high performance computing on graphics processing units: hgpu.org

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High-performance software rasterization on GPUs

Samuli Laine, Tero Karras

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Package, Rasterization

October 31, 2011 by hgpu

Parallel implematation of flow and matching algorithms

Agnieszka Lupinska

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 560 Ti

October 31, 2011 by hgpu

Automatic CUDA Code Synthesis Framework for Multicore CPU and GPU architectures

Hanwoong Jung, Youngmin Yi, Soonhoi Ha

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Tags: Code generation, Computer science, CUDA, Data parallelism, Heterogeneous systems, nVidia, Tesla M2050

October 30, 2011 by hgpu

Accelerating Real-time processing of the ATST Adaptive Optics System using Coarse-grained Parallel Hardware Architectures

V. Venugopal, K. Richards, S. Barden, T. Rimmele, S. Gregory, L. Johnson

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Tags: Algorithms, Benchmarking, CUDA, DSP, FPGA, nVidia, Optics, Physics, Tesla C1060, Tesla C2050

October 30, 2011 by hgpu

A scalable hybrid algorithm based on domain decomposition and algebraic multigrid for solving partial differential equations on a cluster of CPU/GPUs

Li Luo, Chao Yang, Yubo Zhao, Xiao-Chuan Cai

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Tags: Algorithms, Computer science, CUDA, Differential equations, Factorization, FFT, Finite difference, GPU cluster, nVidia, Partial differential equations, PDEs, Tesla C1060

October 30, 2011 by hgpu

Efficient Implementation of the eta_T Pairing on GPU

Yosuke Katoh, Yun-Ju Huang, Chen-Mou Cheng, Tsuyoshi Takagi

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Security, Tesla C1060, Tesla C2050

October 30, 2011 by hgpu

Optimizing and Auto-tuning Belief Propagation on the GPU

Scott Grauer-Gray, John Cavazos

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 470, Optimization, Tesla C870

October 30, 2011 by hgpu

Effective Parallelization of Non-bonded Interactions Kernel for Virtual Screening on GPUs

Gines D. Guerrero, Horacio Perez-Sanchez, Wolfgang Wenzel, Jose M. Cecilia, Jose M. Garcia

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Tags: Bioinformatics, Biology, CUDA, nVidia, Optimization, Pharmaceuticals, Tesla C1060

October 30, 2011 by hgpu

Exploring Many-Core Design Templates for FPGAs and ASICs

Ilia Lebedev, Christopher Fletcher, Shaoyi Cheng, James Martin, Austin Doupnik, Daniel Burke, Mingjie Lin, John Wawrzynek

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Tags: ASIC, Bayesian, Computer science, FPGA, nVidia, nVidia GeForce GTX 580, OpenCL, Performance

October 30, 2011 by hgpu

Improving Energy Efficiency of GPU based General-Purpose Scientific Computing through Automated Selection of Near Optimal Configurations

Xiaohan Ma, Marion Rincon, Zhigang Deng

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce 8800 GT

October 30, 2011 by hgpu

Leveraging Binary Translation for Heterogeneous Profiling

Dan Upton, Kim Hazelwood

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Tags: ATI, ATI Radeon HD 5870, Computer science, Heterogeneous systems, nVidia, OpenCL, Performance, Tesla C2050

October 30, 2011 by hgpu

GPU Computations in Heterogeneous Grid Environments

Marcus Hinders

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Tags: ATI, ATI Radeon HD 5870, Biology, Computational biology, Computer science, Grid, Heterogeneous systems, nVidia, nVidia GeForce 8600 GT, nVidia GeForce GTX 280, OpenCL, Thesis

October 30, 2011 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

High-performance software rasterization on GPUs

Parallel implematation of flow and matching algorithms

Automatic CUDA Code Synthesis Framework for Multicore CPU and GPU architectures

Accelerating Real-time processing of the ATST Adaptive Optics System using Coarse-grained Parallel Hardware Architectures

A scalable hybrid algorithm based on domain decomposition and algebraic multigrid for solving partial differential equations on a cluster of CPU/GPUs

Efficient Implementation of the eta_T Pairing on GPU

Optimizing and Auto-tuning Belief Propagation on the GPU

Effective Parallelization of Non-bonded Interactions Kernel for Virtual Screening on GPUs

Exploring Many-Core Design Templates for FPGAs and ASICs

Improving Energy Efficiency of GPU based General-Purpose Scientific Computing through Automated Selection of Near Optimal Configurations

Leveraging Binary Translation for Heterogeneous Profiling

GPU Computations in Heterogeneous Grid Environments

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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