Understanding the Performance Behaviors of End-to-End Protein Design Pipelines on GPUs
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🤖 AI Summary
Current end-to-end protein design pipelines lack systematic performance characterization of their heterogeneous compute behavior on GPUs, leading to suboptimal resource utilization. This work presents the first multi-granularity performance profiling—spanning both individual components and the full pipeline—across varying input sizes and hyperparameters. Our analysis reveals consistently low GPU utilization and strong performance dependence on sequence length and sampling strategies. Based on these insights, we identify key performance bottlenecks and release a fully open-source implementation along with dedicated profiling tools. This effort establishes a foundational benchmark and optimization framework for future high-performance protein design systems.
Application Category
📝 Abstract
Recent computational advances enable protein design pipelines to run end-to-end on GPUs, yet their heterogeneous computational behaviors remain undercharacterized at the system level. We implement and profile a representative pipeline at both component and full-pipeline granularities across varying inputs and hyperparameters. Our characterization identifies generally low GPU utilization and high sensitivity to sequence length and sampling strategies. We outline future research directions based on these insights and release an open-source pipeline and profiling scripts to facilitate further studies.
Problem
Research questions and friction points this paper is trying to address.
protein design
GPU utilization
performance characterization
end-to-end pipeline
computational behavior
Innovation
Methods, ideas, or system contributions that make the work stand out.
GPU profiling
protein design pipeline
performance characterization
end-to-end GPU execution
computational heterogeneity
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