high performance computing on graphics processing units: hgpu.org

Siegfried Benkner, Sabri Pllana, Jesper Larsson Traff, Philippas Tsigas, Andrew Richards, Raymond Namyst, Beverly Bachmayer, Christoph Kessler, David Moloney, Peter Sanders

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Optimization, Performance, Tesla C1060

February 8, 2012 by hgpu

Symbolic Testing of OpenCL Code

Peter Collingbourne, Cristian Cadar, Paul H. J. Kelly

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Tags: Benchmarking, Code generation, Computer science, nVidia, OpenCL, Package, PTX, Tesla C1060

February 8, 2012 by hgpu

Seismic imaging based on spectral differentiation matrix and GPU implementation

Jing-Bo Chen, Guo-Feng Liu, Hong Liu

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Tags: Algorithms, CUDA, Earth and Space Sciences, nVidia, Seismology, Tesla C1060, Wave equation

February 7, 2012 by hgpu

Efficient Computation of SOM for Outage Database

Petr Gajdos, Michal Kratky, David Bednar, Radim Baca, Radomir Gono, Jiri Walder

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Tags: Computer science, CUDA, Databases, Neural networks, nVidia, Self-organizing map, Tesla C1060, Visualization

February 5, 2012 by hgpu

Towards a Tunable Multi-Backend Skeleton Programming Framework for Multi-GPU Systems

Johan Enmyren, Usman Dastgeer, Christoph W. Kessler

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Tags: Code generation, Computer science, CUDA, Data parallelism, MapReduce, nVidia, nVidia GeForce GTS 250, OpenCL, Performance, Tesla C1060

January 30, 2012 by hgpu

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

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Model-Driven Tile Size Selection for DOACROSS Loops on GPUs

Multiscale Hemodynamics Using GPU Clusters

Using Graphical Processing Units in Scheduling Problems

LAMMPScuda – a new GPU accelerated Molecular Dynamics Simulations Package and its Application to Ion-Conducting Glasses

FPGA accelerated 3D reconstruction using compressive sensing

Parallel Semi-Implicit Time Integrators

Auto-Generation and Auto-Tuning of 3D Stencil Codes on GPU Clusters

The PEPPHER Approach to Programmability and Performance Portability for Heterogeneous many-core Architectures

Symbolic Testing of OpenCL Code

Seismic imaging based on spectral differentiation matrix and GPU implementation

Efficient Computation of SOM for Outage Database

Towards a Tunable Multi-Backend Skeleton Programming Framework for Multi-GPU Systems

Recent source codes

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

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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