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Overtaking CPU DBMSes with a GPU in Whole-Query Analytic Processing with Parallelism-Friendly Execution Plan Optimization

Adnan Agbaria, David Minor, Natan Peterfruend, Eyal Rozenberg, Ofer Rosenberg, Roman Talyansky

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 780 Ti

October 12, 2016 by hgpu

GPU-Acceleration of In-Memory Data Analytics

Evangelia Sitaridi

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, String matching, Tesla C2070, Tesla K40, Thesis

August 18, 2016 by hgpu

Accelerating Database Query Processing on OpenCL-based FPGAs

Zeke Wang, Johns Paul, Huiyan Cheah, Bingsheng He, Wei Zhang

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Tags: Computer science, Databases, FPGA, OpenCL

July 30, 2016 by hgpu

[Serbian] The Methods and Procedures for Accelerating Operations and Queries in Large Database Systems and Data Warehouse (Big Data Systems)

Jovan Ivkovic

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Tags: big data, Databases, IoT, OpenCL, Thesis

July 11, 2016 by hgpu

Challenges for a GPU-Accelerated Dynamic Programming Approach for Join-Order Optimization

Andreas Meister, Gunter Saake

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Tags: Algorithms, Computer science, Databases

June 2, 2016 by hgpu

Parallel Subgraph Mining on Hybrid Platforms: HPC Systems, Multi-Cores and GPUs

Nilothpal Talukder

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Tags: Algorithms, Computer science, CUDA, Data mining, Databases, MPI, nVidia, Tesla C2075, Thesis

April 29, 2016 by hgpu

GPL: A GPU-based Pipelined Query Processing Engine

Johns Paul, Jiong He, Bingsheng He

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Tags: Analytical model, Computer science, Databases, nVidia, OpenCL

April 24, 2016 by hgpu

Breadth First Search Vectorization on the Intel Xeon Phi

Mireya Paredes, Graham Riley, Mikel Lujan

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Tags: Algorithms, Computer science, Databases, Intel Xeon Phi, Prefetch

April 14, 2016 by hgpu

Algorithmic and Software System Support to Accelerate Data Processing in CPU-GPU Hybrid Computing Environments

Kaibo Wang

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Tags: Algorithms, Computer science, CUDA, Databases, Hybrid computing, nVidia, nVidia GeForce GTX 580, Package, Tesla M2050, Thesis

April 12, 2016 by hgpu

Generic Inverted Index on the GPU

Jingbo Zhou, Qi Guo, H. V. Jagadish, Wenhao Luan, Anthony K. H. Tung, Yueji Yang, Yuxin Zheng

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Source codes

Tags: Computer science, CUDA, Databases, Hashing, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, Tesla K80

March 29, 2016 by hgpu

Analyzing and Improving the Performance of Spatial Database Processing

Bogdan Simion

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Thesis

March 20, 2016 by hgpu

Performance Analysis of kNN on large datasets using CUDA & Pthreads

Sriram Kankatala

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Tags: Computer science, CUDA, Data mining, Databases, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX 550 Ti, Pattern recognition, Pthreads, Thesis

March 5, 2016 by hgpu

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

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high performance computing on graphics processing units: hgpu.org

Overtaking CPU DBMSes with a GPU in Whole-Query Analytic Processing with Parallelism-Friendly Execution Plan Optimization

GPU-Acceleration of In-Memory Data Analytics

Accelerating Database Query Processing on OpenCL-based FPGAs

[Serbian] The Methods and Procedures for Accelerating Operations and Queries in Large Database Systems and Data Warehouse (Big Data Systems)

Challenges for a GPU-Accelerated Dynamic Programming Approach for Join-Order Optimization

Parallel Subgraph Mining on Hybrid Platforms: HPC Systems, Multi-Cores and GPUs

GPL: A GPU-based Pipelined Query Processing Engine

Breadth First Search Vectorization on the Intel Xeon Phi

Algorithmic and Software System Support to Accelerate Data Processing in CPU-GPU Hybrid Computing Environments

Generic Inverted Index on the GPU

Analyzing and Improving the Performance of Spatial Database Processing

Performance Analysis of kNN on large datasets using CUDA & Pthreads

Recent source codes

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

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