high performance computing on graphics processing units: hgpu.org

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Geometric Algebra Computing Technology for Accelerated Processing Units

Patrick Charrier, Dietmar Hildenbrand

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Programming techniques

April 1, 2013 by hgpu

A Discussion of Selected Vienna-Libraries for Computational Science

Karl Rupp, Florian Rudolf, Josef Weinbub

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 285, OpenCL, Programming techniques, Review

April 1, 2013 by hgpu

Formal Analysis of GPU Programs with Atomics via Conflict-Directed Delay-Bounding

Wei-Fan Chiang, Ganesh Gopalakrishnan, Guodong Li, Zvonimir Rakamaric

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Tags: Computer science, CUDA, nVidia, Programming techniques

March 31, 2013 by hgpu

Specification and Verification of GPGPU Programs using Permission-Based Separation Logic

Marieke Huisman and Matej Mihelcic

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Tags: Computer science, OpenCL, Programming techniques

March 31, 2013 by hgpu

A journey from single-GPU to optimized multi-GPU SPH with CUDA

E. Rustico, G. Bilotta, G. Gallo, A. Herault, C. Del Negro, R. A. Dalrymple

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Tags: CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 480, Package

March 31, 2013 by hgpu

A Massively Parallel Associative Memory Based on Sparse Neural Networks

Zhe Yao, Vincent Gripon, Michael G. Rabbat

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Tags: Artificial intelligence, Computer science, CUDA, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla C1060

March 31, 2013 by hgpu

CMCpy: Genetic Code-Message Coevolution Models in Python

Peter J. Becich, Brian P. Stark, Harish S. Bhat, David H. Ardell

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 460, Package, Python

March 31, 2013 by hgpu

High Performance Computing using GPGPU’s

Omar Usman Khan

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Tags: Electrodynamics, FFT, nVidia, nVidia GeForce GTX 260, OpenCL, Package, Thesis

March 29, 2013 by hgpu

Warp Size Impact in GPUs: Large or Small?

Ahmad Lashgar, Amirali Baniasadi, Ahmad Khonsari

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Tags: Computer science, GPGPU-sim, Memory, Performance

March 29, 2013 by hgpu

Graphics Processing Unit Acceleration of the Explicit Solution of the Time Domain Volume Integral Equation Using OpenACC

Ahmed Al-Jarro, Alain Clo, Hakan Bagci

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Tags: Electrodynamics, Integral equations, Mathematics, OpenACC

March 29, 2013 by hgpu

Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

Anuj V. Prakash, Anwesha Chaudhury, Rohit Ramachandran

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Tags: CAD, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Pharmaceuticals

March 29, 2013 by hgpu

Accelerating Graph Analysis with Heterogeneous Systems

Ate Penders

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Tags: Algorithms, Computer science, Graph theory, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL, Tesla C2050, Thesis

March 29, 2013 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

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

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

Geometric Algebra Computing Technology for Accelerated Processing Units

A Discussion of Selected Vienna-Libraries for Computational Science

Formal Analysis of GPU Programs with Atomics via Conflict-Directed Delay-Bounding

Specification and Verification of GPGPU Programs using Permission-Based Separation Logic

A journey from single-GPU to optimized multi-GPU SPH with CUDA

A Massively Parallel Associative Memory Based on Sparse Neural Networks

CMCpy: Genetic Code-Message Coevolution Models in Python

High Performance Computing using GPGPU’s

Warp Size Impact in GPUs: Large or Small?

Graphics Processing Unit Acceleration of the Explicit Solution of the Time Domain Volume Integral Equation Using OpenACC

Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

Accelerating Graph Analysis with Heterogeneous Systems

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

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

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

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