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Application of the Characteristic Basis Function Method using CUDA

Juan Ignacio Perez, Eliseo Garcia, Jose A. de Frutos, Felipe Catedra

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Tags: Computer science, CUDA, CULA, nVidia, Tesla C2075

February 16, 2014 by hgpu

LDetector: A Low Overhead Race Detector For GPU Programs

Pengcheng Li, Chen Ding, Xiaoyu Hu, Tolga Soyata

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

February 16, 2014 by hgpu

Work-Efficient Parallel GPU Methods for Single-Source Shortest Paths

Andrew Davidson, Sean Baxter, Michael Garland, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 680, Package

February 15, 2014 by hgpu

GPGPUs: How to Combine High Computational Power with High Reliability

L. Bautista Gomez, F. Cappello, L. Carro, N. DeBardeleben, B. Fang, S. Gurumurthi, S. Keckler, K. Pattabiraman, P. Rech, M. Sonza Reorda

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Tags: Computer science, CUDA, Fault tolerance, nVidia, nVidia GeForce GTX 480, Tesla C2050

February 15, 2014 by hgpu

Optimizing exact computation of Betweenness Centrality for CUDA

Aakriti Gupta

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Tags: Computer science, CUDA, Networks, nVidia

February 15, 2014 by hgpu

Accelerator Aware MPI Micro-benchmarking using CUDA, OpenACC and OpenCL

Sadaf Alam, Ugo Varetto

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, MPI, nVidia, OpenACC, OpenCL, Performance, Tesla K20, Tesla M2090

February 15, 2014 by hgpu

Effective Multi-Modal Retrieval based on Stacked Auto-Encoders

Wei Wang, Beng Chin Ooi, Xiaoyan Yang, Dongxiang Zhang, Yueting Zhuang

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Tags: Computer science, CUDA, Information Retrieval, nVidia, nVidia GeForce GTX Titan, Package, Python

February 14, 2014 by hgpu

High-Performance Zonal Histogramming on Large-Scale Geospatial Rasters Using GPUs and GPU-Accelerated Clusters

Jianting Zhang, Dali Wang

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Tags: Cluster computing, CUDA, Image processing, nVidia, nVidia GeForce GTX Titan, Visualization

February 14, 2014 by hgpu

High-Performance Spatial Query Processing on Big Taxi Trip Data using GPGPUs

Jianting Zhang, Simin You, Le Gruenwald

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Tags: Computer science, CUDA, Hadoop, MapReduce, Nearest neighbour, nVidia, nVidia GeForce GTX Titan

February 14, 2014 by hgpu

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

M. Bernaschi, M. Bisson, F. Salvadore

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, Physics, Spin Systems, Tesla K20

February 14, 2014 by hgpu

Designing Bit-Reproducible Portable High-Performance Applications

Andrea Arteaga, Oliver Fuhrer, Torsten Hoefler

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, MPI, nVidia, Portability, Tesla K20

February 12, 2014 by hgpu

GROMACS on Hybrid CPU-GPU and CPU-MIC Clusters: Preliminary Porting Experiences, Results and Next Steps

Sadaf Alam, Ugo Varetto

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Tags: Computer science, CUDA, GPU cluster, Hybrid computing, Intel Xeon Phi, MPI, nVidia, OpenCL, Tesla K20

February 12, 2014 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

Programming

Application of the Characteristic Basis Function Method using CUDA

LDetector: A Low Overhead Race Detector For GPU Programs

Work-Efficient Parallel GPU Methods for Single-Source Shortest Paths

GPGPUs: How to Combine High Computational Power with High Reliability

Optimizing exact computation of Betweenness Centrality for CUDA

Accelerator Aware MPI Micro-benchmarking using CUDA, OpenACC and OpenCL

Effective Multi-Modal Retrieval based on Stacked Auto-Encoders

High-Performance Zonal Histogramming on Large-Scale Geospatial Rasters Using GPUs and GPU-Accelerated Clusters

High-Performance Spatial Query Processing on Big Taxi Trip Data using GPGPUs

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

Designing Bit-Reproducible Portable High-Performance Applications

GROMACS on Hybrid CPU-GPU and CPU-MIC Clusters: Preliminary Porting Experiences, Results and Next Steps

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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