and Parasail, as well as NVIDIA’s own GPU-based library known as NVBIO. GASAL2 is unique in performing sequence packing on GPU, which is up to 750x faster than NVBIO. Overall on Geforce GTX 1080 Ti GPU, GASAL2 is up to 21x faster than Parasail on a dual socket hyper-threaded Intel Xeon system with 28 cores and up to 13x faster than NVBIO with a query length of up to 300 bases and 100 bases, respectively. GASAL2 alignment functions are asynchronous/non-blocking and allow full overlap of CPU and GPU execution. The paper shows how to use GASAL2 to accelerate BWA-MEM, speeding up the local alignment by 20x, which gives an overall application speedup of 1.3x vs. CPU with up to 12 threads.ConclusionsThe library provides high performance APIs for local, global and semi-global alignment that can be easily integrated into various bioinformatics tools.

This paper describes a low overhead softwarebased fault tolerance approach for shared memory multicore systems. The scheme is implemented at user-space level and requires almost no changes to the original application. Redundant multithreaded processes are used to detect soft errors and recover from them. Our scheme makes sure that the execution of the redundant processes is identical even in the presence of nondeterminism due to shared memory accesses. It provides a very low overhead mechanism to achieve this. Moreover it implements a fast error detection and recovery mechanism. The overhead incurred by our approach ranges from 0% to 18% for selected benchmarks. This is lower than comparable systems published in literature.978-3-9815370-0-0/DATE13/ c �2013 EDAA

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Due to the rapid decrease in the cost of NGS (Next Generation Sequencing), interest has increased in using data generated from NGS to diagnose genetic diseases. However, the data generated by NGS technology is usually in the order of hundreds of gigabytes per experiment, thus requiring efficient and scalable programs to perform data analysis quickly. This paper presents SparkGA2, a memory efficient, production quality framework for high performance DNA analysis in the cloud, which can scale according to the available computational resources by increasing the number of nodes. Our framework uses Apache Spark’s ability to cache data in the memory to speed up processing, while also allowing the user to run the framework on systems with lower amounts of memory at the cost of slightly less performance. To manage the memory footprint, we implement an on-the-fly compression method of intermediate data and reduce memory requirements by up to 3x. Our framework also uses a streaming approach to gradually stream input data as processing is taking place. This makes our framework faster than other state of the art approaches while at the same time allowing users to adapt it to run on clusters with lower memory. As compared to the state of the art, SparkGA2 is up to 22% faster on a large big data cluster of 67 nodes and up to 9% faster on a smaller cluster of 6 nodes. Including the streaming solution, where data pre-processing is considered, SparkGA2 is 51% faster on a 6 node cluster. The source code of SparkGA2 is publicly available at https://github.com/HamidMushtaq/SparkGA2.