high performance computing on graphics processing units: hgpu.org

Applications

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Study of low density nuclear matter with quantum molecular dynamics: the role of the symmetry energy

Rana Nandi, Stefan Schramm

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Tags: AMD FirePro S10000, ATI, Molecular dynamics, Nuclear Theory, OpenCL, Physics

January 12, 2016 by hgpu

Real-Time Dedispersion for Fast Radio Transient Surveys, using Auto Tuning on Many-Core Accelerators

Alessio Sclocco, Joeri van Leeuwen, Henri E. Bal, Rob V. van Nieuwpoort

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Tags: Astrophysics, ATI, ATI Radeon HD 7970, Instrumentation and Methods for Astrophysics, Intel Xeon Phi, nVidia, nVidia GeForce GTX 680, nVidia GeForce Titan, OpenCL, OpenMP, Package, Tesla K20

January 12, 2016 by hgpu

Computationally Efficient Tsunami Modelling on Graphics Processing Units (GPU)

Reza Amouzgar, Qiuhua Liang, Peter J. Clarke, Tomohiro Yasuda, Hajime Mase

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Tags: CUDA, Earth and Space Sciences, Fluid dynamics, nVidia, Tesla M2075

January 7, 2016 by hgpu

GPU-Based Fuzzy C-Means Clustering Algorithm for Image Segmentation

Mishal Almazrooie, Mogana Vadiveloo, Rosni Abdullah

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, Tesla C2050

January 7, 2016 by hgpu

Verifying CUDA Programs using SMT-Based Context-Bounded Model Checking

Phillipe A. Pereira, Higo F. Albuquerque, Hendrio M. Marques, Isabela S. Silva, Celso B. Carvalho, Lucas C. Cordeiro

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Tags: Computer science, CUDA, nVidia, Package

January 7, 2016 by hgpu

DeepLearningKit – an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

Amund Tvei, Torbjorn Morland, Thomas Brox Rost

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Tags: Computer science, Deep learning, Machine learning, Neural networks, OpenCL, Package, Vulkan

January 7, 2016 by hgpu

Faster GPU Based Genetic Programming Using A Two Dimensional Stack

Darren M. Chitty

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Tags: Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 670

January 7, 2016 by hgpu

Batched Linear Algebra Problems on GPU Accelerators

Tingxing Dong

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Tags: Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, Tesla K40, Thesis

January 4, 2016 by hgpu

Programming Models and Scheduling Techniques for Heterogeneous Architectures

Judit Planas Carbonell

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OmpSs, Task scheduling, Tesla K20, Tesla K40, Tesla M2090, Thesis

January 4, 2016 by hgpu

Accelerating Binary Genetic Algorithm Driven Missile Design Optimization Routine with a CUDA Coded Six Degrees-Of-Freedom Simulator

Daniel Benton

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 570

January 4, 2016 by hgpu

CUDA Parallel Algorithms for Forward and Inverse Structural Gravity Problems

Alexander Tsidaev

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Tags: CUDA, Earth and Space Sciences, Geoscience, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 780 Ti, Tesla M2050

January 4, 2016 by hgpu

Automatic Performance Tuning of Stencil Computations on Graphics Processing Units

Joseph D. Garvey

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Tags: Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Performance, Stencil computation, Thesis

January 4, 2016 by hgpu

Allo: Accelerator Design Language

Dato: A Task-Based Programming Model for Dataflow Accelerators

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

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

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high performance computing on graphics processing units: hgpu.org

Applications

Study of low density nuclear matter with quantum molecular dynamics: the role of the symmetry energy

Real-Time Dedispersion for Fast Radio Transient Surveys, using Auto Tuning on Many-Core Accelerators

Computationally Efficient Tsunami Modelling on Graphics Processing Units (GPU)

GPU-Based Fuzzy C-Means Clustering Algorithm for Image Segmentation

Verifying CUDA Programs using SMT-Based Context-Bounded Model Checking

DeepLearningKit – an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

Faster GPU Based Genetic Programming Using A Two Dimensional Stack

Batched Linear Algebra Problems on GPU Accelerators

Programming Models and Scheduling Techniques for Heterogeneous Architectures

Accelerating Binary Genetic Algorithm Driven Missile Design Optimization Routine with a CUDA Coded Six Degrees-Of-Freedom Simulator

CUDA Parallel Algorithms for Forward and Inverse Structural Gravity Problems

Automatic Performance Tuning of Stencil Computations on Graphics Processing Units

Recent source codes

Allo: Accelerator Design Language

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

HPC Benchmark Survey

HDM: Home made Diffusion Models

General Matrix Multiplication (GEMM)

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

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