high performance computing on graphics processing units: hgpu.org

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Parallel Implementation of Dynamic Programming Algorithm Using Graphics Processing Unit

M. Khurram Rehman, M. Umer Sarwar, M. Ramzan Talib, M. Salman Mansoor, M. Bilal Sarwar

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Tags: Algorithms, Bioinformatics, Biology, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GT 525 M, nVidia GeForce GT 610 M, Sequence alignment

April 6, 2013 by hgpu

Scaling up scientific computations by using map-reduce-like control flow on NUMA architectures

Vlad Slavici

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Tags: Algorithms, Chemistry, Computational chemistry, CUDA, MapReduce, Matrix multiplication, nVidia, Tesla M2090, Thesis

April 3, 2013 by hgpu

Accelerated Dictionary Learning with GPU/Multicore CPU and Its Application to Music Classification

Boyang Gao, Emmanuel Dellandrea, Liming Chen

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Tags: Clustering, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 285, Signal processing

March 26, 2013 by hgpu

Parallel Interpretation of L-system Based on CUDA

Sulan Zhang, Qingsheng Zhu, Ji Liu, Lingqiu Zeng

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Tags: Computer science, CUDA, Image generation, Matrix multiplication, nVidia, nVidia GeForce GTS 250

March 24, 2013 by hgpu

Hybrid parallel programming – evaluation of OpenACC

Leonardo Piletti Chatain

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Tags: Benchmarking, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 480, OpenACC, Thesis

February 15, 2013 by hgpu

Performance Evaluation of Sparse Matrix Multiplication Kernels on Intel Xeon Phi

Erik Saule, Kamer Kaya, Umit V. Catalyurek

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Tags: Computer science, CUDA, Hardware Architecture, Intel, Intel Phi, Matrix multiplication, nVidia, Performance, Sparse matrix, Tesla C2050, Tesla K20

February 6, 2013 by hgpu

GPU Enhanced Stream-Based Matrix Multiplication

L. M. Itu, C. Suciu, F. Moldoveanu, A. Postelnicu

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 260

January 31, 2013 by hgpu

Sparse Matrix Multiplication using CUDA and Mex Interface

Abhishek Deshpande

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 480, Sparse matrix

December 20, 2012 by hgpu

Matrix-Matrix Multiplications on GPUs for Accelerating a Parallel Fluid Dynamics Code

Kenneth Webster

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Tags: CUBLAS, CUDA, Fluid dynamics, Matrix multiplication, nVidia, Tesla M2070, Thesis

December 12, 2012 by hgpu

A Data-Parallel Algorithmic Modelica Extension for Efficient Execution on Multi-Core Platforms

Mahder Gebremedhin, Afshin Hemmati Moghadam, Peter Fritzson, Kristian Stavaker

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Tags: Algorithms, Benchmarking, Compilers, Computer science, Matrix multiplication, nVidia, OpenCL, Tesla M2050

November 27, 2012 by hgpu

Parallel Programming using OpenCL on Modern Architectures

Allan Svejstrup Nielsen, Allan Peter Engsig-Karup, Bernd Dammann

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Tags: ATI, ATI Radeon HD 6990, Computer science, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 590, OpenCL, Overview, Tutorial

October 15, 2012 by hgpu

An Efficient GPU Implementation of Modified Discrete Cosine Transform Using CUDA

Massimo Panella, Luigi Basset

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Tags: Algorithms, Compression, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 295, Signal processing

October 6, 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

Parallel Implementation of Dynamic Programming Algorithm Using Graphics Processing Unit

Scaling up scientific computations by using map-reduce-like control flow on NUMA architectures

Accelerated Dictionary Learning with GPU/Multicore CPU and Its Application to Music Classification

Parallel Interpretation of L-system Based on CUDA

Hybrid parallel programming – evaluation of OpenACC

Performance Evaluation of Sparse Matrix Multiplication Kernels on Intel Xeon Phi

GPU Enhanced Stream-Based Matrix Multiplication

Sparse Matrix Multiplication using CUDA and Mex Interface

Matrix-Matrix Multiplications on GPUs for Accelerating a Parallel Fluid Dynamics Code

A Data-Parallel Algorithmic Modelica Extension for Efficient Execution on Multi-Core Platforms

Parallel Programming using OpenCL on Modern Architectures

An Efficient GPU Implementation of Modified Discrete Cosine Transform Using CUDA

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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