high performance computing on graphics processing units: hgpu.org

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PASSATA – Object oriented numerical simulation software for adaptive optics

G. Agapito, A. Puglisi, S. Esposito

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, Numerical simulation, nVidia, Optics

July 28, 2016 by hgpu

Genetic Improvement of GPU Software

William B. Langdon, Brian Yee Hong Lam, Marc Modat, Justyna Petke, Mark Harman

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 580, Tesla C2050, Tesla K20, Tesla T10

July 28, 2016 by hgpu

Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs

Brian Ichter, Edward Schmerling, Ali-akbar Agha-mohammadi, Marco Pavone

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Package, Robotics

July 28, 2016 by hgpu

Strategies for Protecting Intellectual Property when Using CUDA Applications on Graphics Processing Units

X. Bellekens, G. Paul, J. M. Irvine, C. Tachtatzis, R. C. Atkinson

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Tags: CUDA, nVidia

July 26, 2016 by xhgpu

Gerbil: A Fast and Memory-Efficient k-mer Counter with GPU-Support

Marius Erbert, Steffen Rechner, Matthias Muller-Hannemann

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Tags: Bioinformatics, Biology, Computer science, CUDA, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan, Package

July 26, 2016 by hgpu

Lowering IrGL to CUDA

Sreepathi Pai, Keshav Pingali

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Tags: Compilers, Computer science, CUDA, nVidia, Programming Languages

July 20, 2016 by hgpu

THOR: A New and Flexible Global Circulation Model to Explore Planetary Atmospheres

Joao M. Mendonca, Simon L. Grimm, Luc Grosheintz, Kevin Heng

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Tags: Astrophysics, CUDA, Earth and Planetary Astrophysics, Earth and Space Sciences, nVidia, Tesla K20

July 20, 2016 by hgpu

Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

Hokchhay Tann, Soheil Hashemi, R. Iris Bahar, Sherief Reda

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Tags: Algorithms, ARM, ASIC, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia Tegra TX1

July 20, 2016 by hgpu

Accelerating the Conjugate Gradient Algorithm with GPUs in CFD Simulations

Hartwig Anzt, Marc Baboulin, Jack Dongarra, Yvan Fournier, Frank Hulsemann, Amal Khabou, Yushan Wang

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Tags: cfd, Conjugate gradient solver, CUDA, Fluid dynamics, nVidia, OpenCL, Tesla K40

July 18, 2016 by hgpu

IODA: an Input/Output Deep Architecture for image labeling

J. Lerouge, R. Herault, Clement Chatelain, F. Jardin, R. Modzelewski

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Python, Tesla C1060, Theano

July 18, 2016 by hgpu

Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU

Ichitaro Yamazaki, Stanimire Tomov, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Linear Algebra, Matrix decomposition, Mixed precision, Numerical Analysis, nVidia, Performance, Tesla K20

July 16, 2016 by hgpu

An investigation of GPU-based stiff chemical kinetics integration methods

Nicholas J. Curtis, Kyle E. Niemeyer, Chih-Jen Sung

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Tags: Algorithms, Chemistry, Computational Physics, CUDA, nVidia, Physics, Tesla C2075

July 16, 2016 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

See all packages

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high performance computing on graphics processing units: hgpu.org

PASSATA – Object oriented numerical simulation software for adaptive optics

Genetic Improvement of GPU Software

Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs

Strategies for Protecting Intellectual Property when Using CUDA Applications on Graphics Processing Units

Gerbil: A Fast and Memory-Efficient k-mer Counter with GPU-Support

Lowering IrGL to CUDA

THOR: A New and Flexible Global Circulation Model to Explore Planetary Atmospheres

Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

Accelerating the Conjugate Gradient Algorithm with GPUs in CFD Simulations

IODA: an Input/Output Deep Architecture for image labeling

Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU

An investigation of GPU-based stiff chemical kinetics integration methods

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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