Modern heterogeneous computing platforms have become powerful HPC solutions, which could be applied to a wide range of real-life applications. In particular, the hybrid platforms equipped with Intel Xeon Phi coprocessors offer the advantages of massively parallel computing, while supporting practically the same parallel programming model as conventional homogeneous solutions. However, there is still an open issue as to how scientific applications can efficiently utilize hybrid platforms with Intel MIC coprocessors. In this article, we propose an approach for porting a real-life scientific application to such hybrid platforms, assuming no significant modifications of the application code. It allows us to take advantage of all the computing components, including two CPUs and two coprocessors, for the parallel execution of computational workloads. In this study, we focus on the parallel implementation of a numerical model of the dendritic solidification process in isothermal conditions. We develop a sequence of steps that are necessary for the porting and optimization of the solidification application to hybrid platforms with Intel coprocessors. The main challenges include not only overlapping data movements with computations, but also ensuring adequate utilization of cores/threads and vector units of processors, as well as coprocessors. To reach this aim, we propose an efficient and flexible method for the workload distribution between heterogeneous computing components. For implementing the potential benefits of the proposed approach, we choose a heterogeneous programming model based on a combination of the offload mode for Intel MIC and OpenMP programming standard. The developed approach allows us to execute the whole application up to 9.33 3 faster than the original parallel version that uses two CPUs. Furthermore, the CPU-MIC hybrid platforms enable achieving the speedup of about 1.9 3 that of the CPU platform with 24 cores based on the Ivy Bridge architecture, and about 1.5 3 that of the Haswell-based CPU platform with 36 cores.

MODELLING OF HEAT TREATMENT OF STEEL ELEMENTS WITH THE MOVEMENT OF COOLANT MODELOWANIE OBRÓBKI CIEPLNEJ ELEMENTÓW STALOWYCH Z UWZGLĘDNIENIEM RUCHÓW CHŁODZIWA A mathematical and numerical model of hardening process using the generalized finite difference method for the movement of fluid and heat transport have been proposed in this paper. To solve the Navier-Stokes equation the characteristic based split scheme (CBS) has been used. The solution of the heat transport equation with the convective term has been obtained by a stabilized meshless method. To determine of the phase transformation the macroscopic model built on the basis of CCT diagrams for continuous cooling of medium-carbon steel has been used. The temporary temperature fields, the phase transformation, thermal and structural strains for the heat treated element and the fields of temperature and velocity for the coolant have been determined. The comparative analysis of the results of calculations for the model without taking into account movement of coolant has been carried out. The effect of the notch in the shaft on the cooling rates and fields of the kinetics of the phase transformations has been presented.

No abstract

The motivation of the presented paper is the desire to create a universal tool to analyse the process of austenite decomposition during the cooling process of various steel grades. The presented analysis concerns the application of Recurrent Artificial Neural Networks (RANN) of the Long Short-Term Memory (LSTM) type for the analysis of the transition path of the cooling curve. This type of network was selected due to its ability to predict events in time sequences. The proposed generalisation allows for the determination of the austenite transformation during the continuous cooling process for various cooling curves. As training data for the neural network, values determined from the macroscopic model based on the analysis of Continuous Cooling Transformation (CCT) diagrams were used. All relations and analyses used to build training/testing or validation sets are presented in the paper. The modelling with the use of LSTM network gives the possibility to determine the incremental changes of phase transformation (in a given time step) with the assumed changes of temperature resulting from the considered cooling rate.

No abstract