high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

Particle-Based Fluid Simulation on the GPU

Kyle Hegeman, Nathan Carr, Gavin Miller

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Tags: Fluid dynamics, nVidia, nVidia GeForce FX 7800

November 8, 2010 by hgpu

An extended GPU radiosity solver

Gunter Wallner

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Tags: Cg, Computer science, Image processing, nVidia, nVidia GeForce 8800 GTS, OpenGL

November 8, 2010 by hgpu

A SIMD-efficient 14 instruction shader program for high-throughput microtriangle rasterization

Jordi Roca, Victor Moya, Carlos Gonzalez, Vicente Escandell, Albert Murciego, Agustin Fernandez, Roger Espasa

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Tags: ATI, Image processing, OpenGL, Rasterization, Rendering

November 8, 2010 by hgpu

Hybrid CUDA, OpenMP, and MPI parallel programming on multicore GPU clusters

Chao-Tung Yang, Chih-Lin Huang, Cheng-Fang Lin

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Tags: Computer science, CUDA, GPU cluster, Hybrid computing, MPI, nVidia, OpenMP, Tesla C1060, Tesla S1070

November 8, 2010 by hgpu

Solving lattice QCD systems of equations using mixed precision solvers on GPUs

M. A. Clark, R. Babich, K. Barros, R. C. Brower, C. Rebbi

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Tags: CUDA, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, nVidia GeForce GTX 280, Package, Physics, QCD

November 8, 2010 by hgpu

Monte Carlo randomization tests for large-scale abundance datasets on the GPU

John L. Van Hemert, Julie A. Dickerson

Tags: Bioinformatics, Biology, CUDA, Medicine, Metabolomics, Monte Carlo simulation, Next-Generation sequencing, nVidia

November 8, 2010 by hgpu

Parallel, distributed and GPU computing technologies in single-particle electron microscopy

M. Schmeisser, B. C. Heisen, M. Luettich, B. Busche, F. Hauer, T. Koske, K. H. Knauber, H. Stark

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Tags: Biology, Cg, Cluster computing, Electron microscopy, GLSL, MPI, nVidia, OpenGL, Physics

November 8, 2010 by hgpu

Accelerating incompressible flow computations with a Pthreads-CUDA implementation on small-footprint multi-GPU platforms

Julien Thibault, Inanc Senocak

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Tags: CUDA, Fluid dynamics, Navier-Stokes equations, NSEs, nVidia, Pthreads

November 8, 2010 by hgpu

A GPU-based matting Laplacian solver for high resolution image matting

Mengcheng Huang, Fang Liu, Enhua Wu

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

November 8, 2010 by hgpu

Parallel Iterative Linear Solvers on GPU: A Financial Engineering Case

Abhijeet Gaikwad, Ioane M. Toke

Tags: Computational finance, CUDA, Finance, nVidia, Sparse linear iterative solvers

November 8, 2010 by hgpu

Parallel medical image reconstruction: from graphics processing units (GPU) to Grids

Maraike Schellmann, Sergei Gorlatch, Dominik Meilander, Thomas Kosters, Klaus Schafers, Frank Wubbeling, Martin Burger

Tags: Cell processor, CUDA, Image processing, Image reconstruction, Medicine, nVidia, PET, Tomography

November 8, 2010 by hgpu

Power Efficient Large Matrices Multiplication by Load Scheduling on Multi-core and GPU Platform with CUDA

DaQi Ren, Reiji Suda

Tags: Computer science, CUDA, Energy-efficient computing, Matrix multiplication, nVidia

November 8, 2010 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

Applications

Particle-Based Fluid Simulation on the GPU

An extended GPU radiosity solver

A SIMD-efficient 14 instruction shader program for high-throughput microtriangle rasterization

Hybrid CUDA, OpenMP, and MPI parallel programming on multicore GPU clusters

Solving lattice QCD systems of equations using mixed precision solvers on GPUs

Monte Carlo randomization tests for large-scale abundance datasets on the GPU

Parallel, distributed and GPU computing technologies in single-particle electron microscopy

Accelerating incompressible flow computations with a Pthreads-CUDA implementation on small-footprint multi-GPU platforms

A GPU-based matting Laplacian solver for high resolution image matting

Parallel Iterative Linear Solvers on GPU: A Financial Engineering Case

Parallel medical image reconstruction: from graphics processing units (GPU) to Grids

Power Efficient Large Matrices Multiplication by Load Scheduling on Multi-core and GPU Platform with CUDA

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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