Genomic Compute Trifecta

In this paper, we present the Genomic Compute Trifecta, which are the fundamental structural barriers to scalable computation of genomics and multi-omics workloads due to the complexity of the data structure and its incompatibility with the assumptions underpinning traditional computing architectures. Bioinformatics pipelines work over sparse graphs with nonuniform memory access patterns, dynamic control flow, and power-law distributions and control-heavy logic flows which lead clinical interpretability decreases logarithmically with computational burden due to system overhead, memory latency,and parallel speedups plateau due to Amdahl's law. This presents fundamental bottlenecks for fields that require real-time analytical capabilities such as precision medicine, AI-driven drug discovery, and clinical diagnostics. We establish that satisfying computational requirements alone is insufficient to ensure actionable clinical insight. As the biological state space expands superpolynomially, both analytical efficiency and signal fidelity degrade substantially. We derive a resource estimation formula to allow bioinformaticians to determine minimum computational thresholds for maintaining clinical return on investment (ROI).