The amount of data being generated in genomics is predicted to be between 2 and 40 exabytes per year for the next decade, making genomic analysis the new frontier and the new challenge for precision medicine. We explore targeted deployment of hardware accelerators in the cloud to improve the runtime and throughput of immense-scale genomic data analyses. In particular, INDEL(INsertion/DELetion) realignment is a critical operation that enables diagnostic testings of cancer through error correction prior to variant calling. It is the slowest part of the somatic (cancer) genomic analysis pipeline, the alignment refinement pipeline, and represents roughly one-third of the execution time of time-sensitive diagnostics for acute cancer patients.
To accelerate genomic analysis, we architected a hardware accelerator for INDEL realignment (IR), and a hardware-software framework leveraging FPGAs-as-a-service in the cloud. We chose to implement genomics analytics on FPGAs because genomic algorithms are still rapidly evolving (e.g. the de facto standard "GATK Best Practices" has had five releases since January of this year). We chose to deploy genomics accelerators in the cloud to reduce capital expenditure and to provide a more quantitative performance and cost analysis. We built and deployed a sea of IR accelerators using our hardware-software accelerator development framework on AWS EC2 F1 instances. We show that our IR accelerator system, measured end-to-end, performed 81× better than multi-threaded genomic analysis software while being 32× more cost efficient.
This efficient accelerated IR system can perform INDEL realignment for the entire human genome (i.e. chr1 through chr22) in roughly 30 minutes, compared to software completing the same amount of work in 42 hours. Using the cost (in dollars) per hour running IR on EC2 F1 instances, we can complete chr1 through chr22 for less than one dollar, compared to the software baseline running on EC2 R3 instances for around 28 dollars.
Lisa Wu, David Bruns-Smith, Frank A. Nothaft, Qijing Huang, Sagar Karandikar, Johnny Le, Andrew Lin, Howard Mao, Brendan Sweeney, Krste Asanović, David A. Patterson, and Anthony D. Joseph, "FPGA Accelerated INDEL Realignment in the Cloud". IEEE International Symposium on High-Performance Computer Architecture (HPCA) 2019.
Lisa Wu, Frank A. Nothaft, Brendan Sweeney, David Bruns-Smith, Sagar Karandikar, Johnny Le, Howard Mao, Krste Asanović, David A. Patterson, and Anthony D. Joseph, "Accelerating Duplicate Marking in the Cloud". HPCA Workshop on Accelerator Architecture in Computational Biology and Bioinformatics (AACBB) 2018.