high performance computing on graphics processing units: hgpu.org

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GPU accelerated Hybrid Tree Algorithm for Collision-less N-body Simulations

Tsuyoshi Watanabe, Naohito Nakasato

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Tags: Algorithms, Astrophysics, GPU cluster, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, OpenCL, Tesla M2090

June 25, 2014 by hgpu

Preemptive Thread Block Scheduling with Online Structural Runtime Prediction for Concurrent GPGPU Kernels

Sreepathi Pai, R. Govindarajan, Matthew J. Thazhuthaveetil

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

June 25, 2014 by hgpu

GPU-driven Parallel Processing for Realtime Creation of Tree Animation

Sang-Min Song, Young-Min Kang, Kang-Hyuk Lee, Soo-Yol Ok

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Real-time graphics

June 24, 2014 by hgpu

GPU-based ray-casting of non-rigid deformations: a comparison between direct and indirect approaches

F. M. M. Marreiros, O. Smedby

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Tags: 3D Graphics and Realism, Computed tomography, Computer science, CT, CUDA, nVidia, nVidia GeForce GTX 680, Rendering

June 23, 2014 by hgpu

Using JavaScript and WebCL for Numerical Computations: A Comparative Study of Native and Web Technologies

Faiz Khan, Vincent Foley-Bourgon, Sujay Kathrotia, Erick Lavoie, Laurie Hendren

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Tags: ATI, ATI Radeon HD 7970, Benchmarking, Computer science, Java, nVidia, OpenCL, Package, Tesla C2050

June 23, 2014 by hgpu

Feature Generation for Quantification of Visual Similarity

Tianning Steven Han

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Tags: Algorithms, Computer science, Computer vision, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 480, Tesla K20, Thesis

June 23, 2014 by hgpu

Accelerating Band Linear Algebra Operations on GPUs with Application in Model Reduction

Peter Benner, Ernesto Dufrechou, Pablo Ezzatti, Pablo Igounet, Enrique S. Quintana-Orti, Alfredo Remon

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Tags: Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse matrix, Tesla S2070

June 23, 2014 by hgpu

Efficient GPU-based Training of Recurrent Neural Network Language Models Using Spliced Sentence Bunch

X. Chen, Y. Wang, X. Liu, M.J.F. Gales, P. C. Woodland

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX Titan, Speech recognition

June 23, 2014 by hgpu

An efficient parallel algorithm for accelerating computational protein design

Yichao Zhou, Wei Xu, Bruce R. Donald, Jianyang Zeng

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Tags: Algorithms, Bioinformatics, Biology, CUDA, Java, nVidia, Package, Tesla K20

June 23, 2014 by hgpu

A Scala Prototype to Generate Multigrid Solver Implementations for Different Problems and Target Multi-Core Platforms

Harald Koestler, Christian Schmitt, Sebastian Kuckuk, Frank Hannig, Juergen Teich, Ulrich Ruede

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Tags: Algorithms, Computer science, CUDA, Differential equations, Mathematical Software, nVidia, nVidia GeForce GTX 680, Partial differential equations, PDEs

June 23, 2014 by hgpu

Coupled Vlasov and two-fluid codes on GPUs

M. Rieke, T. Trost, R. Grauer

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Tags: Computational Physics, CUDA, Maxwell's equations, nVidia, Physics, Plasma physics

June 23, 2014 by hgpu

The Power-Performance Tradeoffs of the Intel Xeon Phi on HPC Applications

Bo Li, Hung-Ching Chang, Shuaiwen Leon Song, Chun-Yi Su, Timmy Meyer, John Mooring, Kirk Cameron

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, nVidia, Performance, Tesla C2050

June 22, 2014 by hgpu

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

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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high performance computing on graphics processing units: hgpu.org

GPU accelerated Hybrid Tree Algorithm for Collision-less N-body Simulations

Preemptive Thread Block Scheduling with Online Structural Runtime Prediction for Concurrent GPGPU Kernels

GPU-driven Parallel Processing for Realtime Creation of Tree Animation

GPU-based ray-casting of non-rigid deformations: a comparison between direct and indirect approaches

Using JavaScript and WebCL for Numerical Computations: A Comparative Study of Native and Web Technologies

Feature Generation for Quantification of Visual Similarity

Accelerating Band Linear Algebra Operations on GPUs with Application in Model Reduction

Efficient GPU-based Training of Recurrent Neural Network Language Models Using Spliced Sentence Bunch

An efficient parallel algorithm for accelerating computational protein design

A Scala Prototype to Generate Multigrid Solver Implementations for Different Problems and Target Multi-Core Platforms

Coupled Vlasov and two-fluid codes on GPUs

The Power-Performance Tradeoffs of the Intel Xeon Phi on HPC Applications

Recent source codes

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

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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