Model-Based Parallelization for Simulink Models on Multicore CPUs and GPUs
DOI:
https://doi.org/10.24297/ijct.v20i.8533Keywords:
Multicore, MATLAB Simulink, GPU, Model-Based DevelopmentAbstract
In this paper we propose a model-based approach to parallelize Simulink models of image processing algorithms on homogeneous multicore CPUs and NVIDIA GPUs at the block level and generate CUDA C codes for parallel execution on the target hardware. In the proposed approach, the Simulink models are converted to directed acyclic graphs (DAGs) based on their block diagrams, wherein the nodes represent tasks of grouped blocks or subsystems in the model and the edges represent the communication behaviors between blocks. Next, a path analysis is conducted on the DAGs to extract all execution paths and calculate their respective lengths, which comprises the execution times of tasks and the communication times of edges on the path. Then, an integer linear programming (ILP) formulation is used to minimize the length of the critical path of the DAG, which represents the execution time of the Simulink model. The ILP formulation also balances workloads on each CPU core for optimized hardware utilization. We parallelized image processing models on a platform of two homogeneous CPU cores and two GPUs with our approach and observed a speedup performance between 8.78x and 15.71x.
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