ResCUE rational behind FPGA
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FPGAs are proposed as a solution to speed up DNA variation identification while lowering power consumption compared to GPUs. FPGAs can handle both the large amounts of genomic data involved in analysis as well as the sequential and parallel processes like Hidden Markov Models 6 times faster than GPUs. FPGAs are also more power efficient than GPUs for algorithms like k-Nearest Neighbors that are 8 times less power consuming on FPGAs. Based on these factors, FPGAs are concluded to be the best solution for algorithms like XHMM and CLAMMS used in genomics analysis.
ResCUE rational behind FPGA
- 2. 2OUR GOAL Speed up the identification of DNA variation while lowering the power consumption
- 3. 3 HARDWARE AND GENOMICS GENOMICS ANALYSIS INVOLVES • Big amount of data • Both sequential and parallel processes • Hidden Markov Model (6 times faster on FPGA then on GPU) http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1287823
- 4. ALGORITHMS’ PIPELINE: XHMM Mean per-target coverage using GATK Filter out «extreme» target and simple Filter target and samples and calculate z-score Mean center each target and run PCA HMM to merge targets and call per-sample CNVs
- 5. ALGORITHMS’ PIPELINE: CLAMMS • High Scalability • Both parallel and sequential processes • k-Nearest Neighbors (8 times less power consuming on FPGA then on GPU) • Depth Of Coverage with GATK • HMM https://www.ncbi.nlm.nih.gov/pubmed/26382196 http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7160066
- 6. CONCLUSION FPGA are the best solution for both algorithms