Abstract
Smith–Waterman algorithm (SW) is a popular dynamic programming algorithm widely used in bioinformatics for local biological sequence alignment. Due to the \(O(n^2)\) high time and space complexity of SW and growing size of biological data, it is crucial to accelerate SW for high performance. In view of the GPU high efficiency in science computation, many existing studies (e.g., CUDAlign, CUDASW++) speedup SW with GPU. However, the strong data dependency makes SW communication intensive, and the previous works fail to fully leverage the heterogeneous capabilities of the GPU machine for either the neglect of the CPU ability or the low bandwidth of PCI-e. In this paper, we propose ASW, which aims at accelerating SW algorithm with accelerated processing unit (APU), a heterogeneous processor integrates CPU and GPU in a single die and share the same memory. This coupled CPU–GPU architecture is more suitable for frequent data exchanging due to the elimination of PCI-e bus. For the full utilization of both CPU and GPU in APU system, ASW partitions the whole SW matrix into blocks and dynamically dispatches each block to CPU and GPU for the concurrent execution. A DAG-based dynamic scheduling method is presented to dispatch the workload automatically. Moreover, we also design a time cost model to determine the partition granularity in the matrix division phase. We have evaluated ASW on AMD A12 platform and our results show that ASW achieves a good performance of 7.2 GCUPS (gigacells update per second).
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Mol. Biol. 215(3), 403–410 (1990)
Branover, A., Foley, D., Steinman, M.: AMD fusion APU: Llano. IEEE Micro 32(2), 28–37 (2012)
De Oliveira Sandes, E.F., Miranda, G., Martorell, X., Ayguade, E., Teodoro, G., Melo, A.C.M.: Cudalign 4.0: incremental speculative traceback for exact chromosome-wide alignment in GPU clusters. IEEE Trans. Parallel Distrib. Syst. 27(10), 2838–2850 (2016)
De Oliveira Sandes, E.F., Miranda, G., De Melo, A.C., Martorell, X., Ayguade, E.: CUDAlign 3.0: parallel biological sequence comparison in large GPU clusters. In: 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 160–169. IEEE (2014)
He, J., Lu, M., He, B.: Revisiting co-processing for hash joins on the coupled CPU–GPU architecture. Proc. VLDB Endow. 6(10), 889–900 (2013)
He, J., Zhang, S., He, B.: In-cache query co-processing on coupled CPU–GPU architectures. Proc. VLDB Endow. 8(4), 329–340 (2014)
Lipman, D.J., Pearson, W.R.: Rapid and sensitive protein similarity searches. Science 227(4693), 1435–1441 (1985)
Liu, Y., Tran, T.T., Lauenroth, F., Schmidt, B.: SWAPHI-LS: Smith–Waterman algorithm on Xeon Phi coprocessors for long DNA sequences. In: IEEE International Conference on CLUSTER Computing, pp. 257–265 (2014)
Liu, Y., Wirawan, A., Schmidt, B.: Cudasw++ 3.0: accelerating Smith–Waterman protein database search by coupling CPU and GPU SIMD instructions. BMC Bioinform. 14(1), 117 (2013)
Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)
Rucci, E., García, C., Botella, G., De Giusti, A., Naiouf, M., Prieto-Matias, M.: OSWALD: OpenCL Smith–Waterman on Altera’s FPGA for large protein databases. In: 2015 IEEE Trustcom/BigDataSE/ISPA, vol. 3, pp. 208–213. IEEE (2015)
Rucci, E., Garcia, C., Botella, G., Giusti, A.D., Naiouf, M., Prieto-Matias, M.: Accelerating Smith–Waterman alignment of long DNA sequences with OpenCL on FPGA. In: International Conference on Bioinformatics and Biomedical Engineering, pp. 500–511 (2017)
Ryzen APU. https://www.amd.com/en/products/apu/amd-ryzen-5-2400g (2018). Accessed 12 Feb 2018
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147(1), 195–197 (1981)
Stone, J.E., Gohara, D., Shi, G.: OpenCL: a parallel programming standard for heterogeneous computing systems. Comput. Sci. Eng. 12(3), 66–73 (2010)
Tang, S., He, B., Zhang, S., Niu, Z.: Elastic multi-resource fairness: balancing fairness and efficiency in coupled CPU–GPU architectures. In: SC16: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 875–886. IEEE (2016)
Tang, S., Yu, C., Sun, J., Lee, B.S., Zhang, T., Xu, Z., Wu, H.: EasyPDP: an efficient parallel dynamic programming runtime system for computational biology. IEEE Trans. Parallel Distrib. Syst. 23(5), 862–872 (2012)
Zhang, F., Zhai, J., He, B., Zhang, S., Chen, W.: Understanding co-running behaviors on integrated CPU/GPU architectures. IEEE Trans. Parallel Distrib. Syst. 28(3), 905–918 (2017)
Zhang, K., Hu, J., He, B., Hua, B.: DIDO: dynamic pipelines for in-memory key-value stores on coupled CPU-GPU architectures. In: IEEE International Conference on Data Engineering, pp. 671–682 (2017)
Zhang, F., Wu, B., Zhai, J., He, B., Chen, W.: FinePar: irregularity-aware fine-grained workload partitioning on integrated architectures. In: Proceedings of the 2017 International Symposium on Code Generation and Optimization, pp. 27–38. IEEE Press (2017)
Acknowledgements
This work is supported by the Natural Science Foundation of Jilin Province (CN) (61602336), National Natural Science Foundation of China (61370010, 61702527) and Natural Science Foundation of Tianjin City (18JCZDJC30800).
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zou, H., Tang, S., Yu, C. et al. ASW: Accelerating Smith–Waterman Algorithm on Coupled CPU–GPU Architecture. Int J Parallel Prog 47, 388–402 (2019). https://doi.org/10.1007/s10766-018-0617-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10766-018-0617-3