high performance computing on graphics processing units: hgpu.org

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Exploring Multiple Dimensions of Parallelism in Junction Tree Message Passing

Lu Zheng, Ole J. Mengshoel

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Tags: Artificial intelligence, Computer science, CUDA, Data mining, nVidia, nVidia GeForce GTX 460

September 13, 2013 by hgpu

Recent progress and challenges in exploiting graphics processors in computational fluid dynamics

Kyle E Niemeyer, Chih-Jen Sung

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Tags: Algorithms, Computational Physics, CUDA, Fluid dynamics, Lattice Boltzmann model, Navier-Stokes equations, NSEs, nVidia, OpenACC, Package, Review, Tesla C2075

September 13, 2013 by hgpu

Accelerating moderately stiff chemical kinetics in reactive-flow simulations using GPUs

Kyle E Niemeyer, Chih-Jen Sung

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Tags: Algorithms, Computational Physics, CUDA, nVidia, Physics, Tesla C2075

September 13, 2013 by hgpu

A massively parallel program to solve the phase field formulation for crack propagation

V. Ziaei-Rad, Y. Shen

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Tags: CUDA, FEM, Finite element method, Mathematics, nVidia

September 13, 2013 by hgpu

Simulation and modeling of physical broadcasts

Ahmed Ahmed

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Tags: Cellular automata, Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GTX 680, Thesis

September 13, 2013 by hgpu

Neptune: An astrophysical smooth particle hydrodynamics code for massively parallel computer architectures

Prashanth Jaikumar

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Tags: Astrophysics, Gravitation, OpenCL, Thesis

September 13, 2013 by hgpu

Fast computation of computer-generated hologram using Xeon Phi coprocessor

Koki Murano, Tomoyoshi Shimobaba, Atsushi Sugiyama, Naoki Takada, Takashi Kakue, Minoru Oikawa, Tomoyoshi Ito

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Tags: Algorithms, Computational Physics, Computer holography, CUDA, Intel Phi, nVidia, Performance, Physics, Tesla K20

September 13, 2013 by hgpu

Histogram Computations on GPUs Kernel using Global and Shared Memory Atomics

C.P.Patidar, Meena Sharma

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Tags: Computer science, CUDA, Memory, nVidia, nVidia GeForce GT 610

September 11, 2013 by hgpu

Evaluating the Viability of Application-Driven Cooperative CPU/GPU Fault Detection

Dong Li, Seyong Lee, Jeffrey S. Vetter

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Tags: Computer science, CUDA, Fault tolerance, Heterogeneous systems, nVidia

September 11, 2013 by hgpu

Coherent transport by adiabatic passage on atom chips

T. Morgan, L. J. O'Riordan, N. Crowley, B. O'Sullivan, Th. Busch

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Tags: CUDA, nVidia, nVidia GeForce GTX 580, Physics, Quantum Physics

September 11, 2013 by hgpu

D5.5.4 – Characterization of Redundancy and Definition of Work Reuse

Stefanos Kaxiras, Georgios Keramidas, Konstantinos Koukos, Iakovos Stamoulis

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 650

September 11, 2013 by hgpu

Hardware-Oblivious Parallelism for In-Memory Column-Stores

Max Heimel, Michael Saecker, Holger Pirk, Stefan Manegold, Volker Markl

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Tags: Computer science, Databases, nVidia, nVidia GeForce GTX 460, OpenCL

September 11, 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

Exploring Multiple Dimensions of Parallelism in Junction Tree Message Passing

Recent progress and challenges in exploiting graphics processors in computational fluid dynamics

Accelerating moderately stiff chemical kinetics in reactive-flow simulations using GPUs

A massively parallel program to solve the phase field formulation for crack propagation

Simulation and modeling of physical broadcasts

Neptune: An astrophysical smooth particle hydrodynamics code for massively parallel computer architectures

Fast computation of computer-generated hologram using Xeon Phi coprocessor

Histogram Computations on GPUs Kernel using Global and Shared Memory Atomics

Evaluating the Viability of Application-Driven Cooperative CPU/GPU Fault Detection

Coherent transport by adiabatic passage on atom chips

D5.5.4 – Characterization of Redundancy and Definition of Work Reuse

Hardware-Oblivious Parallelism for In-Memory Column-Stores

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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