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SoaAlloc: Accelerating Single-Method Multiple-Objects Applications on GPUs

Matthias Springer

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Programming Languages

September 23, 2018 by hgpu

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Geir Thore Berge, Ole-Christoffer Granmo, Tor Oddbjorn Tveit, Morten Goodwin, Lei Jiao, Bernt Viggo Matheussen

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Tags: CUDA, Deep learning, Keras, Medicine, Neural networks, nVidia, Package, TensorFlow

September 16, 2018 by hgpu

Benchmarking and Optimization of Gradient Boosted Decision Tree Algorithms

Andreea Anghel, Nikolaos Papandreou, Thomas Parnell, Alessandro De Palma, Haralampos Pozidis

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Tags: Algorithms, Bayesian, Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 1080 Ti

September 16, 2018 by hgpu

Developing a New Storage Format and a Warp-Based SpMV Kernel for Configuration Interaction Sparse Matrices on the GPU

Mohammed Mahmoud, Mark Hoffmann, Hassan Reza

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Tags: Algorithms, Computer science, CUDA, nVidia, Sparse matrix, Tesla K20

September 9, 2018 by hgpu

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

Edward Choi

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Tags: CUDA, Deep learning, Medicine, nVidia, nVidia GeForce GTX Titan X, Package, RNN, Tesla K80, Theano, Thesis

September 9, 2018 by hgpu

Cracks in the Sky: Abelian-Higgs Cosmic String Evolution with CUDA

J. R. C. C. C. Correia, C. J. A. P. Martins

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Tags: Computational Physics, CUDA, High Energy Physics - Phenomenology, nVidia, nVidia Quadro P 5000, Physics

September 9, 2018 by hgpu

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

Jianyu Huang, Chenhan D. Yu, Robert A. van de Geijn

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Package, Tesla V100

September 2, 2018 by hgpu

Full Speed Ahead: 3D Spatial Database Acceleration with GPUs

Lucas C. Villa Real, Bruno Silva

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Tags: Computer science, CUDA, Databases, nVidia, Tesla V100

September 2, 2018 by hgpu

Auto-tuning Hybrid CPU-GPU Execution of Algorithmic Skeletons in SkePU

Tomas Ohberg

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Tags: Computer science, CUDA, Heterogeneous systems, Hybrid computing, nVidia, OpenCL, Tesla K20, Thesis

August 26, 2018 by hgpu

Performance Evaluation of OpenMP’s Target Construct on GPUs – Exploring Compiler Optimizations

Akihiro Hayashi, Jun Shirako, Etorre Tiotto, Robert Ho, Vivek Sarkar

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Tags: Compilers, Computer science, CUDA, LLVM, nVidia, OpenMP, Performance, Tesla K80, Tesla P100

August 26, 2018 by hgpu

A Qualitative Comparison Study Between Common GPGPU Frameworks

Adam Soderstrom

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Tags: Computer science, CUDA, DirectCompute, DirectX, N-body simulation, nVidia, nVidia GeForce GTX 1050, OpenCL, Thesis

August 26, 2018 by hgpu

Kernel Tuner: A search-optimizing GPU code auto-tuner

Ben van Werkhoven

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Tags: AMD Radeon Vega Frontier Edition, ATI, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, OpenCL, Optimization, Package

August 19, 2018 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

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high performance computing on graphics processing units: hgpu.org

SoaAlloc: Accelerating Single-Method Multiple-Objects Applications on GPUs

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Benchmarking and Optimization of Gradient Boosted Decision Tree Algorithms

Developing a New Storage Format and a Warp-Based SpMV Kernel for Configuration Interaction Sparse Matrices on the GPU

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

Cracks in the Sky: Abelian-Higgs Cosmic String Evolution with CUDA

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

Full Speed Ahead: 3D Spatial Database Acceleration with GPUs

Auto-tuning Hybrid CPU-GPU Execution of Algorithmic Skeletons in SkePU

Performance Evaluation of OpenMP’s Target Construct on GPUs – Exploring Compiler Optimizations

A Qualitative Comparison Study Between Common GPGPU Frameworks

Kernel Tuner: A search-optimizing GPU code auto-tuner

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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