Skip to Main content Skip to Navigation
Conference papers

Multi and Many-core Parallel B&B approaches for the Blocking Job Shop Scheduling Problem

Abstract : 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 Mas-ter/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.
Complete list of metadata

Cited literature [24 references]  Display  Hide  Download

https://hal.laas.fr/hal-02115637
Contributor : Didier El Baz <>
Submitted on : Tuesday, April 30, 2019 - 1:52:46 PM
Last modification on : Thursday, June 10, 2021 - 3:01:21 AM

File

Multi and Many-core Parallel B...
Files produced by the author(s)

Identifiers

Citation

Adel Dabah, Ahcène Bendjoudi, Abdelhakim Aitzai, Didier El Baz, Nadia Taboudjemat. Multi and Many-core Parallel B&B approaches for the Blocking Job Shop Scheduling Problem. International Conference on High Performance Computing & Simulation (HPCS 2016), Jul 2016, Innsbruck, Austria. 8p., ⟨10.1109/HPCSim.2016.7568404⟩. ⟨hal-02115637⟩

Share

Metrics

Record views

102

Files downloads

208