Bilkent University
Department of Computer Engineering
CS 590/690 SEMINAR
KENT: Fast Pathogen Screening with NVIDIA Jetson Cluster in Resource-Constrained Areas
Arda İçöz
Master Student
(Supervisor:Assoc.Prof.Can Alkan)
Computer Engineering Department
Bilkent University
Abstract: Metagenomic sequencing enables broad pathogen detection without requiring prior assumptions about the organisms present, but its analysis pipelines usually depend on desktop or server-class computing. This limits practical use in disaster-stricken and other resource-constrained environments, where portable sequencing may be available (e.g., with MinION) but reliable access to cloud or laboratory infrastructure is not. We present a portable edge-computing framework, KENT, for metagenomic pathogen screening based on a cluster of NVIDIA Jetson Nano GPUs. KENT adapts CuCLARK for low memory embedded execution and extends it with a custom software stack for distributed read classification, abundance estimation, split-run abundance merging, and report generation. Using an MPI-based controller/responder architecture, KENT supports local, offline analysis across multiple nodes. We evaluate the system using long read wastewater metagenomic samples with a custom-built pathogen database, and using the CAMI2 Marine long read dataset and its corresponding reference set. In the wastewater setting, KENT achieves an average 3.1× speedup over the CPU-based CLARK-l baseline while preserving close agreement with CLARK-l abundance estimates, with an average absolute difference of approximately 0.12% in classified-read abundance. On the CAMI2 Marine dataset, KENT again remained highly consistent with CLARK-l, with a mean absolute difference of approximately 0.01%, and achieved an average speedup of about 2.68× across matched runs. Overall, the results indicate that GPU-accelerated metagenomic classification with KENT can be adapted to low-power embedded platforms while preserving baseline abundance estimates, supporting portable and offline pathogen screening in resource-constrained settings.
DATE: March 30, Monday @ 15:50 Place: EA 502