Towards Exascale Computation for Turbomachinery Flows

A state-of-the-art large eddy simulation code has been developed to solve compressible flows in turbomachinery. The code has been engineered with a high degree of scalability, enabling it to effectively leverage the many-core architecture of the new Sunway system. A consistent performance of 115.8 DP-PFLOPs has been achieved on a high-pressure turbine cascade consisting of over 1.69 billion mesh elements and 865 billion Degree of Freedoms (DOFs). By leveraging a high-order unstructured solver and its portability to large heterogeneous parallel systems, we have progressed towards solving the grand challenge problem outlined by NASA, which involves a time-dependent simulation of a complete engine, incorporating all the aerodynamic and heat transfer components.Comment: SC23, November, 2023, Denver, CO., US

Extended tabu search-based scheduling to improve profitability in heterogeneous parallel systems

Higher utilization of existing resources and facilities in order to increase efficiency and profitability is always one of the basic challenges for parallel processing systems and environments, and this challenge becomes more complicated when the system resources are heterogeneous. One way to achieve high efficiency and profitability of heterogeneous parallel systems is to schedule tasks optimally. In this paper, an extended tabu search-based scheduling algorithm (ESTS) is presented to improve the profitability of heterogeneous parallel systems, which can achieve suitable solutions in a short computational time. To evaluate the efficiency of the proposed solution, due to the lack of a suitable criterion to evaluate this problem, the obtained results are compared with both the results of an extended scheduling based on a genetic algorithm (ESGA) with a large number of chromosomes and a high number of generations, as well as an extended scheduling based on a simulated annealing algorithm (ESSA) with a linear temperature reduction. The benchmark files of different sizes were tested under the same conditions, and the comparison of results shows the superiority of the proposed solution in terms of profitability and computational time

Model-based Development of Enhanced Ground Proximity Warning System for Heterogeneous Multi-Core Architectures

The aerospace domain, very much similar to other cyber-physical systems domains such as automotive or automation, is demanding new methodologies and approaches for increasing performance and reducing cost, while maintaining safety levels and programmability. While the heterogeneous multi-core architectures seem promising, apart from certification issues, there is a solid necessity for complex toolchains and programming processes for exploiting their full potential. The ARGO (WCET-Aware PaRallelization of Model-Based Ap-plications for HeteroGeneOus Parallel Systems) project is addressing this challenge by providing an inte-grated toolchain that realizes an innovative holistic approach for programming heterogeneous multi-core sys-tems in a model-based workflow. Model-based design elevates systems modeling and promotes simulation with the executing these models for verification and validation of the design decisions. As a case study, the ARGO toolchain and workflow will be applied to a model-based Enhanced Ground Proximity Warning System (EGPWS) development. EGPWS is a readily available system in current aircraft which provides alerts and warnings for obstacles and terrain along the flight path utilizing high resolution terrain databases, Global Positioning System and other sensors-. After a gentle introduction to the model-based development approach of the ARGO project for the heterogeneous multi-core architectures, the EGPWS and the EGPWS systems modelling will be presented

High-Level Programming for Medical Imaging on Multi-GPU Systems Using the SkelCL Library

Application development for modern high-performance systems with Graphics Processing Units (GPUs) relies on low-level programming approaches like CUDA and OpenCL, which leads to complex, lengthy and error-prone programs. In this paper, we present SkelCL – a high-level programming model for systems with multiple GPUs and its implementation as a library on top of OpenCL. SkelCL provides three main enhancements to the OpenCL standard: 1) computations are conveniently expressed using parallel patterns (skeletons); 2) memory management is simplified using parallel container data types; 3) an automatic data (re)distribution mechanism allows for scalability when using multi-GPU systems. We use a real-world example from the field of medical imaging to motivate the design of our programming model and we show how application development using SkelCL is simplified without sacrificing performance: we were able to reduce the code size in our imaging example application by 50% while introducing only a moderate runtime overhead of less than 5%