Einladung: Informatik Oberseminar Markus Hoehnerbach
+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 23. Maerz 2020, 10.00 Uhr Ort: Raum 115, Rogowski-Gebaeude, Referent: Markus Hoehnerbach M.Sc. High-Performance and Automatic Computing Thema: A Framework for the Vectorization of Molecular Dynamics Kernels Abstract: We introduce a domain-specific language (DSL) for many-body potentials, which are used in molecular dynamics (MD) simulations in the area of materials science. We also introduce a compiler to translate the DSL into high-performance code suitable for modern supercomputers. We begin by studying ways to speedup up potentials on supercomputers using two case studies: The Tersoff and the AIREBO potentials. In both case studies, we identify a number of optimizations, both domain-specific and general, to achieve speedups of up to 5x; we also introduce a method to keep the resulting code performance portable. During the AIREBO case study, we also discover that the existing code contains a number of errors. This experience motivates us to include the derivation step, the most error-prone step in manual optimization, in our automation effort. After having identified beneficial optimization techniques, we create a ``potential compiler'', short PotC, which generates fully-usable performance-portable potential implementations from specifications written in our DSL. DSL code is significantly shorter (20x to 30x) than a manual code, reducing both manual work and opportunities to introduce bugs. We present performance results on five different platforms: Three CPU platforms (Broadwell, Knights Landing, and Skylake) and two GPU platforms (Pascal and Volta). While the performance in some cases remains far below that of hand-written code, it also manages to match or exceed manually written implementations in other cases. For these cases, we achieve speedups of up to 9x compared to non-vectorized code. Es laden ein: die Dozentinnen und Dozenten der Informatik
Sehr geehrte Damen und Herren, Die untenstehende Veranstaltung findet nicht statt. Eine neue Ankuendigung wird erfolgen, sobald ein neuer Termin feststeht. Mit freundlichen Gruessen, Markus Hoehnerbach On 06-Mar-20 20:56, Markus Hoehnerbach wrote:
+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +**********************************************************************
Zeit: Montag, 23. Maerz 2020, 10.00 Uhr Ort: Raum 115, Rogowski-Gebaeude,
Referent: Markus Hoehnerbach M.Sc. High-Performance and Automatic Computing
Thema: A Framework for the Vectorization of Molecular Dynamics Kernels
Abstract:
We introduce a domain-specific language (DSL) for many-body potentials, which are used in molecular dynamics (MD) simulations in the area of materials science. We also introduce a compiler to translate the DSL into high-performance code suitable for modern supercomputers.
We begin by studying ways to speedup up potentials on supercomputers using two case studies: The Tersoff and the AIREBO potentials. In both case studies, we identify a number of optimizations, both domain-specific and general, to achieve speedups of up to 5x; we also introduce a method to keep the resulting code performance portable.
During the AIREBO case study, we also discover that the existing code contains a number of errors. This experience motivates us to include the derivation step, the most error-prone step in manual optimization, in our automation effort.
After having identified beneficial optimization techniques, we create a ``potential compiler'', short PotC, which generates fully-usable performance-portable potential implementations from specifications written in our DSL. DSL code is significantly shorter (20x to 30x) than a manual code, reducing both manual work and opportunities to introduce bugs.
We present performance results on five different platforms: Three CPU platforms (Broadwell, Knights Landing, and Skylake) and two GPU platforms (Pascal and Volta). While the performance in some cases remains far below that of hand-written code, it also manages to match or exceed manually written implementations in other cases. For these cases, we achieve speedups of up to 9x compared to non-vectorized code.
Es laden ein: die Dozentinnen und Dozenten der Informatik
participants (1)
-
Markus Hoehnerbach