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Parallel CPU-GPU Framework for Cost-Bounded DFS
Mentored by: Next Silicon
Hybrid CPU-GPU framework for parallel depth-first search with cost constraints
C++
CUDA
OpenMP
GPU Computing
Graph Algorithms
Parallel Computing
Description
A sophisticated parallel computing framework that leverages both CPU and GPU resources for efficient depth-first search (DFS) traversal with cost constraints. Optimizes workload distribution, minimizes data transfer overhead, and provides adaptive load balancing. Suitable for large-scale graph exploration and constraint satisfaction problems.
Team Members
Cohort: Embedded Systems Bootcamp 2025 (Embedded)