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MapGraph: A High Level API for Fast Development of High Performance Graph Analytics on GPUs
TLDR
MapGraph is presented, a high performance parallel graph programming framework that delivers up to 3 billion Traversed Edges Per Second on a GPU and is comparable to state-of-the-art, manually optimized GPU implementations. Expand
Architecting the finite element method pipeline for the GPU
TLDR
This paper proposes an efficient GPU-based algorithm to generate local element information and to assemble the global linear system associated with the FEM discretization of an elliptic PDE and proposes a new fine-grained parallelism strategy, a corresponding multigrid cycling stage and efficient data mapping to the many-core architecture of GPU. Expand
A Fast Iterative Method for Solving the Eikonal Equation on Tetrahedral Domains
TLDR
This paper presents a parallel algorithm for solving the eikonal equation on fully unstructured tetrahedral meshes, appropriate for the type of fine-grained parallelism found on modern massively-SIMD architectures such as graphics processors and takes into account the particular constraints and capabilities of these computing platforms. Expand
Exploiting Batch Processing on Streaming Architectures to Solve 2D Elliptic Finite Element Problems: A Hybridized Discontinuous Galerkin (HDG) Case Study
TLDR
It is demonstrated that the HDG method is well-suited for GPU implementation, obtaining total speedups on the order of 30–35 times over a serial CPU implementation for moderately sized problems. Expand
A Relaxation Method for Surface-Conforming Prisms
This paper presents a method for computing thin layers of high-quality, triangular prisms that conform to surfaces that are specified as level sets of an implicit function. Triangular prisms areExpand
A Fast Iterative Method for Solving the Eikonal Equation on Triangulated Surfaces
TLDR
A new local update scheme that provides solutions of first-order accuracy for both architectures and a novel triangle-based update scheme and its corresponding data structure for efficient irregular data mapping to parallel single-instruction multiple-data (SIMD) processors. Expand
Parallel Breadth First Search on GPU clusters
TLDR
Previous research on GPUs and on scalable graph processing on supercomputers is extended and it is demonstrated that a high-performance parallel graph machine can be created using commodity GPUs and networking hardware. Expand
Bayesian Segmentation of Atrium Wall Using Globally-Optimal Graph Cuts on 3D Meshes
TLDR
A surface-detection method capable of extracting the atrial wall by computing an optimal a-posteriori estimate on a set of nested meshes, constructed from an ensemble of segmented training images, and graph cuts on an associated multi-column, proper-ordered graph. Expand
GeaBase: A High-Performance Distributed Graph Database for Industry-Scale Applications
TLDR
GeaBase is a new distributed graph database that provides the capability to store and analyze graph-structured data in real-time at massive scale, including a novel update architecture, called Update Center (UC), and a new language that is suitable for both graph traversal and analytics. Expand
MapGraph - Graphprocessing at 30 Billion Edges per Second on NVIDIA GPUs
TLDR
This paper argues that graph algorithms are data-intensive, not compute intensive, and have degree dependent parallelism, which place an extreme burden on the memory bus and communications network, so many-core computing is the future. Expand
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