Optimizing Performance of Combustion Chemistry Solvers on Intel's Many Integrated Core (MIC) Architectures

Research output: Contribution to conferencePaperpeer-review


This work investigates novel algorithm designs and optimization techniques for restructuring chemistry integrators in zero and multidimensional combustion solvers, which can then be effectively used on the emerging generation of Intel’s Many Integrated Core/Xeon Phi processors. These processors offer increased computing performance via large number of lightweight cores at relatively lower clock speeds compared to traditional processors (e.g. Intel Sandybridge/Ivybridge) used in current supercomputers. This style of processor can be productively used for chemistry integrators that form a costly part of computational combustion codes, in spite of their relatively lower clock speeds. Performance commensu-rate with traditional processors is achieved here through the combination of careful memory layout, exposing multiple levels of fine grain parallelism and through extensive use of vendor supported libraries (Cilk Plus and Math Kernel Libraries). Important optimization techniques for efficient memory usage and vectorization have been identified and quantified. These optimizations resulted in a factor of ~ 3 speed-up using Intel 2013 compiler and ~ 1.5 using Intel 2017 compiler for large chemical mechanisms compared to the unoptimized version on the Intel Xeon Phi. These strategies, especially with respect to memory usage and vectorization, should also be beneficial for general purpose computational fluid dynamics codes.

Original languageAmerican English
Number of pages12
StatePublished - 2017
Event23rd AIAA Computational Fluid Dynamics Conference, 2017 - Denver, United States
Duration: 5 Jun 20179 Jun 2017


Conference23rd AIAA Computational Fluid Dynamics Conference, 2017
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

NREL Publication Number

  • NREL/CP-2C00-68445


  • algorithms
  • optimization
  • supercomputers


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