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A tutorial on the implementations of linear image filters in CPU and GPU

Alvaro Pardo

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 430, Signal processing, Tutorial

December 7, 2017 by hgpu

MILC Code Performance on High End CPU and GPU Supercomputer Clusters

Ruizi Li, Carleton DeTar, Steven Gottlieb, Doug Toussaint

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Tags: Computational Physics, CUDA, High Energy Physics - Lattice, Intel Xeon Phi, nVidia, Performance, Physics, QCD, Tesla K20, Tesla P100

December 7, 2017 by hgpu

Study of Bandwidth Partitioning for Co-executing GPU Kernels

Erik Melander

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Tags: Computer science, CUDA, nVidia, Performance, Tesla K20, Thesis

December 7, 2017 by hgpu

HCudaBLAST: an implementation of BLAST on Hadoop and Cuda

Nilay Khare, Alind Khare and Farhan Khan

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Tags: Algorithms, Bioinformatics, Biology, BLAST, CUDA, Hadoop, nVidia, nVidia Quadro 2000, nVidia Quadro 600, Tesla C2075

December 3, 2017 by hgpu

A Hybrid-parallel Architecture for Applications in Bioinformatics

Jan Christian Kassens

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Tags: Algorithms, Bioinformatics, Biology, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 780 Ti, Thesis

December 3, 2017 by hgpu

Methods for GPU Acceleration of Big Data Applications

Reza Mokhtari

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Tags: big data, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX Titan Black, Thesis

December 3, 2017 by hgpu

Implementing implicit OpenMP data sharing on GPUs

Gheorghe-Teodor Bercea, Carlo Bertolli, Arpith C. Jacob, Alexandre Eichenberger, Alexey Bataev, Georgios Rokos, Hyojin Sung, Tong Chen, Kevin O'Brien

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Tags: Computer science, CUDA, LLVM, Memory model, nVidia, OpenMP, Programming Languages, PTX, Tesla K40, Tesla P100

November 30, 2017 by hgpu

Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs

Shaohuai Shi, Xiaowen Chu

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Tags: Algorithms, Benchmarking, Caffe, Computer science, CUDA, Deep learning, MPI, Neural networks, nVidia, Package, Performance, TensorFlow, Tesla P40, Torch

November 26, 2017 by hgpu

Efficient Target and Application Specific Selection and Ordering of Compiler Passes

Ricardo Jorge Ferreira Nobre

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Tags: Code generation, Compilers, Computer science, CUDA, Design space exploration, nVidia, nVidia GeForce GT 650 M, nVidia GeForce GTX 460, nVidia GeForce GTX 480, OpenCL, PTX, Thesis

November 26, 2017 by hgpu

GPU Pro 7: Advanced Rendering

Wolfgang Engel (editor)

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Source codes

Tags: 3D Graphics and Realism, Book, Computer science, CUDA, DirectCompute, DirectX, nVidia, OpenGL, Package, Rendering, Visualization

November 26, 2017 by hgpu

A survey on graphic processing unit computing for large-scale data mining

Alberto Cano

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Tags: Computer science, CUDA, Data mining, Distributed computing, Machine learning, nVidia

November 21, 2017 by hgpu

GPU Parallelization for Unstructured Sparse Matrix Problems with OpenMP 4.5 and OpenACC

Stefan Rosenberger, Gundolf Haase

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 1060, OpenACC, OpenMP, Sparse matrix

November 21, 2017 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

A tutorial on the implementations of linear image filters in CPU and GPU

MILC Code Performance on High End CPU and GPU Supercomputer Clusters

Study of Bandwidth Partitioning for Co-executing GPU Kernels

HCudaBLAST: an implementation of BLAST on Hadoop and Cuda

A Hybrid-parallel Architecture for Applications in Bioinformatics

Methods for GPU Acceleration of Big Data Applications

Implementing implicit OpenMP data sharing on GPUs

Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs

Efficient Target and Application Specific Selection and Ordering of Compiler Passes

GPU Pro 7: Advanced Rendering

A survey on graphic processing unit computing for large-scale data mining

GPU Parallelization for Unstructured Sparse Matrix Problems with OpenMP 4.5 and OpenACC

Recent source codes

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

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