Accelerated HaplotypeCaller DNA Analysis Application on FPGAs Using CAPI
Nikolas Bampetas (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Zaid Al-Ars – Mentor
Stephan Wong – Graduation committee member
Rene van Leuken – Graduation committee member
J.W. Peltenburg – Coach
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Abstract
DNA carries all the information needed to define the genetic structure of an individual. The large amounts of information stored in the DNA makes it costly to store and to process. In this work, a fast and efficient implementation of a Field Programmable GateArray (FPGA) based, streaming multicore architecture for accelerating variant calling algorithms will be designed. We focused on the HaplotypeCaller which is the variant calling software part of the Genome Analysis Toolkit (GATK), which is one of the most widely used DNA analysis tools in the field. The most time consuming part of the HaplotypeCaller is the PairHMM algorithm. PairHMM is a probabilistic algorithm that executes pairwise alignment of two sequences. Starting from an existing single core PairHMM accelerator design, which was implemented using the POWER8 Coherent Accelerator Processor Interface (CAPI), we designed three extra cores of thePairHMM algorithm that can work independently and increase the performance of the overall system. The new accelerator achieves a 2.2x speedup in comparison with the single core. The accelerator is integrated with the HaplotypeCaller and uses CAPI to access a shared processor-accelerator memory for direct communication. A JNI call is used to send the memory addresses to the accelerator, which reduces the communication overhead between the HaplotypeCaller and the accelerator. Results show that the application is not able to saturate the accelerator with data, resulting in accelerator idle time and under-utilization. The accelerator presented idle time for the 26% of the different data sets that were used. It would be beneficial to implement a faster version of the HaplotypeCaller which can load data sets to the accelerator as current data sets are being processed.