Acceleration of read alignment with coherent attached FPGA coprocessors

With the advent of Next Generation Sequencing (NGS), the cost of sequencing human DNA has decreased significantly over the past decade. This decrease in cost has attracted a great deal of attention from medical research and is now transitioning to clinical practice. Precision medicine, tailored to a persons's genetic profile, is becoming a viable option in the battle against cancer and rare genetic diseases. NGS sequencers produce millions of small fragments of DNA called reads. Mapping those reads to a reference genome proves to be a tremendous computational task and forms the bottleneck in current DNA analysis flows. BWA-MEM, a state-of-the-art alignment tool, applies the seed and extend paradigm to rapidly align the reads with a reference genome. The Smith-Waterman (S-W) algorithm is widely adopted by these state-of-the-art aligners. We have identified the S-W algorithm to be the main computational bottleneck on IBM's POWER8 processor. This recently released processor includes a new Coherent Accelerator Processor Interface (CAPI), that provides cache coherent access to shared memory for heterogeneous processors. We present a highly parallel FPGA-based accelerator that offloads the Smith-Waterman task. We have integrated the accelerator with software, relying on CAPI, in a tightly coupled fashion. This integrated heterogeneous system is able to achieve a speedup of 1.6X over purely software-based multithreaded execution of BWA-MEM. Furthermore, we propose a more general framework for dividing workload between the processor and accelerator in a fine-grained manner. We expect to achieve the maximum obtainable speedup (bounded by Amdahl's law) of 2X with the proposed framework.Computer EngineeringMicroelectronics & Computer EngineeringElectrical Engineering, Mathematics and Computer Scienc