Jinsung Kim

TL;DR: An in-depth analysis is presented to contrast SpMV and SpMM, and a new sparse-matrix representation and computation approach suited to achieving high data-movement efficiency and effective GPU parallelization of SpMM is developed.

TL;DR: This paper develops an approach to graph processing on GPUs that seeks to overcome some of the performance limitations of existing frameworks, and uses multiple data representation and execution strategies for dense versus sparse vertex frontiers, dependent on the fraction of active graph vertices.

TL;DR: A high-performance GPU code generator for arbitrary tensor contractions that exploits domain-specific properties about data reuse in tensorcontractions to devise an effective code generation schema and determine parameters for mapping of computation to threads and staging of data through the GPU memory hierarchy.

TL;DR: FuseT as discussed by the authors is a source-to-source code transformation framework to automatically generate fused composite recursive operators from an input program containing a sequence of primitive recursive operators, which can offer significant performance improvement.

TL;DR: This paper proposes a novel object-based home network middleware that provides various types of abstract objects for flexible representation of heterogeneous home devices, which are classified based on their characteristics and can provide efficient interoperability among home devices and smart grid devices for future energy efficient home.