CORAL: Verification-Aware OpenCL Based Read Mapper for Heterogeneous Systems

Abstract

Genomics has the potential to transform medicine from reactive to a personalized, predictive, preventive and participatory (P4) form. Being a Big Data application with continuously increasing rate of data production, the computational costs of genomics have become a daunting challenge. Most modern computing systems are heterogeneous consisting of various combinations of computing resources, such as CPUs, GPUs and FPGAs. They require platform-specific software and languages to program making their simultaneous operation challenging. Existing read mappers and analysis tools in the whole genome sequencing (WGS) pipeline do not scale for such heterogeneity. Additionally, the computational cost of mapping reads is high due to expensive dynamic programming based verification, where optimized implementations are already available. Thus, improvement in filtration techniques is needed to reduce verification overhead. To address the aforementioned limitations with regards to the mapping element of the WGS pipeline, we propose a Cross-platfOrm Read mApper using opencL (CORAL). CORAL is capable of executing on heterogeneous devices/platforms simultaneously. It can reduce computational time by suitably distributing the workload without any additional programming effort. We showcase this on a quadcore Intel CPU along with two Nvidia GTX 590 GPUs, distributing the workload judiciously to achieve up to 2× speedup compared to when only CPUs are used. To reduce the verification overhead, CORAL dynamically adapts k-mer length during filtration. We demonstrate competitive timings in comparison with other mappers using real and simulated reads. CORAL

An FPGA Based Energy-Efficient Read Mapper With Parallel Filtering and In-Situ Verification

“…Software based read mappers include BWA-MEM, Bowtie, mrFAST, SOAP, RazerS, SHRiMP2, GEM, RazerS 3, Yara, Hobbes3, CORAL and GRIM-Filter [28]- [37], [53]. In the filtration stage of many of these read mappers, pigeonhole principle and q-gram lemma approaches are used [9], [38].…”

“…As short segments of the read are more likely found, the specificity of these filters is less. GEM mapper, RazerS 3, BitMapper, Hobbes3 and CORAL are the read mappers based on the pigeonhole principle [29], [34], [35], [37], [40]. A string of length q is called q-gram.…”

“…RazerS 3 is an allmapper and has the highest accuracy and sensitivity compared to other methods [29]. In the literature, it is used to build the gold standard for Rabema [58] accuracy benchmarking [33], [35], [37]. As such, due to its accuracy, sensitivity, and all-mapper capability, RazerS 3 [29] is chosen as a gold standard in the evaluation of mapping sensitivity of the proposed methodology.…”

PLEDGER: Embedded Whole Genome Read Mapping using Algorithm-HW Co-design and Memory-aware Implementation

“…1(a) visualizes the construction of FM-Index data structure for the string GAAATCGZATCATZACCGTG$. It involves the formation of tally matrix (TM) using F and L arrays obtained by applying the Burrows-Wheeler transform, refer [10] for more details. The length of TM is same as that of the genome and is the major bottleneck to low-memory implementations.…”

“…CORAL [10] demonstrates an OpenCL based heterogeneous read mapping scenario where workloads are distributed on available CPU+GPU for acceleration. It, however, uses a heuristic filtration methodology.…”