Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ahead-of-time data layout optimization by vertex reordering is a widely used technique to improve memory access locality in graph analysis. While reordered graphs yield better analysis performance, the existing reordering algorithms use significant amounts of computation time to provide efficient vertex ordering, hence, they fail to reduce end-to-end processing time.  presents a first algorithm for just-in-time parallel reordering, named Rabbit Order. It reduces end-to-end runtime by achieving high locality and fast reordering at the same time through two approaches. The first approach is hierarchical community-based ordering, which exploits the locality derived from hierarchical community structures in real-world graphs. Our ordering fully leverages low-latency cache levels by mapping hierarchical communities into hierarchical caches. The second approach is parallel incremental aggregation, which improves the runtime efficiency of reordering by decreasing the number of vertices to be processed. In addition, this approach utilizes lightight atomic operations for concurrency control to avoid locking overheads and achieve high scalability. Our experiments show that Rabbit Order significantly outperforms state-of-the-art reordering algorithms.
['Junya Arai', 'Hiroaki Shiokawa', 'Takeshi Yamamuro', 'Makoto Onizuka', 'Sotetsu Iwamura']
I am writing a research paper on a idea Rabbit Order: Just-in-Time Parallel Reordering for Fast Graph Analysis
[{"from": "human", "value": "I am writing a research paper on a idea Rabbit Order: Just-in-Time Parallel Reordering for Fast Graph Analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ahead-of-time data layout optimization by vertex reordering is a widely used technique to improve memory access locality in graph analysis. While reordered graphs yield better analysis performance, the existing reordering algorithms use significant amounts of computation time to provide efficient vertex ordering, hence, they fail to reduce end-to-end processing time.  presents a first algorithm for just-in-time parallel reordering, named Rabbit Order. It reduces end-to-end runtime by achieving high locality and fast reordering at the same time through two approaches. The first approach is hierarchical community-based ordering, which exploits the locality derived from hierarchical community structures in real-world graphs. Our ordering fully leverages low-latency cache levels by mapping hierarchical communities into hierarchical caches. The second approach is parallel incremental aggregation, which improves the runtime efficiency of reordering by decreasing the number of vertices to be processed. In addition, this approach utilizes lightight atomic operations for concurrency control to avoid locking overheads and achieve high scalability. Our experiments show that Rabbit Order significantly outperforms state-of-the-art reordering algorithms."}]