2019 · 22 citations · 39 references
ComputingHigh-performance ComputingMaterials ScienceComputational Materials ScienceParallel ComputingScientific ComputingComputational Solid MechanicsData ParallelismMaterial ComputationMaterial ModelingMesh GenerationPhase-field ModelingCode OptimizationComputational Phase-field ModelingHigh-throughput ComputationParallel Mesh GenerationPhase-field ThermodynamicsScientific Computing On GpusTime-stepping Schemes
This article describes the development of automatic program generation technology to create scalable phase-field methods for material science applications. To simulate the formation of microstructures in metal alloys, we employ an advanced, thermodynamically consistent phase-field method. A state-of-the-art large-scale implementation of this model requires extensive, time-consuming, manual code optimization to achieve unprecedented fine mesh resolution. Our new approach starts with an abstract description based on free-energy functionals which is formally transformed into a continuous PDE and discretized automatically to obtain a stencil-based time-stepping scheme. Subsequently, an automatized performance engineering process generates highly optimized, performance-portable code for CPUs and GPUs. We demonstrate the efficiency for real-world simulations on large-scale GPU-based (PizDaint) and CPU-based (SuperMUC-NG) supercomputers. Our technique simplifies program development and optimization for a wide class of models.
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SymPy: symbolic computing in Python
Aaron Meurer, Christopher P. Smith, Mateusz Paprocki et al. · PeerJ Computer Science · 2017 · 1.6K citations · Full text