Bio-sequence database scanning on a GPU

Liu, Weiguo; Schmidt, Bertil; Voß, Gerrit; Schroder, Andre; Müller‐Wittig, Wolfgang

doi:10.1109/ipdps.2006.1639531

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…GPU Accelerations of Sequence Alignments. There is a line of work on accelerating sequence alignment with GPUs, using OpenGL library [25,26], or earlier version of CUDA [6,27,30,31,33]. In addition, some target single-pair alignment and utilize intra-query parallelism [10,11,46,47].…”

Section: Related Workmentioning

confidence: 99%

AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping

Park,

Hong,

Song

et al. 2024

Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

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With the advance in genome sequencing technology, the lengths of deoxyribonucleic acid (DNA) sequencing results are rapidly increasing at lower prices than ever. However, the longer lengths come at the cost of a heavy computational burden on aligning them. For example, aligning sequences to a human reference genome can take tens or even hundreds of hours. The current de facto standard approach for alignment is based on the guided dynamic programming method. Although this takes a long time and could potentially benefit from high-throughput graphic processing units (GPUs), the existing GPU-accelerated approaches often compromise the algorithm's structure, due to the GPU-unfriendly nature of the computational pattern. Unfortunately, such compromise in the algorithm is not tolerable in the field, because sequence alignment is a part of complicated bioinformatics analysis pipelines. In such circumstances, we propose AGAThA, an exact and efficient GPU-based acceleration of guided sequence alignment. We diagnose and address the problems of the algorithm being unfriendly to GPUs, which comprises strided/redundant memory accesses and workload imbalances that are difficult to predict. According to the experiments on modern GPUs, AGAThA achieves 18.8× speedup against the CPU-based baseline, 9.6× against the best GPU-based baseline, and 3.6× against GPU-based algorithms with different heuristics.

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Section: Related Workmentioning

confidence: 99%

AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping

Park,

Hong,

Song

et al. 2024

Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

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“…RNA folding is one of several Bioinformatics problems that can be solved using Dynamic Programming [Liu et al, 2006;Manavski and Valle, 2008;Luo et al, 2013]. RNA is a linear biological polymer whose monomer compo- Fig.…”

Section: Rna Binding Energy Cuda Dynamic Programmingmentioning

confidence: 99%

“…BarraCUDA was initially created by a team of nVidia, GPGPU and Bioinformatics experts who ported the popular BWA [Li and Durbin, 2010] tool to run on GPU hardware. Nevertheless, Section 7 will show that genetic programming in combination with manual effort was able to give more than a 100 fold improvement in a critical component.…”

Section: Introductionmentioning

confidence: 99%

Genetic improvement of GPU software

Langdon

Lam

Modat

et al. 2016

Genet Program Evolvable Mach

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We survey Genetic Improvement (GI) of general purpose computing on graphics cards. We summarise several experiments which demonstrate four themes. Experiments with the gzip program show that genetic programming (GP) can automatically port sequential C code to parallel code. Experiments with the StereoCamera program show that GI can upgrade legacy parallel code for new hardware and software. Experiments with NiftyReg and BarraCUDA show that GI can make substantial improvements to current parallel CUDA applications. Finally, experiments with the pknotsRG program show that with semi-automated approaches, enormous speed ups can sometimes be had by growing and grafting new code with genetic programming in combination with human input.

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“…Inicialmente, en 2006, surgieron las primeras implementaciones en GPU gracias a los esfuerzos de Weiguo Liu et al [136], así como de Yang Liu et al [137]. Ambas propuestas, a pesar de sus similitudes como el uso de la librería OpenGL y el almacenamiento de secuencias en memoria de textura, presentaron características distintivas.…”

Section: Búsqueda De Similitudunclassified

Estudio de viabilidad de SYCL como modelo de programación unificado para sistemas heterogéneos basados en GPUs en bioinformática

Costanzo

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Por un lado, la computación de alto rendimiento (HPC) requiere de modelos de programación que aprovechen el paralelismo masivo de arquitecturas heterogéneas modernas como las plataformas CPU-GPU. Sin embargo, modelos de programación como CUDA y OpenCL presentan limitaciones en portabilidad y productividad. SYCL surge como alternativa prometedora al unificar la programación en C++ y abstraer las particularidades del hardware. Por otro lado, la bioinformática y la biología computacional representan dos campos que han estado explotando las GPUs durante más de dos décadas, y muchas de sus implementaciones se basan en CUDA, lo que impone limitaciones significativas en cuanto a la portabilidad en una amplia gama de arquitecturas heterogéneas. Es por lo que esta tesis doctoral propone evaluar la viabilidad de SYCL como modelo de programación paralelo unificado, portable y eficiente para sistemas heterogéneos con GPUs, específicamente en bioinformática. Considerando que el alineamiento de secuencias biológicas representa una operación fundamental con amplias aplicaciones en diversas áreas de la biología y la medicina, se seleccionó la suite SW\# como caso de estudio por su relevancia y por estar desarrollado en CUDA. Mediante la herramienta SYCLomatic se migró completamente el código de SW\# de CUDA a SYCL. Este proceso involucró la ejecución de la herramienta, modificación del código generado, corrección de errores, verificación funcional, optimizaciones y estandarización SYCL. Posteriormente, se realizaron múltiples experimentos en un amplio conjunto de GPUs y CPUs de diferentes tipos y fabricantes para evaluar la portabilidad de rendimiento del código migrado en contextos individuales, tanto de GPU como de CPU, multi-GPU y CPU+GPU, en un entorno híbrido. Los resultados de esta tesis muestran, en primer lugar, que la herramienta SYCLomatic resulta efectiva y útil para la migración automática, aunque no pueda considerarse una solución final. En segundo lugar, las pruebas realizadas revelan que SYCL presenta una eficiencia comparable a la de CUDA en las GPUs NVIDIA y ofrece una amplia portabilidad funcional con una aceptable portabilidad de rendimiento a GPUs y CPUs de otros fabricantes. En conclusión, SYCL se posiciona como una alternativa viable como modelo unificado de programación heterogénea, portable y eficiente, estableciendo un precedente importante para futuros desarrollos en el área.

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Bio-sequence database scanning on a GPU

Abstract: Protein sequences with unknown functionality are often compared to a set of known sequences to detect

Cited by 7 publications

References 18 publications

AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping

AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping

Genetic improvement of GPU software

Estudio de viabilidad de SYCL como modelo de programación unificado para sistemas heterogéneos basados en GPUs en bioinformática

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