Dual-Wheel Adaptive Wave Sieve (DAWS): Structural Elimination and Adaptive Navigation in Large-Scale Prime Sieving

Zenodo (CERN European Organization for Nuclear Research) 2026 Thesis Open access

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Authors: Toru Kawahara
DOI: 10.5281/zenodo.18163362
Cited by: 0

Abstract

This paper introduces DAWS, a hybrid GPU–CPU framework for large-scale prime sieving based on structural elimination and adaptive workload partitioning.By avoiding unnecessary composite evaluation through constant-cost preprocessing and sparsity-aware traversal, the method exhibits a qualitative shift in computational behavior at large scales.

Fields of Study & Subjects

Electromagnetic Scattering and Analysis Parallel Computing and Optimization Techniques Matrix Theory and Algorithms

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
6 January 2026
Document type
Thesis