@techreport{oai:ipsj.ixsq.nii.ac.jp:02003152,
 author = {Zhengyang,Bai and Peng,Chen and Lingqi,Zhang and Chen,Zhuang and Tenindra,Abeywickrama and Jing,Xu and Du,Wu and Emil,Vatai and Mohamed,Wahib and Zhengyang Bai and Peng Chen and Lingqi Zhang and Chen Zhuang and Tenindra Abeywickrama and Jing Xu and Du Wu and Emil Vatai and Mohamed Wahib},
 issue = {15},
 month = {Jul},
 note = {Directed acyclic graphs (DAGs), where all edges are directed and the graph is cycle-free, play an essential role in various domains, such as phylogenetic networks in biology, causal inference in social science, and cutting-edge technologies like task scheduling in large-scale computer systems. However, most traditional DAG algorithms are complex and difficult to accelerate because of their irregular data access patterns and control-flow divergency that do not align well with GPUs' SIMT (Single Instruction, Multiple Threads) architecture. However, recent advances in GPU architecture offer new opportunities to overcome these issues by ray tracing cores, which were originally designed for rendering tasks but have enhanced GPU's ability to perform divergences that can be efficiently applied to DAG-related problems. In this work, we present a novel approach to modeling DAG problems as rendering tasks. We employ GPU ray tracing cores to efficiently explore the complex relationships within DAGs. In addition, we propose a general workflow for processing this graphical ray casting representation and demonstrate its practical implementation via a case study on critical path detection., Directed acyclic graphs (DAGs), where all edges are directed and the graph is cycle-free, play an essential role in various domains, such as phylogenetic networks in biology, causal inference in social science, and cutting-edge technologies like task scheduling in large-scale computer systems. However, most traditional DAG algorithms are complex and difficult to accelerate because of their irregular data access patterns and control-flow divergency that do not align well with GPUs' SIMT (Single Instruction, Multiple Threads) architecture. However, recent advances in GPU architecture offer new opportunities to overcome these issues by ray tracing cores, which were originally designed for rendering tasks but have enhanced GPU's ability to perform divergences that can be efficiently applied to DAG-related problems. In this work, we present a novel approach to modeling DAG problems as rendering tasks. We employ GPU ray tracing cores to efficiently explore the complex relationships within DAGs. In addition, we propose a general workflow for processing this graphical ray casting representation and demonstrate its practical implementation via a case study on critical path detection.},
 title = {Graphical Ray Casting Representation of Directed Acyclic Graph in Critical Path Detection},
 year = {2025}
}