GBTL-CUDA: Graph Algorithms and Primitives for GPUs
| Autor: | Marcin Zalewski, Scott McMillan, Andrew Lumsdaine, Samantha Misurda, Peter Zhang |
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| Rok vydání: | 2016 |
| Předmět: |
Programming language
Computer science Computation Graphics processing unit 010103 numerical & computational mathematics 02 engineering and technology Parallel computing computer.software_genre Data structure 01 natural sciences Graph CUDA 020204 information systems Linear algebra 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) Distributed memory 0101 mathematics General-purpose computing on graphics processing units computer Implementation |
| Zdroj: | IPDPS Workshops |
| Popis: | GraphBLAS is an emerging paradigm for graph computation that makes it easy to program new graph algorithms in a highly abstract language of linear algebra. The promise of GraphBLAS is that an abstract graph program will execute in a wide variety of programming environments, ranging from embedded environments to distributed memory computers. In this paper we present our initial implementation of GraphBLAS primitives for graphics processing unit (GPU) systems called GraphBLAS Template Library (GBTL). Our implementation is an ongoing effort in the context of GraphBLAS standardization efforts by a diverse group of academics and representatives of the industry. Our implementation consists of a high-level C ++ frontend, and the GPU functionality is implemented with a combination of the CUSP library for sparse-matrix computation on GPU and the NVIDIA Thrust framework for abstract GPU programs. We give initial performance results of our implementations, and we discuss solutions to the problems we encountered when providing a low-level implementation for a high-level generic interface. |
| Databáze: | OpenAIRE |
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