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hgpu.org » Programming » CUDA

Revision of Relational Joins for Multi-Core and Many-Core Architectures

Martin Krulis, Jakub Yaghob

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Tags: Algorithms, Computer science, CUDA, Databases, Join, nVidia, nVidia GeForce GTX 580

January 22, 2012 by hgpu

Accelerating the Simulations of the Ising Model by the GPU under the CUDA Environment

Xing Lu, Jing Cai, Peidong Cui, Wei Zhang

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Tags: Algorithms, CUDA, Ising model, Monte Carlo simulation, nVidia, Physics, Tesla C1060

January 22, 2012 by hgpu

Automatic Code Generation and Adaptive Grid Scheduling for GPU Cluster Computing

Xuyuan Jin

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Tags: Cluster computing, Code generation, Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 570

January 21, 2012 by hgpu

GPGPU calculations of gas thermodynamic quantities

Igor Mracka, Peter Somora, Tibor Zacik

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Tags: CUDA, Differential equations, Fluid dynamics, nVidia, nVidia GeForce GTX 570, Tesla C2050

January 21, 2012 by hgpu

Parallel FEM Simulation Using GPUs

Vishnukanthan Kandasamy

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Tags: Computer science, CUDA, FEM, Finite element method, MPI, nVidia

January 21, 2012 by hgpu

EASEA: A Generic Optimization Tool for GPU Machines in Asynchronous Island Model

Laurent A. Baumes, Frederic Kruger, Pierre Collet

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Tags: Algorithms, Computer science, CUDA, Evolutionary Computations, Materials Science, nVidia, nVidia GeForce GTX 275, Package

January 21, 2012 by hgpu

Fast Graph Cuts using Shrink-Expand Reparameterization

Parikshit Sakurikar, P. J. Narayanan

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 580, Optimization

January 21, 2012 by hgpu

On the Correctness of the SIMT Execution Model of GPUs

Axel Habermaier, Alexander Knapp

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Tags: Computer science, CUDA, nVidia, Task scheduling, Thesis

January 20, 2012 by hgpu

Experiences in Teaching a Specialty Multicore Computing Course

Peter E. Strazdins

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Tags: Cloud, Computer science, CUDA, Education, nVidia, Package, Virtualization

January 20, 2012 by hgpu

Efficient fMRI Analysis and Clustering on GPUs

B.E. Dharneesh Talasu

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Tags: Clustering, CUDA, Heterogeneous systems, Hierarchical clustering, Image processing, Medicine, MRI, nVidia, nVidia GeForce 9400 M, Thesis

January 20, 2012 by hgpu

Dynamically Managed Data for CPU-GPU Architectures

Thomas B. Jablin, James A. Jablin, Prakash Prabhu, Feng Liu, David I. August

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Tags: Computer science, CUDA, Data Structures and Algorithms, Memory model, nVidia, nVidia GeForce GTX 480, Performance

January 19, 2012 by hgpu

Platform Characterization for Domain-Specific Computing

Alex Bui, Kwang-Ting (Tim) Cheng, Jason Cong, Luminita Vese, Yi-Chu Wang, Bo Yuan, Yi Zou

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Tesla C1060

January 19, 2012 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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Revision of Relational Joins for Multi-Core and Many-Core Architectures

Accelerating the Simulations of the Ising Model by the GPU under the CUDA Environment

Automatic Code Generation and Adaptive Grid Scheduling for GPU Cluster Computing

GPGPU calculations of gas thermodynamic quantities

Parallel FEM Simulation Using GPUs

EASEA: A Generic Optimization Tool for GPU Machines in Asynchronous Island Model

Fast Graph Cuts using Shrink-Expand Reparameterization

On the Correctness of the SIMT Execution Model of GPUs

Efficient fMRI Analysis and Clustering on GPUs

Dynamically Managed Data for CPU-GPU Architectures

Platform Characterization for Domain-Specific Computing

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

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

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