Bioinformatics and Life Sciences Standards and Programming for Heterogeneous Architectures

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1 Bioinformatics and Life Sciences Standards and Programming for Heterogeneous Architectures Eric Stahlberg Ph.D. (SAIC-Frederick contractor) SIAM Conference on Parallel Processing for Scientific Computing Savannah, GA, February 16, 2012 Caveats: Content and statements following do not constitute any official position or endorsement, whether stated or implied. All copyrights of referenced material remain with the original owner. 1

2 Context for Heterogeneous Acceleration Cancer kills every 55 seconds Cancer research utilizes bioinformatics heavily Bioinformatics is computationally intensive Faster solutions help cancer research move faster Faster and better clinical applications help to impact patient lives Today s Goal: Encourage paths to improve bioinformatics applications for cancer research

3 Three Key Needs Faster and better applications Better education and preparation in parallel and distributed computing Better and faster data handling solutions

4 SAIC-Frederick, Inc. Technical and operations contractor to the U.S. National Cancer Institute Federally Funded Research and Development Center for DHHS Many technical and operational areas of support for the NCI including bioinformatics IT picture here 4

5 NCI Center for Cancer Research

6 NCI Center for Cancer Research BRANCHES Cell and Cancer Biology Dermatology Experimental Immunology Experimental Transplantation and Immunology Genetics HIV and AIDS Malignancy HIV DRP Host-Virus Interaction Medical Oncology Metabolism Neuro-Oncology Pediatric Oncology Radiation Biology Radiation Oncology Surgery Urologic Oncology Vaccine PROGRAMS Cancer and Inflammation CCR Nanobiology HIV Drug Resistance Molecular Discovery Molecular Imaging Mouse Cancer Genetics LABS Basic Research Laboratory Cancer and Developmental Biology Laboratory Chemical Biology Laboratory Gene Regulation and Chromosome Biology Laboratory HIV DRP Retroviral Replication Laboratory Laboratory of Biochemistry and Molecular Biology Laboratory of Cancer Biology and Genetics Laboratory of Cancer Prevention Laboratory of Cell and Developmental Signaling Laboratory of Cell Biology Laboratory of Cellular and Molecular Biology Laboratory of Cellular Oncology Laboratory of Experimental Carcinogenesis Biophysics Laboratory Laboratory of Experimental Immunology Laboratory of Genome Integrity Laboratory of Human Carcinogenesis Laboratory of Immune Cell Biology Laboratory of Metabolism Laboratory of Molecular Biology Laboratory of Molecular Immunoregulation Laboratory of Molecular Pharmacology Laboratory of Pathology Laboratory of Population Genetics Laboratory of Protein Dynamics and Signaling Laboratory of Receptor Biology and Gene Expression Laboratory of Tumor Immunology and Biology Macromolecular Crystallography Laboratory Molecular Targets Laboratory Structural Biophysics Laboratory

7 Life Science Application Areas Image processing 3D imaging 2D imaging Sequence and protein analysis Microarray Next Generation Sequence Analysis Proteomics Simulation Molecular interactions and dynamics Complex systems biology simulations Data mining and analytics Statistics Graph and cluster analysis Population analysis

8 Dataflow View of Basic Biology DNA Transcription RNA Mitosis Translation new cell Duplicated DNA Cell Functions Intra-Cellular Functions Proteins DNA information flow Protein feedback loop Intercellular communication Transform Process Data source

9 Metabolic Pathways at Higher Resolution Source:

10 Next Generation Sequencing Focus Next Generation Sequencing Focus Used to understand complex biological systems Common types of NGS applications ChIPseq RNAseq mirnaseq Epigenetic studies Large and growing dataset sizes Identify, associate, and compare within individual experiments Integrate and compare across experiments

11 Data Acquisition Costs Plummet 11

12 Large Data Challenges Big Data = Big Challenges Volume of available data is growing rapidly One run produces hundreds of gigabytes of data* Policy issues HIPAA, security and protection Move it, store it, delete it? Validation and clinical liability Metadata - reliable secondary value *Reference: Barski and Zhao, Journal of Cellular Biochemistry, 107:11-18,

13 Generic General NextGenWorkflow NGS Sequence Acquisition Data Quality Evaluation Sequence Read Mapping Analysis of Mapped Reads Compare Across Samples Experimental data is progressively concentrated to become knowledge for decision Per sample volume of information reduced as data is analyzed Concentrated results are integrated to inform decisions

14 Example Illustrative Areas Next in Gen Next Sequencing Gen Sequencing Apps Genome Assembly Combine small fragments of DNA/RNA into highconfidence composite contigs Connect the small pieces into a larger string consistent with observed sequences and known biology Read Mapping Start with a known baseline reference genome Map smaller pieces of DNA/RNA to their correct location on the reference genome allowing for mismatches, insertions, deletions

15 RNA Sequencing Overview Source:

16 Challenges Key in NGS Next Challenges Gen Sequencing Transferring large datasets Processing huge datasets Integrating datasets Proliferation of sequencing capabilities Growing data volumes too great to store results Overcoming ambiguity with algorithmic improvements Reproducibility over time Translation to clinical application Applications are parallel but not system friendly

17 Research vs. Clinical Application Contrasting Application Goals Research Application Aims Agile Rapid incorporation of new advances Ad hoc development process Open source Documented as needed Generally portable Limited liability for failures Marginal testing Reproducibility Speed SIAM Conference on Parallel Processing for Scientific Computing, February 16, 2012 Clinical Application Aims Stable Measuredincorporation of proven advances Development process required Licensed and proprietary Well documented Supportable Liability for failure Certification of testing Reproducibility Speed

18 Three Key Needs Faster and better applications Better education and preparation in parallel and distributed computing Better and faster data handling solutions

19 Why Better Education in PDC? Improved application development Higher speed applications More robust applications in PDC environments More efficient applications Better interoperability among PDC technologies More effective application use at run time Analysts know how to use parallel computing effectively Understanding of scalability to better relate problem size to computational resources Improved planning of large computational analysis efforts Better run-time efficiency

20 Changing a Way of Thinking Education is key Teaching parallel and accelerated computing across the CS curriculum Innovative NSF funded project Incorporating parallel computing into CS, software development, and computational science Workshop and website under development See more information Courses Enhanced Computer Literacy Intro to Programming Data Structures Algorithms Programming Languages Computer Hardware Computational Modeling Bioinformatics (applications) Computational chemistry (applications) We gratefully acknowledge the support of the National Science Foundation Grant CCF , SHF:Small:RUI:Collaborative Research: Accelerators to Applications Supercharging the Undergraduate Computer Science Curriculum

21 Why Heterogeneous Acceleration?? Problems are large Recent sample runs have taken up to 4 days to compute Experiments include many samples Data is becoming too large to move Instrument systems are becoming smaller and cheaper Trend to generate much more data continues Technologies are heterogeneous Multicoreis pervasive and proven GPU technology is affordable and available FPGAs have history for fast bioinformatics

22 Parallel Computing in Bioinformatics Parallel Computing and bioinformatics 182 articles in PubMed since 1995 GPU and Bioinformatics 50 articles dating back to articles in CUDA and bioinformatics Message Passing and bioinformatics 26 articles with message passing and bioinformatics FPGA and Bioinformatics 22 articles in PubMed since 1993 OpenMP and bioinformatics 6 articles in OpenMP and bioinformatics OpenCL and bioinformatics 3 articles reported Parallel Computing GPU CUDA Message Passing FPGA OpenMP OpenCL

23 Biowulfat NIH Biowulf NIH HPC Resource GPU cluster available

24 Weighing Relative the Merits of Standards of Pros Stabilizes development efforts Improve portability of algorithms and applications Raise productivity and innovation Improve robustness of mission critical applications Improve supportability Channels creativity and innovation Easier education Cons Takes time forcommunity adoption Possible performance penalty in some cases

25 Open Accelerator Not to be confused with OpenACC Open Accelerator Initiative provides community knowledge base of accelerated computing activity Components, performance, literature, and more to come Encourages interoperability among technologies and standards Registration services support application reproducibility and certification Downloads: OpenFPGA draft GenAPI standard Visit

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27 Summary Faster and better applications Heterogeneous acceleration Support standards and interoperability Multiple areas exist Better education and preparation in parallel and distributed computing Improved application development Ease of application use Better and faster data handling solutions Not addressed here

28 Colleagues at NCI CCR, SAIC-F ABCC National Science Foundation CISE Colleagues at Wittenberg and Clemson Dr. Steven Bogaerts, Dr. Kyle Burke, Dr. Brian Shelburne, Acknowledgements Dr. Melissa Smith OpenFPGA and OpenAccelerator communities Contact information: estahlberg(-at-) gmail.com or stahlbergea(-at-)mail.nih.gov