One machine, one minute, three billion tetrahedra

This paper presents a new scalable parallelization scheme to generate the 3D Delaunay triangulation of a given set of points. Our first contribution is an efficient serial implementation of the incremental Delaunay insertion algorithm. A simple dedicated data structure, an efficient sorting of the points and the optimization of the insertion algorithm have permitted to accelerate reference implementations by a factor three. Our second contribution is a multi-threaded version of the Delaunay kernel that is able to concurrently insert vertices. Moore curve coordinates are used to partition the point set, avoiding heavy synchronization overheads. Conflicts are managed by modifying the partitions with a simple rescaling of the space-filling curve. The performances of our implementation have been measured on three different processors, an Intel core-i7, an Intel Xeon Phi and an AMD EPYC, on which we have been able to compute 3 billion tetrahedra in 53 seconds. This corresponds to a generation rate of over 55 million tetrahedra per second. We finally show how this very efficient parallel Delaunay triangulation can be integrated in a Delaunay refinement mesh generator which takes as input the triangulated surface boundary of the volume to mesh

Fractal curve accelerated study of granular surface tension

In this thesis we look at a performance bottleneck of running molecular dynamics code on GPGPU devices (specifically the CUDA platform), namely random memory access into global graphics card memory. We offer a solution that involves reordering memory blocks in order to enable more memory reads from the local cache (shared memory) instead of the global memory. For determining a memory block order that sufficiently increases performance a space-filling curve was used. Significant performance increases for higher filling fractions were observed with a molecular dynamics simulation that was written from scratch

Highly accelerated simulations of glassy dynamics using GPUs: caveats on limited floating-point precision

Modern graphics processing units (GPUs) provide impressive computing resources, which can be accessed conveniently through the CUDA programming interface. We describe how GPUs can be used to considerably speed up molecular dynamics (MD) simulations for system sizes ranging up to about 1 million particles. Particular emphasis is put on the numerical long-time stability in terms of energy and momentum conservation, and caveats on limited floating-point precision are issued. Strict energy conservation over 10^8 MD steps is obtained by double-single emulation of the floating-point arithmetic in accuracy-critical parts of the algorithm. For the slow dynamics of a supercooled binary Lennard-Jones mixture, we demonstrate that the use of single-floating point precision may result in quantitatively and even physically wrong results. For simulations of a Lennard-Jones fluid, the described implementation shows speedup factors of up to 80 compared to a serial implementation for the CPU, and a single GPU was found to compare with a parallelised MD simulation using 64 distributed cores.Comment: 12 pages, 7 figures, to appear in Comp. Phys. Comm., HALMD package licensed under the GPL, see http://research.colberg.org/projects/halm

Achieving High Speed CFD simulations: Optimization, Parallelization, and FPGA Acceleration for the unstructured DLR TAU Code

Today, large scale parallel simulations are fundamental tools to handle complex problems. The number of processors in current computation platforms has been recently increased and therefore it is necessary to optimize the application performance and to enhance the scalability of massively-parallel systems. In addition, new heterogeneous architectures, combining conventional processors with specific hardware, like FPGAs, to accelerate the most time consuming functions are considered as a strong alternative to boost the performance. In this paper, the performance of the DLR TAU code is analyzed and optimized. The improvement of the code efficiency is addressed through three key activities: Optimization, parallelization and hardware acceleration. At first, a profiling analysis of the most time-consuming processes of the Reynolds Averaged Navier Stokes flow solver on a three-dimensional unstructured mesh is performed. Then, a study of the code scalability with new partitioning algorithms are tested to show the most suitable partitioning algorithms for the selected applications. Finally, a feasibility study on the application of FPGAs and GPUs for the hardware acceleration of CFD simulations is presented

Systematically Exploring High-Performance Representations of Vector Fields Through Compile-Time Composition

We present a novel benchmark suite for implementations of vector fields in high-performance computing environments to aid developers in quantifying and ranking their performance. We decompose the design space of such benchmarks into access patterns and storage backends, the latter of which can be further decomposed into components with different functional and non-functional properties. Through compile-time meta-programming, we generate a large number of benchmarks with minimal effort and ensure the extensibility of our suite. Our empirical analysis, based on real-world applications in high-energy physics, demonstrates the feasibility of our approach on CPU and GPU platforms, and highlights that our suite is able to evaluate performance-critical design choices. Finally, we propose that our work towards composing vector fields from elementary components is not only useful for the purposes of benchmarking, but that it naturally gives rise to a novel library for implementing such fields in domain applications

Systematically Exploring High-Performance Representations of Vector Fields Through Compile-Time Composition

We present a novel benchmark suite for implementations of vector fields in high-performance computing environments to aid developers in quantifying and ranking their performance. We decompose the design space of such benchmarks into access patterns and storage backends, the latter of which can be further decomposed into components with different functional and non-functional properties. Through compile-time meta-programming, we generate a large number of benchmarks with minimal effort and ensure the extensibility of our suite. Our empirical analysis, based on real-world applications in high-energy physics, demonstrates the feasibility of our approach on CPU and GPU platforms, and highlights that our suite is able to evaluate performance-critical design choices. Finally, we propose that our work towards composing vector fields from elementary components is not only useful for the purposes of benchmarking, but that it naturally gives rise to a novel library for implementing such fields in domain applications.</p

From 3D Models to 3D Prints: an Overview of the Processing Pipeline

Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step, in particular along the Process Planning pipeline, can now count on dozens of methods that prepare the 3D model for fabrication, while analysing and optimizing geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and Innovation action; Grant agreement N. 68044