Multi and Many-core Parallel B&B approaches for the Blocking Job Shop Scheduling Problem
Ait Zai, Abdelhakim
El Baz, Didier
In this paper, we propose three approaches to accelerate the B&B execution time using Multi and Many-core systems to solve the NP-hard Blocking Job Shop Scheduling problem (BJSS). The first approach is based on Master/Worker paradigm where the workers independently explore the branches sent by the master. The second approach is a node-based parallelization that does not change the design of the B&B algorithm, except that the bounding process is faster since it is calculated in parallel using several threads organized in one GPU block. The third approach is a Multi-Core CPU/GPU hybridization that benefits from the power of both the CPU-cores and the GPU at the same time. This hybridization is based on concurrent kernels execution provided by Nvidia Multi process Service (MPS) i.e. each host process (Master or Worker) launches his own kernel to accelerate the bounding process on GPU. The obtained results using Taillard instances confirm the efficiency of our proposals. The first two approaches are respectively three and eighteen times faster compared to the sequential version. The results of the hybrid approach show a relative speedup over ninety times as compared to the sequential approach and therefore prove the advantage of using both the CPU-cores and the GPU at the same time.
Job Shop blocking with swap, GPU Computing, Multi-core CPU, Parallel computing, Branch-and-Bound