high performance computing on graphics processing units: hgpu.org

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A Performance Study for Iterative Stencil Loops on GPUs with Ghost Zone Optimizations

Jiayuan Meng, Kevin Skadron

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance

January 26, 2011 by hgpu

Correlating Radio Astronomy Signals with Many-Core Hardware

Rob van Nieuwpoort, John Romein

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Tags: Astrophysics, ATI, ATI CAL, ATI Radeon HD 4870, ATI Stream, Brook, Cell processor, CUDA, nVidia, OpenCL, Performance, Physics, Signal processing, Tesla C1060

January 26, 2011 by hgpu

Adaptable particle-in-cell algorithms for graphical processing units

Viktor K. Decyk, Tajendra V. Singh

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Tags: CUDA, nVidia, Particle-in-cell methods, Physics, Presentation, Tesla C1060

January 26, 2011 by hgpu

Hierarchical Agglomerative Clustering Using Graphics Processor with Compute Unified Device Architecture

S. A. Arul Shalom, Manoranjan Dash, Minh Tue, Nithin Wilson

Tags: Computer science, CUDA, GPU cluster, nVidia

January 26, 2011 by hgpu

Supercomputing with toys: harnessing the power of NVIDIA 8800GTX and playstation 3 for bioinformatics problem

Justin Wilson, Manhong Dai, Elvis Jakupovic, Stanley Watson, Fan Meng

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Tags: Bioinformatics, Cell processor, CUDA, nVidia, nVidia GeForce 8800 GTX, Performance, Playstation, RapidMind

January 26, 2011 by hgpu

Efficient Bayesian inference in stochastic chemical kinetic models using graphical processing units

Jarad Niemi, Matthew Wheeler

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Tags: Bayesian, Biology, Chemistry, CUDA, nVidia, Stochastic simulation, Tesla T10

January 26, 2011 by hgpu

CUDASA: Compute Unified Device and Systems Architecture

M. Strengert, C. Muller, C. Dachsbacher, and T. Ertl

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Tags: Computer science, CUDA, GPU cluster, High-level Languages, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTX, nVidia Quadro FX 5600

January 24, 2011 by hgpu

Sparse regularization in MRI iterative reconstruction using GPUs

Yue Zhuo, Bradley Sutton, Xiao-Long Wu, Justin Haldar, Wen-mei Hwu, Zhipei Liang

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Tags: CUDA, Image processing, Image reconstruction, MRI, nVidia, nVidia GeForce GTX 280, Sparse matrix

January 24, 2011 by hgpu

Exploiting More Parallelism from Applications Having Generalized Reductions on GPU Architectures

Xiao-Long Wu, Nady Obeid, Wen-Mei Hwu

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Tags: Algorithms, Computer science, CUDA, nVidia, Optimization

January 24, 2011 by hgpu

Multi-GPU Implementation for Iterative MR Image Reconstruction with Field Correction

Y. Zhuo, X-L. Wu, J. P. Haldar, W-M. W. Hwu, Z-P. Liang, and B. P. Sutton

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Tags: CUDA, Image processing, Image reconstruction, MRI, nVidia, Tesla S1070

January 24, 2011 by hgpu

Accelerating iterative field-compensated MR image reconstruction on GPUs

Yue Zhuo, Xiao-Long Wu, Justin P. Haldar, Wen-mei Hwu, Zhi-pei Liang, Bradley P. Sutton

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Tags: Conjugate gradient solver, CUDA, Image processing, Image reconstruction, Medicine, MRI, nVidia, nVidia GeForce GTX 280

January 24, 2011 by hgpu

Data Layout Transformation for Structured-Grid Codes on GPU

I-Jui Sung, Wen-Mei Hwu

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280

January 24, 2011 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

A Performance Study for Iterative Stencil Loops on GPUs with Ghost Zone Optimizations

Correlating Radio Astronomy Signals with Many-Core Hardware

Adaptable particle-in-cell algorithms for graphical processing units

Hierarchical Agglomerative Clustering Using Graphics Processor with Compute Unified Device Architecture

Supercomputing with toys: harnessing the power of NVIDIA 8800GTX and playstation 3 for bioinformatics problem

Efficient Bayesian inference in stochastic chemical kinetic models using graphical processing units

CUDASA: Compute Unified Device and Systems Architecture

Sparse regularization in MRI iterative reconstruction using GPUs

Exploiting More Parallelism from Applications Having Generalized Reductions on GPU Architectures

Multi-GPU Implementation for Iterative MR Image Reconstruction with Field Correction

Accelerating iterative field-compensated MR image reconstruction on GPUs

Data Layout Transformation for Structured-Grid Codes on GPU

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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