Performance Modeling and Analysis of a de Bruijn Graph Based Local Assembly Kernel on Multiple Vendor GPUs

Bioinformatics workloads differ significantly from traditional scientific computing and AI workloads because they consist primarily of integer-only operations and string comparisons rather than floating-point operations. The underlying algorithms usually have low arithmetic intensity, irregular memory access patterns, and non-deterministic workloads. Local Assembly is an essential step in large-scale genome assembly software and is typically implemented using de Bruijn graphs. This paper examines the performance, portability, and productivity of a local assembly GPU kernel from a metagenome assembly pipeline implemented using hash table data structures on NVIDIA, AMD, and Intel GPUs. We focus on the challenges of achieving portability while maintaining performance for a complex bioinformatics GPU kernel that relies on hardware-specific optimizations. In this paper, we evaluate the local assembly kernel's performance and portability across different GPU architectures, identify performance bottlenecks, and propose modifications in existing tools and methods for performance modeling and analysis of integer-heavy bioinformatics application kernels.