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CURFIL: Random Forests for Image Labeling on GPU

Hannes Schulz, Benedikt Waldvogel, Rasha Sheikh, Sven Behnke

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 675 M, nVidia GeForce GTX Titan, Package, Tesla K20

January 16, 2015 by hgpu

Dense Real-Time Mapping of Object-Class Semantics from RGB-D Video

Jorg Stuckler, Benedikt Waldvogel, Hannes Schulz, Sven Behnke

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Tags: Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 675 M, nVidia GeForce GTX Titan

December 9, 2013 by hgpu

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A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

INT v.s. FP: A framework to compare low-bit integer and float-point formats

INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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