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Solving PDEs on unstructured grids is a cornerstone of engineering and scientific computing. Heterogeneous parallel platforms, including CPUs, GPUs, and FPGAs, enable energy-efficient and computationally demanding simulations.
In this article, we introduce the HPM C++-embedded DSL that bridges the abstraction gap between the mathematical formulation of mesh-based algorithms for PDE problems on the one hand and an increasing number of heterogeneous platforms with their different programming models on the other hand.
Thus, the HPM DSL aims at higher productivity in the code development process for multiple target platforms. We introduce the concepts as well as the basic structure of the HPM DSL, and demonstrate its usage with three examples. The mapping of the abstract algorithmic description onto parallel hardware, including distributed memory compute clusters, is presented.
A code generator and a matching back end allow the acceleration of HPM code with GPUs. Finally, the achievable performance and scalability are demonstrated for different example problems.
Solving partial differential equations on unstructured grids is a cornerstone of engineering and scientific computing. Nowadays, heterogeneous parallel platforms with CPUs, GPUs, and FPGAs enable energy-efficient and computationally demanding simulations. We developed the HighPerMeshes C++-embedded Domain-Specific Language (DSL) for bridging the abstraction gap between the mathematical and algorithmic formulation of mesh-based algorithms for PDE problems on the one hand and an increasing number of heterogeneous platforms with their different parallel programming and runtime models on the other hand. Thus, the HighPerMeshes DSL aims at higher productivity in the code development process for multiple target platforms. We introduce the concepts as well as the basic structure of the HighPer-Meshes DSL, and demonstrate its usage with three examples, a Poisson and monodomain problem, respectively, solved by the continuous finite element method, and the discontinuous Galerkin method for Maxwell’s equation. The mapping of the abstract algorithmic description onto parallel hardware, including distributed memory compute clusters is presented. Finally, the achievable performance and scalability are demonstrated for a typical example problem on a multi-core CPU cluster.
Performance Evaluation and Improvements of the PoCL Open-Source OpenCL Implementation on Intel CPUs
(2021)
The Portable Computing Language (PoCL) is a vendor independent open-source OpenCL implementation that aims to support a variety of compute devices in a single platform. Evaluating PoCL versus the Intel OpenCL implementation reveals significant performance drawbacks of PoCL on Intel CPUs – which run 92 % of the TOP500 list. Using a selection of benchmarks, we identify and analyse performance issues in PoCL with a focus on scheduling and vectorisation. We propose a new CPU device-driver based on Intel Threading Building Blocks (TBB), and evaluate LLVM with respect to automatic compiler vectorisation across work-items in PoCL. Using the TBB driver, it is possible to narrow the gap to Intel OpenCL and even outperform it by a factor of up to 1.3× in our proxy application benchmark with a manual vectorisation strategy.
Seit fast drei Jahren betreibt das Konrad-Zuse-Zentrum für Informationstechnik Berlin (ZIB) Parallelrechner der höchsten Leistungsklasse im normalen Rechenzentrumsbetrieb. Bereits im Mai 1995 hat das ZIB über seine Erfahrungen mit dem damals leistungsstärksten Parallelrechner Deutschlands berichtet. Das Gesamtkonzept des ZIB sieht weiterhin einen Höchstleistungsrechner als unabdingbaren Bestandteil des High Performance Scientific Computing (HPSC) im ZIB vor. Der vorliegende Bericht beschreibt die aktuelle Konfiguration, Betriebserfahrungen und die Rechnernutzung sowie typische Rechenleistungen, die für einzelne Anwendungsprogramme erzielt wurden. Beschreibungen der Forschungsgebiete mit den Forschungsgruppen, die den Rechner nutzen und die Anforderungen an den Rechnerausbau, die sich aus deren Arbeiten herleiten, beschließen den Bericht.