This paper proposes and experimentally evaluates a multi-level CPU-oriented execution method for high-throughput portion-based processing of file-backed Variant Call Format (VCF) data and automated mutation classification. The approach is based on a formally defined local processing scheme and integrates three coordinated levels of parallelism: block-based partitioning of file-backed VCF portions read sequentially into localized fragments with data-level parallel processing; task-level decomposition of feature construction into independent transformations; and execution-level specialization via JIT compilation of numerical kernels. To prevent performance degradation caused by nested parallelism, a resource-control mechanism is introduced as an execution rule that bounds effective parallelism and mitigates oversubscription, improving throughput stability on a single multi-core CPU node. Experiments on a public chromosome-17 VCF dataset for BRCA1-region pathogenicity classification demonstrate that the proposed multi-level local CPU execution (parsing/filtering, feature construction, and JIT-specialized numeric kernels) reduces runtime from 291.25 s (sequential) to 73.82 s, yielding a 3.95× speedup. When combined with resource-coordinated parallel model training, the end-to-end runtime further decreases to 51.18 s, corresponding to a 5.69× speedup, while preserving classification quality (accuracy 0.8483, precision 0.8758, recall 0.8261, F1 0.8502). A stage-wise ablation analysis quantifies the contribution of each execution level and confirms consistent scaling under resource-bounded execution.
Mochurad et al. (Sun,) studied this question.
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