Acceleration of the Pair-HMM forward algorithm on FPGA with cloud integration for GATK

Abstract

The Pair-HMM 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 speed-up computations we propose an FPGA based 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× speed-up against the software implementation when used in-pipeline. The accelerator has also been tested in the experimental Spark (distributed) version of the GATK HaplotypeCaller tool. An in-depth analysis of the compute time contributions allowed to point out the main bottlenecks for accelerators in the GATK pipeline, resulting in a hybrid CPU-FPGA solution to best exploit both resources.

GAPiM: Discovering Genetic Variations on a Real Processing-in-Memory System

“…We compare the processing time of our solution with different CPU implementations, including the original Java implementation, the AVX-accelerated version, and the OpenMP multithreaded version (with 4 threads and 8 threads). Additionally, we present the time results of the open-source FPGA implementation from [34], which includes two versions, one with 24 "workers" and the second one with 96 "workers", where a worker is an acceleration unit (respectively represented as 24wk and 96wk). This Pair-HMM implementation is run on a F1 instance of AWS [54].…”

“…Most FPGA solutions use systolic arrays [31,32,33]. Another solution for FPGA is to create a specialized unit to execute the PairHMM command and replicate it to provide e cient parallelization [34]. But these FPGA solutions suffer from limited on-chip memory.…”

HPC-based genome variant calling workflow (HPC-GVCW)

“…With the ability to produce ultra-high-quality reference genomes and population-level resequencing data -at will -accelerated and parallel data processing methods must be developed to efficiently call genetic variation at scale. Some of the accelerated workflows include Sentieon 30 (a commercial license), Clara Parabricks 31 (NVIDIA GPU-based infrastructure), Falcon 32 (hybrid FPGA-CPU cloud-based software), and DRAGEN-GATK 33 (open source software recently made available through the Broad Institute cloud platform, https://broadinstitute.github.io/warp/) are the examples. These workflows require special hardware (e.g., GPUs, FPGAs) or a cloud computing platform to accelerate the data processing, and/or can be expensive to purchase.…”

GAPiM: Discovering Genetic Variations on a Real Processing-in-Memory System

“…Most FPGA solutions use systolic arrays [31,32,33]. Another solution for FPGA is to create a specialized unit to execute the Pair-HMM command and replicate it to provide efficient parallelization [34]. But these FPGA solutions suffer from limited on-chip memory.…”