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Learning Representation for Scene Understanding: Epitomes, CRFs, and CNNs

Liang-Chieh Chen

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 650 M, OpenCV, Tesla K40, Thesis

November 24, 2015 by hgpu

Benchmarking the cost of thread divergence in CUDA

Piotr Bialas, Adam Strzelecki

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GT 610, nVidia GeForce GT 755 M, nVidia GeForce GTX 480, nVidia GeForce GTX 650 M, nVidia GeForce GTX 770, nVidia GeForce GTX 850 M, Performance

April 8, 2015 by hgpu

Modeling Image Patches with a Generic Dictionary of Mini-Epitomes

George Papandreou, Liang-Chieh Chen, Alan L. Yuille

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 650 M, SIFT, Tesla K20

April 17, 2014 by hgpu

Nemo: A parallelized Lagrangian particle-tracking model

Benjamin Jones

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Tags: CUDA, Earth and Space Sciences, Fortran, MPI, nVidia, nVidia GeForce GTX 650 M, Package

January 3, 2014 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

Learning Representation for Scene Understanding: Epitomes, CRFs, and CNNs

Benchmarking the cost of thread divergence in CUDA

Modeling Image Patches with a Generic Dictionary of Mini-Epitomes

Nemo: A parallelized Lagrangian particle-tracking model

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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