high performance computing on graphics processing units: hgpu.org

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Implementing Domain-Specific Languages for Heterogeneous Parallel Computing

HyoukJoong Lee, Kevin J. Brown, Arvind K. Sujeeth, Hassan Chafi, Kunle Olukotun, Tiark Rompf, Martin Odersky

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Optimization, Tesla C2050

October 28, 2011 by hgpu

Efficient Probabilistic Latent Semantic Indexing using Graphics Processing Unit

Eli Koffi Kouassi, Toshiyuki Amagasaa, Hiroyuki Kitagawa

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Tags: Algorithms, Computer science, CUDA, Data mining, nVidia, Semantic indexing, Tesla C2050

October 26, 2011 by hgpu

Evaluation of Speedup of Monte Carlo Calculations of Two Simple Reactor Physics Problems Coded for the GPU/CUDA Environment

Aiping Ding, Tianyu Liu, Chao Liang, Wei Ji, Mark S. Shephard, X George Xu, Forrest B. Brown

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Tags: CUDA, Monte Carlo simulation, nVidia, Physics, Tesla C2050

October 26, 2011 by hgpu

Development of a Flow Solver with Complex Kinetics on the Graphic Processing Units

Hai P. Le, Jean-Luc Cambier

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Tags: Chemistry, Computational Physics, CUDA, Finite volume method, Fluid dynamics, nVidia, Tesla C2050, Tesla C2070

October 25, 2011 by hgpu

APTCC: Auto Parallelizing Translator From C To CUDA

Takehiko Nawataa, Reiji Suda

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, nVidia GeForce GTX 580, Tesla C2050

October 24, 2011 by hgpu

Using OpenCL for Implementing Simple Parallel Graph Algorithms

Michael J. Dinneen, Masoud Khosravani, Andrew Probert

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, OpenCL, Tesla C2050

October 24, 2011 by hgpu

Computer Finite-Difference Time-Domain Simulation of Electromagnetic Wave Propagation using GPUs

Anna Bonet Manchado

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Tags: Algorithms, CUDA, Data parallelism, Electrodynamics, FDTD, Finite-difference time-domain, Maxwell's equations, nVidia, nVidia GeForce GTX 280, Tesla C2050, Thesis, Visualization

October 23, 2011 by hgpu

Solving Linear Recurrences on Hybrid GPU Accelerated Manycore Systems

Przemyslaw Stpiczynski

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Tags: Algorithms, BLAS, Computer science, CUDA, Hybrid computing, Linear Algebra, nVidia, Tesla C2050

October 21, 2011 by hgpu

OpenMP for Accelerators

James C. Beyer, Eric J. Stotzer, Alistair Hart, Bronis R. de Supinski

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Tags: Computer science, CUDA, DSP, Fortran, nVidia, OpenMP, Tesla C2050

October 21, 2011 by hgpu

An Efficient Stream Buffer Mechanism for Dataflow Execution on Heterogeneous Platforms with GPUs

Ana Balevic, Bart Kienhuis

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Tags: Computer science, CUDA, Data parallelism, Heterogeneous systems, nVidia, Programming techniques, Tesla C2050

October 19, 2011 by hgpu

Designing the Language Liszt for Building Portable Mesh-based PDE Solvers

Zachary DeVito, Pat Hanrahan

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Source codes

Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, Optimization, Package, Partial differential equations, PDEs, Performance, Programming Languages, Tesla C2050

October 19, 2011 by hgpu

Pricing composable contracts on the GP-GPU

Joakim Ahnfelt-Ronne, Michael Flaeno Werk

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Tags: Asia options, Finance, Monte Carlo simulation, nVidia, nVidia GeForce GTX 460, OpenCL, Probability, Tesla C2050, Thesis

October 19, 2011 by hgpu

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Implementing Domain-Specific Languages for Heterogeneous Parallel Computing

Efficient Probabilistic Latent Semantic Indexing using Graphics Processing Unit

Evaluation of Speedup of Monte Carlo Calculations of Two Simple Reactor Physics Problems Coded for the GPU/CUDA Environment

Development of a Flow Solver with Complex Kinetics on the Graphic Processing Units

APTCC: Auto Parallelizing Translator From C To CUDA

Using OpenCL for Implementing Simple Parallel Graph Algorithms

Computer Finite-Difference Time-Domain Simulation of Electromagnetic Wave Propagation using GPUs

Solving Linear Recurrences on Hybrid GPU Accelerated Manycore Systems

OpenMP for Accelerators

An Efficient Stream Buffer Mechanism for Dataflow Execution on Heterogeneous Platforms with GPUs

Designing the Language Liszt for Building Portable Mesh-based PDE Solvers

Pricing composable contracts on the GP-GPU

Recent source codes

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

ROCm's implementation of Gromacs

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

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