Certainly! Here is a list of the graph and tree search algorithms that I mentioned earlier, ordered from easiest to hardest to implement in a parallel fashion using CUDA:

Breadth-first search (BFS) Depth-first search (DFS) Iterative deepening DFS (IDDFS) Kruskal's Floyd-Warshall Fringe search Jump point search A* B* Bellman-Ford Bidirectional search D* Dijkstra's Iterative deepening (IDA*) Johnson's Lifelong Planning A* (LPA*) Pathfinding SMA*

Keep in mind that the difficulty of implementing these algorithms in a parallel fashion will depend on several factors, including the complexity of the algorithm itself, the characteristics of the data structure being used, and the desired level of performance and scalability. Some algorithms may be easier to implement in a serial fashion and may not benefit significantly from parallelization, while others may require more sophisticated techniques to achieve good parallel performance.

I hope this list helps you plan your course material effectively! Let me know if you have any questions.