Applications

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A Workload Balanced MapReduce Framework on GPU Platforms

Yue Zhang

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Tags: Benchmarking, Computer science, CUDA, MapReduce, nVidia, Performance, Tesla K40, Thesis

January 12, 2016 by hgpu

Study of low density nuclear matter with quantum molecular dynamics: the role of the symmetry energy

Rana Nandi, Stefan Schramm

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Tags: AMD FirePro S10000, ATI, Molecular dynamics, Nuclear Theory, OpenCL, Physics

January 12, 2016 by hgpu

GPU-Based Fuzzy C-Means Clustering Algorithm for Image Segmentation

Mishal Almazrooie, Mogana Vadiveloo, Rosni Abdullah

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, Tesla C2050

January 7, 2016 by hgpu

Verifying CUDA Programs using SMT-Based Context-Bounded Model Checking

Phillipe A. Pereira, Higo F. Albuquerque, Hendrio M. Marques, Isabela S. Silva, Celso B. Carvalho, Lucas C. Cordeiro

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Tags: Computer science, CUDA, nVidia, Package

January 7, 2016 by hgpu

DeepLearningKit – an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

Amund Tvei, Torbjorn Morland, Thomas Brox Rost

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Source codes

Tags: Computer science, Deep learning, Machine learning, Neural networks, OpenCL, Package, Vulkan

January 7, 2016 by hgpu

Computationally Efficient Tsunami Modelling on Graphics Processing Units (GPU)

Reza Amouzgar, Qiuhua Liang, Peter J. Clarke, Tomohiro Yasuda, Hajime Mase

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Tags: CUDA, Earth and Space Sciences, Fluid dynamics, nVidia, Tesla M2075

January 7, 2016 by hgpu

Faster GPU Based Genetic Programming Using A Two Dimensional Stack

Darren M. Chitty

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Tags: Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 670

January 7, 2016 by hgpu

Batched Linear Algebra Problems on GPU Accelerators

Tingxing Dong

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Tags: Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, Tesla K40, Thesis

January 4, 2016 by hgpu

Programming Models and Scheduling Techniques for Heterogeneous Architectures

Judit Planas Carbonell

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OmpSs, Task scheduling, Tesla K20, Tesla K40, Tesla M2090, Thesis

January 4, 2016 by hgpu

Automatic Performance Tuning of Stencil Computations on Graphics Processing Units

Joseph D. Garvey

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Tags: Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Performance, Stencil computation, Thesis

January 4, 2016 by hgpu

Accelerating Binary Genetic Algorithm Driven Missile Design Optimization Routine with a CUDA Coded Six Degrees-Of-Freedom Simulator

Daniel Benton

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 570

January 4, 2016 by hgpu

CUDA Parallel Algorithms for Forward and Inverse Structural Gravity Problems

Alexander Tsidaev

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Tags: CUDA, Earth and Space Sciences, Geoscience, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 780 Ti, Tesla M2050

January 4, 2016 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

Applications

A Workload Balanced MapReduce Framework on GPU Platforms

Study of low density nuclear matter with quantum molecular dynamics: the role of the symmetry energy

GPU-Based Fuzzy C-Means Clustering Algorithm for Image Segmentation

Verifying CUDA Programs using SMT-Based Context-Bounded Model Checking

DeepLearningKit – an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

Computationally Efficient Tsunami Modelling on Graphics Processing Units (GPU)

Faster GPU Based Genetic Programming Using A Two Dimensional Stack

Batched Linear Algebra Problems on GPU Accelerators

Programming Models and Scheduling Techniques for Heterogeneous Architectures

Automatic Performance Tuning of Stencil Computations on Graphics Processing Units

Accelerating Binary Genetic Algorithm Driven Missile Design Optimization Routine with a CUDA Coded Six Degrees-Of-Freedom Simulator

CUDA Parallel Algorithms for Forward and Inverse Structural Gravity Problems

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

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

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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

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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