Acceleration of the PairHMM forward algorithm on FPGA with cloud integration for GATK The PairHMM forward algorithm is an essential algorithm found in many genomic related analyses. The high number of floating point operations in the algorithm makes it one of the main contributors to the compute time of analysis pipelines. To speedup computations we propose an FPGAbased hardware accelerator for the Amazon AWS F1 Cloud platform. The accelerator is open source and has been tested within the popular Genomic Analysis Toolkit (GATK) pipeline. The accelerator achieved up to 15 xc3x97 speedup against the software implementation when used inpipeline. The accelerator has also been tested in the experimental Spark (distributed) version of the GATK HaplotypeCaller tool. An indepth analysis of the compute time contributions allowed to point out the main bottlenecks for accelerators in the GATK pipeline, resulting in a hybrid CPUFPGA solution to best exploit both resources.

A Complete CPUFPGA Architecture for Protein Identification with Tandem Mass Spectrometry Tandem mass spectrometrybased database searching has currently been a significant technique for protein identification in proteomics. The evergrowing protein databases induce severe challenges for efficient database searching engines. Profiling analysis shows that X!Tandem, one of the most widely used opensource database search engines for protein identification, spends almost 78% of the total time on the scoring process. In this paper, field programmable gate arrays (FPGAs) are used as hardware accelerators due to their ability to parallelize arithmetic operations and execute loops in parallel. A scalable heterogeneous CPUFPGA architecture is proposed to speed up the whole process of X!Tandem, in which parent ion matching and scoring are implemented on FPGAs. The hardware implementation of the scoring process running on one Xilinx Kintex UltraScale FPGA board (XCKU115) at 150 MHz can achieve 21fold speedup over original X!Tandem software implementation running on a CPU, while the complete CPUFPGA architecture, which consists of two FPGA boards, achieves more than 10fold speedup over CPUonly implementation as far as the whole process of protein identification is concerned.