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GraphBIG: understanding graph computing in the context of industrial solutions
This paper characterized GraphBIG on real machines and observed extremely irregular memory patterns and significant diverse behavior across different computations, helping users understand the impact of modern graph computing on the hardware architecture and enables future architecture and system research.
Efficient Multi-training Framework of Image Deep Learning on GPU Cluster
A framework to organize the training procedures of multiple deep learning models into a pipeline on a GPU cluster, where each stage is handled by a particular GPU with a partition of the training dataset is proposed.
Explore Efficient Data Organization for Large Scale Graph Analytics and Storage
This work develops a graph processing system called System G, which explores efficient graph data organization for parallel computing architectures, and discusses various graph data organizations and their impact on data locality during graph traversals, which results in various cache performance behavior on processor side.
Multi-modality Mobile Image Recognition Based on Thermal and Visual Cameras
This paper proposes an effective approach to align image pairs for event detection on mobile through image recognition, and leverages thermal and visual cameras as multi-modality sources for image recognition.
Mapping Multi-Layer Baysian LDA to Massively Parallel Supercomputers
Results from the empirical evaluation indicate the use of dual floating-point unit contr ibutes to a significant perf ormance gain, and thus it should be considered in the design of processors f or computationally intensive machine learning applications.