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The current GPU execution and memory models are somehow limited (memory limit, limit of data structures, no recursion...).
Do you think it would be feasible to implement a graph theory problem on a GPU? For example, vertex cover? dominating set? independent set? max clique?....
Is it also feasible to have branch-and-bound algorithms on GPUs? Recursive backtracking?
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Efficient Array Management in GPU. A scalable GPU algorithm design boils down to array management strategies inside CPU-GPU architecture. This step discusses management and mapping of data to CUDA compute units to achieve fine grained parallelism.
Some Common Graph AlgorithmsBreadth First Search (BFS) Depth First Search (DFS) Dijkstra. Floyd-Warshall Algorithm.
Graph algorithms are used to solve the problems of representing graphs as networks like airline flights, how the Internet is connected, or social network connectivity on Facebook. They are also popular in NLP and machine learning to form networks.
CUDA graphs are a model for work submission in CUDA that help improve this situation. A graph is a series of operations (such as kernel launches) connected by dependencies, which is defined separately from its execution. This allows a graph to be defined once and then launched repeatedly.
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Exploring the Limits of GPUs With Parallel Graph Algorithms
Accelerating large graph algorithms on the GPU using CUDA.
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