high performance computing on graphics processing units: hgpu.org

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cuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPU

Behrooz Zare, Foad Jafarinejad, Matin Hashemi, Saber Salehkaleybar

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Tags: Bayesian, Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 1080

December 23, 2018 by hgpu

Performance Analysis of a Stereo Matching Implementation in OpenCL

Stephan Rotheneder

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 750 Ti, nVidia Quadro 1000 M, OpenCL, Stereo vision

December 16, 2018 by hgpu

Delivering Performance-Portable Stencil Computations on CPUs and GPUs Using Bricks

Tuowen Zhao, Samuel Williams, Mary Hall, Hans Johansen

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Tags: Computer science, CUDA, Intel Xeon Phi, nVidia, performance portability, Stencil computation, Tesla P100

December 16, 2018 by hgpu

SIMD-X: Programming and Processing of Graph Algorithms on GPUs

Hang Liu, H. Howie Huang

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Tags: Computer science, CUDA, Graph theory, nVidia, Tesla K20, Tesla K40, Tesla P100

December 16, 2018 by hgpu

Clacc: Translating OpenACC to OpenMP in Clang

Joel E. Denny, Seyong Lee, Jeffrey S. Vetter

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Tags: Compilers, Computer science, Heterogeneous systems, LLVM, nVidia, nVidia GeForce GTX 1050, OpenACC, OpenMP

December 9, 2018 by hgpu

High Performance Portable Tsunami Simulations on Many-core CPU, GPU, and FPGA

Fumiya Kono

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Tags: AMD FirePro W8100, AMD FirePro W9100, AMD Radeon R9 280X, ATI, CUDA, Earth and Space Sciences, FPGA, Intel Xeon Phi, Numerical simulation, nVidia, OpenCL, Tesla K20, Thesis

December 9, 2018 by hgpu

ASW: Accelerating Smith-Waterman Algorithm on Coupled CPU-GPU Architecture

Huihui Zou, Shanjiang Tang, Ce Yu, Hao Fu, Yusen Li, Wenjie Tang

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Tags: Algorithms, APU, Bioinformatics, Biology, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 750, OpenCL, Sequence alignment, Smith-Waterman algorithm

December 9, 2018 by hgpu

Optimization of a discontinuous finite element solver with OpenCL and StarPU

Philippe Helluy, Laura Mendoza, Bruno Weber

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Tags: Algorithms, Computer science, Hybrid computing, nVidia, nVidia GeForce GTX 1050 Ti, nVidia Quadro P 6000, OpenCL, Package, Task scheduling

December 9, 2018 by hgpu

A Framework for Fast and Efficient Neural Network Compression

Hyeji Kim, Muhammad Umar Karim, Chong-Min Kyung

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Tags: ARM, Caffe, Compression, Computational Complexity, Computer science, Computer vision, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti

December 9, 2018 by hgpu

Mix-and-Match: A Model-driven Runtime Optimisation Strategy for BFS on GPUs

Merijn Verstraaten, Ana Lucia Varbanescu, Cees de Laat

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Tags: Breadth-First Search, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package

December 2, 2018 by hgpu

Parallel source code transformation techniques using design patterns

David del Rio Astorga

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Tags: Algorithms, Auto-Tuning, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Tesla K40, Thesis

December 2, 2018 by hgpu

Performance Portability Challenges for Fortran Applications

Abigail Hsu, David Neill Asanza, Joseph A. Schoonover, Zach Jibben, Neil N. Carlson, Robert Robey

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Tags: Computer science, CUDA, Finite difference, Fortran, nVidia, OpenMP, Package, performance portability, Tesla P100, Tesla V100

December 2, 2018 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

cuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPU

Performance Analysis of a Stereo Matching Implementation in OpenCL

Delivering Performance-Portable Stencil Computations on CPUs and GPUs Using Bricks

SIMD-X: Programming and Processing of Graph Algorithms on GPUs

Clacc: Translating OpenACC to OpenMP in Clang

High Performance Portable Tsunami Simulations on Many-core CPU, GPU, and FPGA

ASW: Accelerating Smith-Waterman Algorithm on Coupled CPU-GPU Architecture

Optimization of a discontinuous finite element solver with OpenCL and StarPU

A Framework for Fast and Efficient Neural Network Compression

Mix-and-Match: A Model-driven Runtime Optimisation Strategy for BFS on GPUs

Parallel source code transformation techniques using design patterns

Performance Portability Challenges for Fortran 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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