high performance computing on graphics processing units: hgpu.org

Modern partial differential equation (PDE) models across scientific disciplines require sophisticated numerical methods resulting in complex codes as well as large numbers of simulations for analysis like parameter studies and uncertainty quantification. To evaluate the behavior of the model for sufficeintly long times, for instance, to compare to laboratory time scales, often requires long-time simulations with small time steps and high mesh resolutions. This motivates the need for very efficient numerical methods and the use of parallel computing on the most recent modern architectures. We use complex code resulting from a PDE model of calcium dynamics in a heart cell to analyze the performance of the recently released Intel Xeon Phi Knights Landing (KNL). The KNL is a second-generation many-integrated-core (MIC) processor released in 2016 with a theoretical peak performance of over 3 TFLOP/s of double-precision floating-point operations for which complex codes can be easily ported because of the x86 compatibility of each KNL core. We demonstrate the benefit of hybrid MPI+OpenMP code when implemented effectively and run efficiently on the KNL including on multiple KNL nodes. For multi-KNL runs for our sample code, it is shown to be optimal to use all cores of each KNL, one MPI process on every other tile, and only two of the maximum of four threads per core.