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Clustering on GPU – A Brief Survey

V.Saveetha, S.Sophia, V.Anusha Sowbarnika

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, Review

September 27, 2013 by hgpu

CUD@ASP: Experimenting with GPUs in ASP solving

Flavio Vella, Alessandro Dal Palu, Agostino Dovier, Andrea Formisano, Enrico Pontelli

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 520, nVidia GeForce GT 640, nVidia GeForce GTX 580

September 27, 2013 by hgpu

OpenCL Parallel Programming Development Cookbook

Raymond Tay

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Tags: Book, Computer science, Heterogeneous systems, OpenCL, Tutorial

September 27, 2013 by hgpu

GPU-TLS: An Efficient Runtime for Speculative Loop Parallelization on GPUs

Chenggang Zhang, Guodong Han, Cho-Li Wang

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Performance, Tesla M2050

September 27, 2013 by hgpu

Java with Auto-Parallelization on Graphics Coprocessing Architecture

Guodong Han, Chenggang Zhang, King Tin Lam, Cho-Li Wang

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Java, nVidia, Performance, Tesla M2050

September 27, 2013 by hgpu

Trellis: Portability Across Architectures with a High-level Framework

Lukasz G. Szafaryn, Todd Gamblin, Bronis R. de Supinski, Kevin Skadron

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, Tesla C2050

September 27, 2013 by hgpu

OpenCL Task Partitioning in the Presence of GPU Contention

Dominik Grewe, Zheng Wang, and Michael F.P. O'Boyle

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Tags: Computer science, Heterogeneous systems, Intel, Intel HD Graphics 4000, Machine learning, OpenCL, Task scheduling

September 25, 2013 by hgpu

Large Graphs on multi-GPUs

Enrico Mastrostefano

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Tesla X2090, Thesis

September 25, 2013 by hgpu

Automatic Software Synthesis from High-Level ForSyDe Models Targeting Massively Parallel Processors

George Ungureanu

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Heterogeneous systems, Image processing, nVidia, Thesis

September 25, 2013 by hgpu

Fast k-NNG construction with GPU-based quick multi-select

Ivan Komarov, Ali Dashti, Roshan D'Souza

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Tags: Algorithms, Bioinformatics, Clustering, Computer science, CUDA, Machine learning, Nearest neighbour, nVidia, Tesla C2050

September 25, 2013 by hgpu

Evaluation of autoparallelization toolkits for commodity graphics hardware

David Williams, Valeriu Codreanu, Po Yang, Baoquan Liu, Feng Dong, Burhan Yasar, Babak Mahdian, Alessandro Chiarini, Xia Zhao, Jos B.T.M. Roerdink

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, OpenACC, Performance

September 24, 2013 by hgpu

Pipeline strategies to accelerate range query processing on a multi-GPU environment

Ricardo J. Barrientos

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Tags: Algorithms, Computer science, CUDA, Image recognition, nVidia, Tesla M2070

September 24, 2013 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Clustering on GPU – A Brief Survey

CUD@ASP: Experimenting with GPUs in ASP solving

OpenCL Parallel Programming Development Cookbook

GPU-TLS: An Efficient Runtime for Speculative Loop Parallelization on GPUs

Java with Auto-Parallelization on Graphics Coprocessing Architecture

Trellis: Portability Across Architectures with a High-level Framework

OpenCL Task Partitioning in the Presence of GPU Contention

Large Graphs on multi-GPUs

Automatic Software Synthesis from High-Level ForSyDe Models Targeting Massively Parallel Processors

Fast k-NNG construction with GPU-based quick multi-select

Evaluation of autoparallelization toolkits for commodity graphics hardware

Pipeline strategies to accelerate range query processing on a multi-GPU environment

Recent source codes

CrossTL: Universal Programming Language & Translator

TBD-GPU

DG-SWEM - The Discontinuous Galerkin Shallow Water Equation Model

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

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