high performance computing on graphics processing units: hgpu.org

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Hybrid Framework for pairwise DNA Sequence Alignment Using the CUDA compatible GPU

H. Khaled, R. El Gohary, N.L. Badr, H. M. Faheem

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GT 540 M, Sequence alignment, Smith-Waterman algorithm

March 6, 2014 by hgpu

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

M.G.B. Johnson, D. P. Playne, K.A. Hawick

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 590, nVidia GeForce GTX 660 M, nVidia GeForce GTX 680, Performance, Tesla M2050, Tesla M2070, Tesla M2075, Tesla M2090

March 6, 2014 by hgpu

Performance Analysis for GPU-based Ray-triangle Algorithms

Juan J. Jimenez, Carlos J. Ogayar, Jose M. Noguera, Felix Paulano

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 560 Ti, Performance, Raytracing

March 6, 2014 by hgpu

Efficient and Scalable Parallel Zonal Statistics on Large-Scale Species Occurrence Data on GPUs

Jianting Zhang, Simin You

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Tags: CUDA, Earth and Space Sciences, Geoscience, nVidia, nVidia GeForce GTX Titan, nVidia Quadro 6000

March 6, 2014 by hgpu

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

K.A. Hawick, D.P.Playne

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, nVidia GeForce GTX 680, Software Engineering

March 4, 2014 by hgpu

Increasing programmability of an embedded domain specific language for GPGPU kernels using static analysis

Niklas Ulvinge

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Thesis

March 4, 2014 by hgpu

Performance Optimization of Clustering On GPU

Vijal D. Patel, Sumitra Menaria

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Tags: Algorithms, Clustering, Computer science, CUDA, Hierarchical clustering, nVidia, nVidia GeForce GTX 480, Tesla C2070

March 4, 2014 by hgpu

Computational Optimization of a Time-Domain Beamforming Algorithm Using CPU and GPU

Johannes Stier, Christopher Hahn, Gero Zechel, Michael Beitelschmidt

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 570, nVidia Quadro FX 3800

March 3, 2014 by hgpu

Increasing predictability of GPU’s

David Defour

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Performance

March 3, 2014 by hgpu

Applications of Linux-Based QT-CUDA Parallel Architecture

Tuyou Peng, Jun Peng

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce G 105 M, OpenGL, Rendering, Visualization

March 1, 2014 by hgpu

Reducing Beamforming Calculation Time with GPU Accelerated Algorithms

Steffen Schmidt

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 430, nVidia GeForce GTX 590, OpenCL

March 1, 2014 by hgpu

CPU-GPU Collaboration for Output Quality Monitoring

Mehrzad Samadi, Scott Mahlke

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Tags: CUDA, Image processing, nVidia, OpenCL

March 1, 2014 by hgpu

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

Geak: Introducing Triton Kernel AI Agent & Evaluation Benchmarks

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

OpenDwarfs 2025: Modernizing the OpenDwarfs Benchmark Suite for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Hybrid Framework for pairwise DNA Sequence Alignment Using the CUDA compatible GPU

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

Performance Analysis for GPU-based Ray-triangle Algorithms

Efficient and Scalable Parallel Zonal Statistics on Large-Scale Species Occurrence Data on GPUs

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

Increasing programmability of an embedded domain specific language for GPGPU kernels using static analysis

Performance Optimization of Clustering On GPU

Computational Optimization of a Time-Domain Beamforming Algorithm Using CPU and GPU

Increasing predictability of GPU’s

Applications of Linux-Based QT-CUDA Parallel Architecture

Reducing Beamforming Calculation Time with GPU Accelerated Algorithms

CPU-GPU Collaboration for Output Quality Monitoring

Recent source codes

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

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