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Approximate computing, skeleton programming and run-time scheduling in an algorithm for process design and controllability in distributed and heterogeneous infrastructures
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DOI：10.1016/j.compchemeng.2022.107874.png)
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Abstract
En 中文
A hybrid algorithm is proposed, comprising Simulated Annealing (SA), an NLP algorithm (IPOPT) and a continuation method (PITCON) for simultaneous process design and controllability assessment. The algorithm integrates the approximate computing techniques of memoization, task dropping and loop perfo-ration. SA and process model calculations are parallelized through skeleton programming (SkePU) and a tool for dynamic, run-time scheduling (StarPU). The same code is ported across different programming interfaces (OpenMP, CUDA, OpenCL and StarPU-SkepU MPI) and executed across different accelerators (multi-node, multi-CPU, GPU and multi-CPU-GPU). Case studies on CO2 capture system design include simultaneous solvent selection, process synthesis and controllability assessment. Up to 70% improvement is attained in the optimal solution, with up to 74% fewer unconverged model simulations. Excellent scal-ability is observed in 1,0 0 0 threads, attaining up to 60 times faster execution in multi-CPU, GPU and multi-CPU-GPU accelerators. A trade-off is observed between the CPU energy consumption and the execution speedup. (C) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Process design
Simulated annealing
Parallel computing
Sensitivity analysis
Static controllability
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