Heshan Lin: Accelerating Short Read Mapping, Local Realignment, and a Discovery on a Graphics Processing Unit (GPU)

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Heshan Lin's talk at the 1st Earth Microbial Project meeting in Shenzhen, June 15th 2011.

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Heshan Lin: Accelerating Short Read Mapping, Local Realignment, and a Discovery on a Graphics Processing Unit (GPU)

  1. 1. Accelerating Sequence Analysis on Graphics Processing Unit (GPU)<br />Wu Feng and Heshan Lin<br />Department of Computer Science <br />
  2. 2. NGS Democratizing DNA Sequencing<br />Sequencing available to the masses in the near future<br />Source: www.genome.gov<br />
  3. 3. Bottleneck Shift -> Computation<br />ChIP-Seq …<br />Transcriptome Sequencing<br />Complete Genome Re-sequencing<br />Metagenomics<br />BIG Data<br />
  4. 4. Traditional HPC Resources<br />HPC Users<br />?<br />Clusters<br />Supercomputers<br />The Masses<br />
  5. 5. Graphics Processing Unit (GPU) <br />Graphics & gaming -> general purpose computing<br />Ubiquitously available: Desktop, laptop, iPad<br />
  6. 6. “Personalized Supercomputer”<br /><ul><li>10x > CPU
  7. 7. 512 cores
  8. 8. 10^12 flops
  9. 9. On par with power of a supercomputer in 2004</li></li></ul><li>Traditional CPU Cores<br />Optimized for single thread<br />Control<br />(Fetch / Decode)<br />Out-of-order Control Logic<br />ALU<br />Branch Predictor<br />Execution Context<br />(Registers)<br />Memory Prefecter<br />Data Cache<br />Courtesy to K. Fatahalian<br />
  10. 10. Source: Borkar, De Intel<br />
  11. 11. GPU: Optimized for Throughput<br />Use much simpler cores<br />Use vectorization to replicate simple cores<br />Control<br />(Fetch / Decode)<br />Control<br />(Fetch / Decode)<br />ALU<br />ALU<br />ALU<br />ALU<br />ALU<br />ALU<br />ALU<br />ALU<br />ALU<br />ALU<br />ALU<br />Execution Context<br />(Registers)<br />Execution Context<br />(Registers)<br />Execution Context<br />(Registers)<br />Execution Context<br />(Registers)<br />Execution Context<br />(Registers)<br />Execution Context<br />(Registers)<br />Execution Context<br />(Registers)<br />Execution Context<br />(Registers)<br />Execution Context<br />(Registers)<br />Execution Context<br />(Registers)<br />Execution Context<br />(Registers)<br />Shared Execution Context<br />Courtesy to K. Fatahalian<br />
  12. 12. Take with a Grain of Salt<br />Raw Compute Power != Application Performance<br />Not all applications are suitable for GPUs<br />Developing fully optimized codes on GPU is non-trivial and requires computational rethinking<br />A GPU core is MUCH SLOWER than a CPU core<br />Need a lot of parallelism to hide memory latency<br />Reduce branching as much as possible<br />Think about an army of synchronized snails <br />
  13. 13. GPU Potential for Sequence Alignment<br />Why sequence alignment? <br />Fundamental in sequence analysis<br />Computationally intensive<br />Preliminary study<br />
  14. 14. Lessons Learnt<br />CPU optimized code may be difficult to accelerate on GPUs<br />BLASTP 6.5x vs. Smith Waterman 30x<br />Require rethinking of algorithm design<br />Scalable but less optimal algorithm is better<br />Example: RMAP<br />Originally uses hash table to find the match (O(n))<br />Switched to a slower binary search algorithm (O(nlogn))<br />
  15. 15. Opportunities<br />Smith Waterman<br />Needleman-Wunsch<br />BWA<br />Time<br />BLAST<br />Bowtie<br />Next-gen Algorithm?<br />Accuracy<br />
  16. 16. Compute the Cure Initiative<br />Partnership between NVIDIA and VT<br />Goal: Leverage GPU power to fight cancer<br />Current focus: GPU accelerated sequence alignment framework<br />http://www.nvidia.com/object/compute-the-cure.html<br />
  17. 17. Conclusion<br />Democratizing DNA sequencing requires more accessible HPC resources<br />GPUs present both opportunities and challenges<br />Initial results are promising<br />For more information<br />Synergy website – http://synergy.cs.vt.edu<br />
  18. 18. Acknowledgement<br />Collaborators<br />David Mittelman, Virginia Bioinformatics Institute<br />Students<br />AshwinAji<br />Shucai Xiao<br />Funding<br />NVIDIA Compute the Cure Program<br />NSF Center for High-Performance Reconfigurable Computing<br />

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