Leveraging LS-DYNA Explicit and Implicit on Latest Intel Technologies
In this paper we discuss Intel’s continued optimization efforts with LS-DYNA® and demonstrate the impact of new Intel technologies. Two different approaches to exploit Intel® Advanced Vector Extensions 512 (AVX-512) are shown: LS-DYNA® Explicit using Intel compiler vectorization techniques and LS-DYNA® Implicit using Intel® Math Kernel Library (MKL) for accelerating dense matrix computational kernels. Numerical accuracy of simulation results for LS-DYNA® Explicit comparing Intel® SSE2 and Intel® AVX-512 is also explored. Finally, we reveal the benefits of Intel® Optane™ DC Persistent Memory technology for LS-DYNA® Implicit simulations. For our studies we used the Topcrunch benchmarks, ODB-10M & car2car models, for LS-DYNA® Explicit and AWE benchmarks, CYL1E6 & CL2E6 models, for LS-DYNA® Implicit
https://www.dynalook.com/conferences/12th-european-ls-dyna-conference-2019/hpc/meng_intel.pdf/view
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Leveraging LS-DYNA Explicit and Implicit on Latest Intel Technologies
In this paper we discuss Intel’s continued optimization efforts with LS-DYNA® and demonstrate the impact of new Intel technologies. Two different approaches to exploit Intel® Advanced Vector Extensions 512 (AVX-512) are shown: LS-DYNA® Explicit using Intel compiler vectorization techniques and LS-DYNA® Implicit using Intel® Math Kernel Library (MKL) for accelerating dense matrix computational kernels. Numerical accuracy of simulation results for LS-DYNA® Explicit comparing Intel® SSE2 and Intel® AVX-512 is also explored. Finally, we reveal the benefits of Intel® Optane™ DC Persistent Memory technology for LS-DYNA® Implicit simulations. For our studies we used the Topcrunch benchmarks, ODB-10M & car2car models, for LS-DYNA® Explicit and AWE benchmarks, CYL1E6 & CL2E6 models, for LS-DYNA® Implicit
Meng_Intel.pdf
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