The advent of generative design in the automotive sector, characterised by the automatic and iterative exploration of expansive solution spaces to discover optimal design configurations, has significantly increased the demand for computational resources to run intensive computer-aided engineering (CAE) simulations within constrained time frames. The inherent limitations of static high-performance computing (HPC) clusters have necessitated the adoption of cloud resources due to their flexible and elastic nature, thereby enhancing the capacity to accommodate the computational demands of these iterative workflows. These workflows, represented as Directed Acyclic Graphs (DAGs), involve the serial and parallel execution of tasks, which can dynamically share resources with other workflows during idle periods. In this paper, we propose an economy-based approach to exploit the gaps generated by these idle periods through a bidding system, thereby enabling more efficient resource utilisation and reducing the average wait time, makespan, cost and deadline miss by more than 40%, 6%, 13% and 45%respectively against certain infrastructures and baselines. Furthermore, we explore the potential for generating revenue by renting out idle resources in a hybrid cloud setup. This approach not only aims to optimise the use of computational resources but also seeks to provide cost-effective solutions to meet the escalating demands of generative design in the automotive sector.

We propose a novel approach to optimization of irregular nonbinary (NB) quasi-cyclic (QC)-LDPC codes over small alphabets. In this approach, first, the base parity-check matrices are constructed by a simulated annealing method, and then these matrices are labeled by the field elements while maximizing the so- called generalized girth of the Tanner graph. In order to analyze the performance of the constructed irregular NB LDPC codes, a new ensemble of irregular NB LDPC codes over the extensions of the binary Galois field is introduced. A finite-length random coding bound on the error probability of the maximum-likelihood (ML) decoding over the binary phase shift keying (BPSK) input AWGN channel for the new code ensemble is derived. The frame error rate (FER) performance of the sum-product belief-propagation (BP) decoding of the constructed NB QC-LDPC block codes is compared to that of both the optimized binary QC-LDPC block codes in the 5G standard and the best known NB QC-LDPC codes as well as to the derived random coding bound on the ML decoding error probability. It is shown that the obtained bound predicts the behavior of BP decoding performance of practical NB QC-LDPC codes more accurately than the BP decoding thresholds do.

We propose a novel approach for optimization of nonbinary (NB) quasi-cyclic (QC)-LDPC codes. In this approach, the base parity-check matrices are constructed by the simulated annealing method, and then labeled while maximizing the so-called generalized girth of the NB LDPC code Tanner graph. Random coding bounds on the ML decoding error probability for ensembles of "almost regular" NB LDPC codes of finite lengths over extensions of the binary Galois field are derived. These bounds are based on the average bit weight spectra for the ensembles of NB LDPC codes. The observed FER performance of the sum-product BP decoding of "almost regular" NB QC-LDPC block codes is presented and compared to the finite-length random coding bounds, as well as to the performance of the optimized binary QC-LDPC block code in the 5G standard. In the waterfall region, the gap between the finite-length bounds on the error probability of the ML decoding and the simulation performance of the BP decoding is about 0.1 – 0.2 dB.