DNA sequences alignment in multi-GPUs: acceleration and energy payoff
Jesús Pérez-Serrano,Edans Flavius de Oliveira Sandes,Alba Cristina Magalhaes Alves de Melo,Manuel Ujaldón +3 more
TLDR
GPUs are found to be an order of magnitude ahead in performance per watt compared to Xeon Phis, and versus typical low-power devices like FPGAs, GPUs keep similar GFLOPS/w ratios in 2017 on a five times faster execution.Abstract:
We present a performance per watt analysis of CUDAlign 4.0, a parallel strategy to obtain the optimal pairwise alignment of huge DNA sequences in multi-GPU platforms using the exact Smith-Waterman method. Our study includes acceleration factors, performance, scalability, power efficiency and energy costs. We also quantify the influence of the contents of the compared sequences, identify potential scenarios for energy savings on speculative executions, and calculate performance and energy usage differences among distinct GPU generations and models. For a sequence alignment on chromosome-wide scale (around 2 Petacells), we are able to reduce execution times from 9.5 h on a Kepler GPU to just 2.5 h on a Pascal counterpart, with energy costs cut by 60%. We find GPUs to be an order of magnitude ahead in performance per watt compared to Xeon Phis. Finally, versus typical low-power devices like FPGAs, GPUs keep similar GFLOPS/w ratios in 2017 on a five times faster execution.read more
Citations
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A GPU-based hybrid jDE algorithm applied to the 3D-AB protein structure prediction
TL;DR: This work provides a high-performance hybrid algorithm to approach the 3D-AB off-lattice model through Graphics Processing Units (GPUs) through a self- Adaptive Differential Evolution (DE) that uses the jDE mechanism to self-adapt the DE parameters and employs the Hooke-Jeeves Direct Search as the exploitation routine.
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Accelerating Binary String Comparisons with a Scalable, Streaming-Based System Architecture Based on FPGAs
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Irregular alignment of arbitrarily long DNA sequences on GPU
TL;DR: GPUGECKO as discussed by the authors is a CUDA implementation for the sequential, seed-and-extend sequence-comparison algorithm, GECKO, which includes optimized kernels based on collective operations capable of producing arbitrarily long alignments while dealing with heterogeneous and unpredictable load.
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Hardware Acceleration of the STRIKE String Kernel Algorithm for Estimating Protein to Protein Interactions
TL;DR: In this article , the authors developed hardware accelerator designs for the weighted STRIKE algorithm, which is a PPI prediction algorithm based on a parallel module accelerator organization, achieving near linear speedups for long sequences of 100 or more characters.
References
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