high performance computing on graphics processing units: hgpu.org

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Opportunities for Parallelism in Matrix Multiplication

Tyler M. Smith, Robert A. van de Geijn, Mikhail Smelyanskiy, Jeff R. Hammond and Field G. Van Zee

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Tags: Computer science, Intel Xeon Phi, Matrix multiplication

January 5, 2014 by hgpu

OpenCL Programming by Example

Ravishekhar Banger, Koushik Bhattacharyya

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Tags: Book, Computer science, Image processing, Matrix multiplication, Memory model, Nearest neighbour, OpenCL, OpenGL, Pattern recognition, Tutorial

December 27, 2013 by hgpu

Optimization of the Oktay-Kronfeld Action Conjugate Gradient Inverter

Yong-Chull Jang, Jon A. Bailey, Weonjong Lee, Carleton DeTar, Mehmet B. Oktay, Andreas S. Kronfeld

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Tags: CUDA, High Energy Physics - Lattice, Matrix multiplication, nVidia, nVidia GeForce GTX 480, Physics

November 22, 2013 by hgpu

Anatomy of High-Performance Many-Threaded Matrix Multiplication

Tyler M. Smith, Robert van de Geijn, Mikhail Smelyanskiy, Jeff R. Hammond, Field Van Zee

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Tags: Computer science, Intel Phi, Intel Xeon Phi, Matrix multiplication, Performance

November 13, 2013 by hgpu

Energy Efficiency Studies of Mont Blanc Applications

Mads Holden

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Tags: ARM, Computer science, Energy-efficient computing, Matrix multiplication, OpenCL, Package, Thesis

October 21, 2013 by hgpu

Optimal Configuration of GPU Cache Memory to Maximize the Performance

Leonid Djinevski, Sime Arsenovski, Sasko Ristov, Marjan Gusev

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, Memory, nVidia, nVidia GeForce GTX 680, Performance

September 16, 2013 by hgpu

Analysis-Driven Design of Parallel Floating-Point Matrix Multiplication for Implementation in Reconfigurable Logic

Ahmad Khayyat

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Tags: Computer science, FPGA, Matrix multiplication, Thesis

August 24, 2013 by hgpu

Performance Drawbacks for Matrix Multiplication using Set Associative Cache in GPU devices

Leonid Djinevski, Sime Arsenovski, Sasko Ristov, Marjan Gusev

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 680, Performance

August 19, 2013 by hgpu

GPU Programming in Functional Languages: A Comparison of Haskell GPU Embedded Domain Specific Languages

C. Ouwehand, P. Hijma, W.J. Fokkink

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Tags: Computer science, CUDA, Haskell, Matrix multiplication, nVidia, nVidia GeForce GTX 480

August 6, 2013 by hgpu

OpenCL API Extensions to achieve Multi-level Parallelism for Efficient Implementation of Strassen’s Matrix Multiplication on GPUs

Sivaramakrishnan Kasiviswanathan

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Tags: Algorithms, Computer science, Data parallelism, Matrix multiplication, nVidia, OpenCL, Tesla K20

July 19, 2013 by hgpu

On Benchmarking the Matrix Multiplication Algorithm using OpenMP, MPI and CUDA Programming Languages

Muhammed Al-Mulhem, Abdulah AlDhamin, Raed Al-Shaikh

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Tags: Algorithms, Benchmarking, Bioinformatics, Computer science, CUDA, Linear Algebra, Matrix multiplication, MPI, nVidia, nVidia GeForce GTX 460

July 19, 2013 by hgpu

CUDA-C implementation of the ADER-DG method for linear hyperbolic PDEs

C. E. Castro, J. Behrens, C. Pelties

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Tags: CUDA, Differential equations, Earth and Space Sciences, Geoscience, Hyperbolic PDEs, Matrix multiplication, nVidia, Partial differential equations, PDEs, Tesla C2070

July 15, 2013 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

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high performance computing on graphics processing units: hgpu.org

Opportunities for Parallelism in Matrix Multiplication

OpenCL Programming by Example

Optimization of the Oktay-Kronfeld Action Conjugate Gradient Inverter

Anatomy of High-Performance Many-Threaded Matrix Multiplication

Energy Efficiency Studies of Mont Blanc Applications

Optimal Configuration of GPU Cache Memory to Maximize the Performance

Analysis-Driven Design of Parallel Floating-Point Matrix Multiplication for Implementation in Reconfigurable Logic

Performance Drawbacks for Matrix Multiplication using Set Associative Cache in GPU devices

GPU Programming in Functional Languages: A Comparison of Haskell GPU Embedded Domain Specific Languages

OpenCL API Extensions to achieve Multi-level Parallelism for Efficient Implementation of Strassen’s Matrix Multiplication on GPUs

On Benchmarking the Matrix Multiplication Algorithm using OpenMP, MPI and CUDA Programming Languages

CUDA-C implementation of the ADER-DG method for linear hyperbolic PDEs

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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