Mars: a MapReduce framework on graphics processors
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Accelerating large graph algorithms on the GPU using CUDA
HiPC'07 Proceedings of the 14th international conference on High performance computing
Scalable Graph Exploration on Multicore Processors
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
Accelerating CUDA graph algorithms at maximum warp
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
GViewer: GPU-accelerated graph visualization and mining
SocInfo'11 Proceedings of the Third international conference on Social informatics
Simulation of Information Propagation over Complex Networks: Performance Studies on Multi-GPU
DS-RT '13 Proceedings of the 2013 IEEE/ACM 17th International Symposium on Distributed Simulation and Real Time Applications
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Graphs are the de facto data structures for many applications, and efficient graph processing is a must for the application performance. GPUs have an order of magnitude higher computational power and memory bandwidth compared to CPUs and have been adopted to accelerate several common graph algorithms. However, it is difficult to write correct and efficient GPU programs and even more difficult for graph processing due to the irregularities of graph structures. To address those difficulties, we propose a programming framework named Medusa to simplify graph processing on GPUs. Medusa offers a small set of APIs, based on which developers can define their application logics by writing sequential code without awareness of GPU architectures. The Medusa runtime system automatically executes the developer defined APIs in parallel on the GPU, with a series of graph-centric optimizations. This poster gives an overview of Medusa, and presents some preliminary results.