Democratizing digital design and manufacturing using high performance cloud computing: Performance evaluation and benchmarking

Dazhong Wu, Xi Liu, Steve Hebert, Wolfgang Gentzsch, Janis Terpenny

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Cloud computing is an innovative computing paradigm that can bridge the gap between increasing computing demands in computationally intensive tasks for digital design and manufacturing applications and limited resources, scalability, flexibility, and agility in traditional computing paradigms. In light of the benefits of cloud computing, cloud-based high performance computing (HPC) has the potential to enable users to not only accelerate computationally expensive tasks, but also to reduce costs by utilizing on-demand, ubiquitous, seamless, and user-friendly access to remote engineering application packages as well as remote HPC resources. However, due to uncertainty about computing performance on the cloud, many manufacturers find it challenging to justify and adopt Cloud-Based Design and Manufacturing (CBDM). Therefore, the objective of this research is to evaluate the performance of solving a large-scale engineering problem using finite element analysis on several public HPC clouds as well as introduce a new workflow for CBDM. A set of experiments is conducted to compare the performance of the public HPC clouds with that of a standard workstation and a dedicated in-house supercomputer. The performance metrics include elapsed time, speedup, scalability, and stability. Experimental results have shown that the Azure Cloud with 32 cores and the Nimbix Cloud with 16 nodes speed up the finite element analysis over a workstation with 8 cores by more than seven-fold and eight-fold. A dedicated in-house supercomputer speeds up the finite element analysis over cloud computing by approximately two-fold because of better I/O performance and larger memory. In addition, considerable variations of elapsed time for solving the finite element model with multiple nodes in the cloud were observed due to resource sharing in cloud computing.

Original languageEnglish (US)
Pages (from-to)316-326
Number of pages11
JournalJournal of Manufacturing Systems
StatePublished - Apr 1 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Hardware and Architecture
  • Industrial and Manufacturing Engineering


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