Leveraging Large-Scale Data for Efficient Low-Bit CUTLASS GEMM Optimization via Neural NetworksOA
Leveraging Large-Scale Data for Efficient Low-Bit CUTLASS GEMM Optimization via Neural Networks
Hong Guo;Nianhui Guo;Christoph Meinel;Haojin Yang
Hasso Plattner Institute for Digital Engineering gGmbH,University of Potsdam,Potsdam 14482,GermanyHasso Plattner Institute for Digital Engineering gGmbH,University of Potsdam,Potsdam 14482,GermanyHasso Plattner Institute for Digital Engineering gGmbH,University of Potsdam,Potsdam 14482,GermanyHasso Plattner Institute for Digital Engineering gGmbH,University of Potsdam,Potsdam 14482,Germany
Low-bit GEneral Matrix Multiplication(GEMM)CUTLASS optimizationneural networkauto-tuningTensor Corestile and pipelinelarge-scale dataset
Low-bit GEneral Matrix Multiplication(GEMM)CUTLASS optimizationneural networkauto-tuningTensor Corestile and pipelinelarge-scale dataset
《大数据挖掘与分析（英文版）》 2026 (2)
632-652,21
This work was supported by the Federal Ministry of Research,Technology and Space under the funding code"KI-Servicezentrum Berlin-Brandenburg"16IS22092.Responsibility for the content of this publication remains with the author.
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