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GPU Software URLs Websites Downloadables

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Andor J Kiss edited this page Jan 16, 2022 · 3 revisions

A non-comprehensive list of websites that have GPU enabled tools for bioinformatics:

Here's the necessary software for both nVIDIA and AMD GPU accelerators:

CUDA: https://developer.nvidia.com/cuda-downloads

ROCm: https://www.amd.com/en/graphics/servers-solutions-rocm-ml

TensorFlow: https://www.tensorflow.org/install

Progammes/Tools that use GPU and/or can use GPU:

CYTOSCAPE: https://manual.cytoscape.org/en/stable/Cytoscape_and_OpenCL_GPU.html

  1. Brunn, M., Himthani, N., Biros, G., Mehl, M. & Mang, A. Fast GPU 3D diffeomorphic image registration. J. Parallel Distrib. Comput. 149, 149–162 (2021). URL: https://peerj.com/articles/808
  2. Bae, J., Jeon, H. & Kim, M.-S. GPrimer: a fast GPU-based pipeline for primer design for qPCR experiments. BMC Bioinformatics 22, 220 (2021). URL: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04133-4
  3. Zhu, M., Kang, K. & Ning, K. Meta-Prism: Ultra-fast and highly accurate microbial community structure search utilizing dual indexing and parallel computation. Brief. Bioinform. (2020) doi:10.1093/bib/bbaa009. URL: https://doi.org/10.1093/bib/bbaa009
  4. Payne, A. et al. Readfish enables targeted nanopore sequencing of gigabase-sized genomes. Nat. Biotechnol. (2020) URL: http://dx.doi.org/10.1038/s41587-020-00746-x.
  5. Feldbauer, R. et al. DeepNOG: Fast and accurate protein orthologous group assignment. Bioinformatics (2020) doi:10.1093/bioinformatics/btaa1051. URL: https://doi.org/10.1093/bioinformatics/btaa1051
  6. Cavicchioli, R., Hu, J. C., Loli Piccolomini, E., Morotti, E. & Zanni, L. GPU acceleration of a model-based iterative method for Digital Breast Tomosynthesis. Sci. Rep. 10, 43 (2020). URL: https://doi.org/10.1038/s41598-019-56920-y
  7. Roels, J. et al. An interactive ImageJ plugin for semi-automated image denoising in electron microscopy. Nat. Commun. 11, 771 (2020). URL: https://doi.org/10.1038/s41467-020-14529-0
  8. Ahmed, N. et al. GASAL2: a GPU accelerated sequence alignment library for high-throughput NGS data. BMC Bioinformatics 20, 520 (2019). URL: https://doi.org/10.1186/s12859-019-3086-9
  9. Ayres, D. L. et al. BEAGLE 3: Improved Performance, Scaling, and Usability for a High-Performance Computing Library for Statistical Phylogenetics. Syst. Biol. 68, 1052–1061 (2019). URL: https://doi.org/10.1093/sysbio/syz020
  10. Haase, R. et al. CLIJ: GPU-accelerated image processing for everyone. Nat. Methods (2019) doi:10.1038/s41592-019-0650-1. URL: https://doi.org/10.1038/s41592-019-0650-1
  11. Bouckaert, R. et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLOS Comput. Biol. 15, e1006650 (2019). URL: https://doi.org/10.1371/journal.pcbi.1006650
  12. Jia, B. et al. GLAPD: Whole Genome Based LAMP Primer Design for a Set of Target Genomes. Front. Microbiol. 10, 2860 (2019). URL: https://www.frontiersin.org/article/10.3389/fmicb.2019.02860
  13. Santander-Jiménez, S., Vega-Rodríguez, M. A., Vicente-Viola, J. & Sousa, L. Comparative assessment of GPGPU technologies to accelerate objective functions: A case study on parsimony. J. Parallel Distrib. Comput. 126, 67–81 (2019). URL: https://linkinghub.elsevier.com/retrieve/pii/S0743731518304350
  14. Czech, E., Aksoy, B. A., Aksoy, P. & Hammerbacher, J. Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging. BMC Bioinformatics 20, 448 (2019). URL: https://doi.org/10.1186/s12859-019-3055-3
  15. Fang, C.-H., Theera-Ampornpunt, N., Roth, M. A., Grama, A. & Chaterji, S. AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU. BMC Bioinformatics 20, 488 (2019). URL: https://doi.org/10.1186/s12859-019-3049-1
  16. Swiercz, A. et al. GRASShopPER—An algorithm for de novo assembly based on GPU alignments. PLoS One 13, e0202355 (2018). URL: https://dx.plos.org/10.1371/journal.pone.0202355
  17. Kobus, R., Hundt, C., Müller, A. & Schmidt, B. Accelerating metagenomic read classification on CUDA-enabled GPUs. BMC Bioinformatics 18, 11 (2017). URL: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1434-6
  18. Tristão Ramos, R. J. et al. CrocoBLAST: Running BLAST efficiently in the age of next-generation sequencing. Bioinformatics 33, 3648–3651 (2017). URL: https://doi.org/10.1093/bioinformatics/btx465
  19. Houtgast, E. J., Sima, V.-M., Bertels, K. & Al-Ars, Z. GPU-Accelerated BWA-MEM Genomic Mapping Algorithm Using Adaptive Load Balancing. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (ed. Hannig, F.) vol. 9637 130–142 (ARCS, 2016). URL: http://link.springer.com/10.1007/978-3-319-30695-7_10
  20. Suzuki, S., Kakuta, M., Ishida, T. & Akiyama, Y. GPU-Acceleration of Sequence Homology Searches with Database Subsequence Clustering. PLoS One 11, e0157338–e0157338 (2016). URL: http://advances.sciencemag.org/lookup/doi/10.1126/sciadv.1501860
  21. Manconi, A., Moscatelli, M., Gnocchi, M., Armano, G. & Milanesi, L. A GPU-based high performance computing infrastructure for specialized NGS analyses. PeerJ Prepr. 4–6 (2016) doi:10.7287/peerj.preprints.2175. URL: https://peerj.com/preprints/2175/
  22. Nobile, M. S., Cazzaniga, P., Tangherloni, A. & Besozzi, D. Graphics processing units in bioinformatics, computational biology and systems biology. Brief. Bioinform. 18, bbw058 (2016). URL: http://dx.doi.org/10.1093/bib/bbw058
  23. Zhou, C., Lang, X., Wang, Y. & Zhu, C. gPGA: GPU Accelerated Population Genetics Analyses. PLoS One 10, e0135028 (2015). URL: https://doi.org/10.1371/journal.pone.0135028
  24. Wilton, R. et al. Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space. PeerJ 3, e808 (2015). URL: https://peerj.com/articles/808
  25. O’Driscoll, A. et al. HBLAST: Parallelised sequence similarity – A Hadoop MapReducable basic local alignment search tool. J. Biomed. Inform. 54, 58–64 (2015). URL: https://www.sciencedirect.com/science/article/pii/S1532046415000106
  26. Zhao, K. & Chu, X. G-BLASTN: accelerating nucleotide alignment by graphics processors. Bioinformatics 30, 1384–1391 (2014).
  27. Heo, Y., Wu, X., Chen, D., Ma, J. & Hwu, W. BLESS: Bloom filter-based error correction solution for high-throughput sequencing reads. Bioinformatics 30, 1354–1362 (2014). URL: https://doi.org/10.1093/bioinformatics/btu047
  28. Ling, C. et al. MrBayes tgMC3: A Tight GPU Implementation of MrBayes. PLoS One 8, e60667 (2013). URL: https://doi.org/10.1371/journal.pone.0060667
  29. Wang, H., Rahnamayan, S. & Wu, Z. Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems. J. Parallel Distrib. Comput. 73, 62–73 (2013). URL: http://dx.doi.org/10.1016/j.jpdc.2012.02.019
  30. Brodtkorb, A. R., Hagen, T. R. & Sætra, M. L. Graphics processing unit (GPU) programming strategies and trends in GPU computing. J. Parallel Distrib. Comput. 73, 4–13 (2013). URL: https://www.sciencedirect.com/science/article/pii/S0743731512000998
  31. Talbi, E.-G. & Hasle, G. Metaheuristics on GPUs. J. Parallel Distrib. Comput. 73, 1–3 (2013). URL: https://linkinghub.elsevier.com/retrieve/pii/S0743731512002298
  32. Pinel, F., Dorronsoro, B. & Bouvry, P. Solving very large instances of the scheduling of independent tasks problem on the GPU. J. Parallel Distrib. Comput. 73, 101–110 (2013). URL: http://www.sciencedirect.com/science/article/pii/S0743731512000627
  33. Augusto, D. A. & Barbosa, H. J. C. Accelerated parallel genetic programming tree evaluation with OpenCL. J. Parallel Distrib. Comput. 73, 86–100 (2013). URL: http://www.sciencedirect.com/science/article/pii/S074373151200024X
  34. Blazewicz, J., Frohmberg, W., Kierzynka, M. & Wojciechowski, P. G-MSA - A GPU-based, fast and accurate algorithm for multiple sequence alignment. J. Parallel Distrib. Comput. 73, 32–41 (2013). URL: http://dx.doi.org/10.1016/j.jpdc.2012.04.004
  35. Suzuki, S., Ishida, T., Kurokawa, K. & Akiyama, Y. GHOSTM: A GPU-Accelerated Homology Search Tool for Metagenomics. PLoS One 7, e36060 (2012). URL: https://doi.org/10.1371/journal.pone.0036060
  36. Vouzis, P. D. & Sahinidis, N. V. GPU-BLAST: Using graphics processors to accelerate protein sequence alignment. Bioinformatics 27, 182–188 (2011). URL: https://academic.oup.com/bioinformatics/article/27/2/182/285951
  37. Ling, C. & Benkrid, K. Design and implementation of a CUDA-compatible GPU-based core for gapped BLAST algorithm. Procedia Comput. Sci. 1, 495–504 (2010). URL: https://www.sciencedirect.com/science/article/pii/S1877050910000542
  38. Sfiligoi, I., McDonald, D. & Knight, R. Enabling microbiome research on personal devices. in 2021 IEEE 17th International Conference on eScience (eScience) 229–230 (IEEE, 2021). doi:10.1109/eScience51609.2021.00035. URL: https://ieeexplore.ieee.org/document/9582377 GitHub Repo: https://github.com/biocore/unifrac
  39. Oh, M. & Zhang, L. DeepMicro: deep representation learning for disease prediction based on microbiome data. Sci. Reports 2020 101 10, 1–9 (2020). URL: https://www.nature.com/articles/s41598-020-63159-5
  40. Su, X., Wang, X., Jing, G. & Ning, K. GPU-Meta-Storms: computing the structure similarities among massive amount of microbial community samples using GPU. Bioinformatics 30, 1031–1033 (2014). URL: https://academic.oup.com/bioinformatics/article/30/7/1031/235324
  41. Oh, M. & Zhang, L. DeepMicro: deep representation learning for disease prediction based on microbiome data. Sci. Reports 2020 101 10, 1–9 (2020). URL: https://www.nature.com/articles/s41598-020-63159-5
  42. Langdon, W. B., Lam, B. Y. H., Petke, J. & Harman, M. Improving CUDA DNA Analysis Software with Genetic Programming. in Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation 1063–1070 (ACM, 2015). doi:10.1145/2739480.2754652.URL:https://dl.acm.org/doi/10.1145/2739480.2754652 BarraCUDA Project: http://seqbarracuda.sourceforge.net/index.html
  43. Repository of GPU based sequence aligners - both nucleotide and protein, for multiple sequence alignment and for NGS. URL: http://gpualign.cs.put.poznan.pl/index.html
  44. Reddy S, Hung L-H, Sala-Torra O, Radich JP, Yeung CC, Yeung KY. 2021. A graphical, interactive and GPU-enabled workflow to process long-read sequencing data. BMC Genomics 22:626. URL: https://doi.org/10.1186/s12864-021-07927-1
  45. Margelevičius M. 2020. COMER2: GPU-accelerated sensitive and specific homology searches. Bioinformatics 36:3570–3572. URL:https://dx.doi.org/10.1093/bioinformatics/btaa185
  46. Sreenivasan V, Kumar S, Pestilli F, Talukdar P, Sridharan D. 2022. GPU-accelerated connectome discovery at scale. Nat Comput Sci 2:298–306. URL:https://www.nature.com/articles/s43588-022-00250-z
  47. Aguado-Puig Q, Doblas M, Matzoros C, Espinosa A, Moure JC, Marco-Sola S, Moreto M. 2023. WFA-GPU: gap-affine pairwise read-alignment using GPUs. Bioinformatics [Internet] 39:btad701. Available from: https://doi.org/10.1093/bioinformatics/btad701
  48. Deng Z-L, Münch PC, Mreches R, McHardy AC. 2022. Rapid and accurate identification of ribosomal RNA sequences via deep learning. Nucleic Acids Research [Internet] 50:e60. Available from: https://doi.org/10.1093/nar/gkac112
  49. Feng Y, Gudukbay Akbulut G, Tang X, Gunasekaran JR, Rahman A, Medvedev P, Kandemir M. 2022. GPU-accelerated and pipelined methylation calling. Bioinformatics Advances [Internet] 2:vbac088. Available from: https://doi.org/10.1093/bioadv/vbac088
  50. Harris LA, Nobile MS, Pino JC, Lubbock ALR, Besozzi D, Mauri G, Cazzaniga P, Lopez CF. 2017. GPU-powered model analysis with PySB/cupSODA. Bioinformatics [Internet] 33:3492–3494. Available from: https://doi.org/10.1093/bioinformatics/btx420
  51. Hippen AA, Crawford J, Gardner JR, Greene CS. 2022. wenda_gpu: fast domain adaptation for genomic data. Bioinformatics [Internet] 38:5129–5130. Available from: https://doi.org/10.1093/bioinformatics/btac663
  52. Wlasnowolski M, Grabowski P, Roszczyk D, Kaczmarski K, Plewczynski D. 2023. cudaMMC: GPU-enhanced multiscale Monte Carlo chromatin 3D modelling. Bioinformatics [Internet] 39:btad588. Available from: https://doi.org/10.1093/bioinformatics/btad588
  53. Ahdritz G, Bouatta N, Floristean C, Kadyan S, Xia Q, Gerecke W, O’Donnell TJ, Berenberg D, Fisk I, Zanichelli N, Zhang B, Nowaczynski A, Wang B, Stepniewska-Dziubinska MM, Zhang S, Ojewole A, Guney ME, Biderman S, Watkins AM, Ra S, Lorenzo PR, Nivon L, Weitzner B, Ban Y-EA, Chen S, Zhang M, Li C, Song SL, He Y, Sorger PK, Mostaque E, Zhang Z, Bonneau R, AlQuraishi M. 2024. OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Nat Methods [Internet] 21:1514–1524. Available from: https://www.nature.com/articles/s41592-024-02272-z
  54. NVIDIA Developer. 2026. NVIDIA CUDA-X Data Science. CUDA-X Data Science Libraries [Internet]. Available from: https://developer.nvidia.com/topics/ai/data-science/cuda-x-data-science-libraries
  55. AMD. 2026. ROCm Life Science documentation — ROCm-LS 25.11 documentation. ROCm Life Science Documentation [Internet]. Available from: https://rocm.docs.amd.com/projects/rocm-ls/en/develop/index.html
  56. Kutzner C, Páll S, Fechner M, Esztermann A, de Groot BL, Grubmüller H. 2019. More bang for your buck: Improved use of GPU nodes for GROMACS 2018. Journal of Computational Chemistry [Internet] 40:2418–2431. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.26011
  57. Páll S, Zhmurov A, Bauer P, Abraham M, Lundborg M, Gray A, Hess B, Lindahl E. 2020. Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS. J Chem Phys [Internet] 153:134110. Available from: https://doi.org/10.1063/5.0018516
  58. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC. 2005. GROMACS: Fast, flexible, and free. Journal of Computational Chemistry [Internet] 26:1701–1718. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.20291
  59. Melsted P, Guðnýjarson EM, Nordal J. 2026. RNA-seq analysis in seconds using GPUs. bioRxiv:2026.03.04.709526. [Internet] Available from: https://www.biorxiv.org/content/10.64898/2026.03.04.709526v1

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