high performance computing on graphics processing units: hgpu.org

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Evaluating one-sided programming models for GPU cluster computations

Jeff R. Hammond and A. Eugene DePrince III

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Tags: Chemistry, Computer science, CUDA, GPU cluster, Matrix multiplication, nVidia, Tesla T10

February 17, 2011 by hgpu

GPU Accelerated Particle System for Triangulated Surface Meshes

Brad Peterson, Manasi Datar, Mary Hall, Ross Whitaker

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Particle simulation, Triangular meshes

February 17, 2011 by hgpu

Medium-Grained Functions Mapping using Modern GPUs

Jiri Filipovic, Jan Fousek

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

February 17, 2011 by hgpu

Dynamically scheduled Cholesky factorization on multicore architectures with GPU accelerators

Emmanuel Agullo, Cedric Augonnet, Jack Dongarra, Hatem Ltaief, Raymond Namyst, Jean Roman, Samuel Thibault, Stanimire Tomov

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia Quadro FX 5800, Task scheduling

February 17, 2011 by hgpu

A Strategy for Automatically Generating High Performance CUDA Code for a GPU Accelerator from a Specialized Fortran Code Expression

Pei-Hung Lin, Jagan Jayaraj, and Paul R. Woodward

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Tags: Cell processor, Code generation, Compilers, Computer science, CUDA, Fortran, High-level Languages, Memory model, nVidia, Tesla C1060

February 17, 2011 by hgpu

Accelerating Algorithms on GPUs in SCIRun: the Conjugate Gradient Case Study

Devon Yablonski, Miriam Leeser, Dana Brooks

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Tags: Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce GTX 280, Package, Sparse matrix

February 17, 2011 by hgpu

Takagi Factorization on GPU using CUDA

Gagandeep S. Sachdev, Vishay Vanjani and Mary W. Hall

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Tags: Computer science, CUDA, Factorization, Matrix decomposition, nVidia, nVidia GeForce GTX 260

February 17, 2011 by hgpu

Automatically Tuned Dense Linear Algebra for Multicore+GPU

Xing Fu, Xue Li, Gregory D. Peterson

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280, Package

February 17, 2011 by hgpu

GpuC: Data parallel language extension to CUDA

Zeki Bozkus

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, OpenMP, Optimization

February 16, 2011 by hgpu

Static Memory Access Pattern Analysis on a Massively Parallel GPU

Byunghyun Jang, Dana Schaa, Perhaad Mistry, and David Kaeli

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Tags: Computer science, Matrix multiplication, Memory model, nVidia, nVidia GeForce 8800 Ultra, nVidia GeForce GTX 285, OpenCL, Optimization

February 16, 2011 by hgpu

An experimental study on performance portability of OpenCL kernels

Sean Rul, Hans Vandierendonck, Joris D'Haene and Koen De Bosschere

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Tags: ATI, ATI FirePro V8700, Cell processor, Computer science, nVidia, OpenCL, Performance, Playstation, Tesla C1060

February 16, 2011 by hgpu

Multi-agent traffic simulation with CUDA

D. Strippgen, K. Nagel

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Tags: Computer science, CUDA, Games, nVidia, nVidia GeForce 8600 GT, nVidia GeForce GTX 280

February 16, 2011 by hgpu

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

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

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

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

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

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

Geak: Introducing Triton Kernel AI Agent & Evaluation Benchmarks

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

OpenDwarfs 2025: Modernizing the OpenDwarfs Benchmark Suite for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Applications

Evaluating one-sided programming models for GPU cluster computations

GPU Accelerated Particle System for Triangulated Surface Meshes

Medium-Grained Functions Mapping using Modern GPUs

Dynamically scheduled Cholesky factorization on multicore architectures with GPU accelerators

A Strategy for Automatically Generating High Performance CUDA Code for a GPU Accelerator from a Specialized Fortran Code Expression

Accelerating Algorithms on GPUs in SCIRun: the Conjugate Gradient Case Study

Takagi Factorization on GPU using CUDA

Automatically Tuned Dense Linear Algebra for Multicore+GPU

GpuC: Data parallel language extension to CUDA

Static Memory Access Pattern Analysis on a Massively Parallel GPU

An experimental study on performance portability of OpenCL kernels

Multi-agent traffic simulation with CUDA

Recent source codes

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)

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

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

SIGMo: Scalable Isomorphism Graph Matching on GPUs

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

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

Most viewed papers (last 30 days)