high performance computing on graphics processing units: hgpu.org

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Perfect Hashing Structures for Parallel Similarity Searches

Tuan Tu Tran, Mathieu Giraud, Jean-Stephane Varre

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Tags: Algorithms, Computer science, Databases, Filtering, Hashing, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX Titan, OpenCL

June 16, 2015 by hgpu

Falcon: A Graph Manipulation Language for Heterogeneous Systems

Unnikrishnan C, Y N Srikant, Rupesh Nasre

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Tags: Compilers, Computer science, CUDA, Graph theory, Heterogeneous systems, nVidia, OpenMP, Tesla C2050, Tesla C2075, Tesla K20

June 16, 2015 by hgpu

Characterizing Dataset Dependence for Sparse Matrix-Vector Multiplication on GPUs

N. Sedaghati, A. Ashari, L. N. Pouchet, S. Parthasarathy, P. Sadayappan

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Performance, Sparse matrix, Tesla K40

June 16, 2015 by hgpu

GPU Predictor-Corrector Interior Point Method for Large-Scale Linear Programming

David Rydberg

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan, Thesis

June 16, 2015 by hgpu

Type-safe Runtime Code Generation: Accelerate to LLVM

Trevor L. McDonell, Manuel M. T. Chakravarty, Vinod Grover, Ryan R. Newton

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Tags: Code generation, Computer science, CUDA, nVidia, Package, Sparse matrix, Tesla K40

June 14, 2015 by hgpu

Automatic Selection of Sparse Matrix Representation on GPUs

Naser Sedaghati, Te Mu, Louis-Noel Pouchet, Srinivasan Parthasarathy, P. Sadayappan

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 580, Physics, Sparse matrix, Tesla K20, Tesla K40

June 14, 2015 by hgpu

A GPU vs CPU performance evaluation of an experimental video compression algorithm

Stamos Katsigiannis, Vasilis Dimitsas, Dimitris Maroulis

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Tags: Algorithms, Compression, CUDA, Image processing, nVidia, Tesla C2070, Video encoding

June 14, 2015 by hgpu

Poisson-Boltzmann model for protein-surface electrostatic interactions and grid-convergence study using the PyGBe code

Christopher D. Cooper, Lorena A. Barba

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Tags: Biomolecules, Boltzmann equation, Computational Physics, CUDA, Electrostatics, nVidia, Package, Physics, Poisson Boltzmann, Python, Tesla C2075

June 14, 2015 by hgpu

Accelerating parameter synthesis for stochastic models

Petr Pilar

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Probability, Sparse matrix, Thesis

June 14, 2015 by hgpu

Practical Symbolic Execution Analysis and Methodology for GPU Programs

Peng Li

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Package, Thesis

June 10, 2015 by hgpu

Performance of a code migration for the simulation of supersonic ejector flow to SMP, MIC and GPU using OpenMP, OpenMP+LEO, and OpenACC directives

C. Couder-Castaneda, H. Barrios-Pina, I. Gitler, M. Arroyo

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Tags: Computer science, Fortran, Intel Xeon Phi, nVidia, OpenACC, OpenMP, Performance, Tesla C2070

June 10, 2015 by hgpu

A Pattern Specification and Optimizations Framework for Accelerating Scientific Computations on Heterogeneous Clusters

Linchuan Chen, Xin Huo, Gagan Agrawal

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, Tesla M2070

June 10, 2015 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

Perfect Hashing Structures for Parallel Similarity Searches

Falcon: A Graph Manipulation Language for Heterogeneous Systems

Characterizing Dataset Dependence for Sparse Matrix-Vector Multiplication on GPUs

GPU Predictor-Corrector Interior Point Method for Large-Scale Linear Programming

Type-safe Runtime Code Generation: Accelerate to LLVM

Automatic Selection of Sparse Matrix Representation on GPUs

A GPU vs CPU performance evaluation of an experimental video compression algorithm

Poisson-Boltzmann model for protein-surface electrostatic interactions and grid-convergence study using the PyGBe code

Accelerating parameter synthesis for stochastic models

Practical Symbolic Execution Analysis and Methodology for GPU Programs

Performance of a code migration for the simulation of supersonic ejector flow to SMP, MIC and GPU using OpenMP, OpenMP+LEO, and OpenACC directives

A Pattern Specification and Optimizations Framework for Accelerating Scientific Computations on Heterogeneous Clusters

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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