high performance computing on graphics processing units: hgpu.org

hgpu.org » Data parallelism

Transparent CPU-GPU Collaboration for Data-Parallel Kernels on Heterogeneous Systems

J. Lee, M. Samadi, Y. Park, S. Mahlke

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Tags: Data parallelism, Heterogeneous systems, nVidia, nVidia GeForce GTX 560, nVidia Quadro 600, OpenCL

September 5, 2013 by hgpu

NOVA: A Functional Language for Data Parallelism

Alexander Collins, Dominik Grewe, Vinod Grover, Sean Lee, Adriana Susnea

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Tags: Compilers, Computer science, CUDA, Data parallelism, High-level Languages, nVidia, nVidia GeForce GTX 480

August 1, 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

Scalable Techniques for Scheduling and Mapping DSP Applications onto Embedded Multiprocessor Platforms

George Zaki

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Tags: Algorithms, Code generation, Computational Engineering, CUDA, Data parallelism, DSP, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, Signal processing, Thesis

July 12, 2013 by hgpu

Exploiting Data Parallelism in the yConvex Hypergraph Algorithm for Image Representation using GPGPUs

Saurabh Jha, Tejaswi Agarwal, B. Rajesh Kanna

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Image processing, nVidia, nVidia GeForce GT 310 M

July 10, 2013 by hgpu

Exploiting multi-level parallelism in streaming applications for heterogeneous platforms with GPUs

Ana Balevic

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Tags: Code generation, Compilers, Computer science, CUDA, Data parallelism, Heterogeneous systems, nVidia, Tesla C2050, Thesis

July 1, 2013 by hgpu

String Algorithm on GPGPU

Ong Wen Mei

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Tags: Algorithms, Bloom filter, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GT 530, nVidia Quadro FX 4000, String matching, Thesis

June 25, 2013 by hgpu

Real-space density functional theory on graphical processing units: computational approach and comparison to Gaussian basis set methods

Xavier Andrade, Alan Aspuru-Guzik

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Source codes

Tags: ATI, ATI Radeon HD 7970, Computational Physics, Condensed matter, Data parallelism, nVidia, OpenCL, Package, Physics, Tesla K20

June 14, 2013 by hgpu

Job Parallelism using Graphical Processing Unit individual Multi-Processors and Highly Localised Memory

D. P. Playne, K. A. Hawick

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, nVidia GeForce GTX 590, nVidia GeForce GTX 680, Tesla M2075

May 29, 2013 by hgpu

An implementation of level set based topology optimization using GPU

D. Herrero, J. Martinez, P. Marti

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Tags: Computational Complexity, CUDA, Data parallelism, FEM, Finite element method, Image processing, nVidia, Tesla C2070

May 19, 2013 by hgpu

Parallel Zonal Summations of Large-Scale Species Occurrence Data on Hybrid CPU-GPU Systems

Jianting Zhang, Simin You

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Tags: CUDA, Data parallelism, Earth and Space Sciences, Filtering, Geoscience, nVidia, nVidia Quadro FX 6000, Performance

May 19, 2013 by hgpu

GPU Acceleration of the Variational Monte Carlo Method for Many Body Physics

Rajagopalan Kaushik Ragavan

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Tags: CUDA, Data parallelism, Heterogeneous systems, Monte Carlo simulation, MPI, nVidia, Physics, Tesla M2070, Thesis

May 1, 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Transparent CPU-GPU Collaboration for Data-Parallel Kernels on Heterogeneous Systems

NOVA: A Functional Language for Data Parallelism

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

Scalable Techniques for Scheduling and Mapping DSP Applications onto Embedded Multiprocessor Platforms

Exploiting Data Parallelism in the yConvex Hypergraph Algorithm for Image Representation using GPGPUs

Exploiting multi-level parallelism in streaming applications for heterogeneous platforms with GPUs

String Algorithm on GPGPU

Real-space density functional theory on graphical processing units: computational approach and comparison to Gaussian basis set methods

Job Parallelism using Graphical Processing Unit individual Multi-Processors and Highly Localised Memory

An implementation of level set based topology optimization using GPU

Parallel Zonal Summations of Large-Scale Species Occurrence Data on Hybrid CPU-GPU Systems

GPU Acceleration of the Variational Monte Carlo Method for Many Body Physics

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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