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Playdoh: A lightweight Python library for distributed computing and optimisation

Cyrille Rossant, Bertrand Fontaine, Dan F.M. Goodman

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Tags: Computer science, CUDA, Differential equations, Distributed computing, Monte Carlo simulation, nVidia, nVidia GeForce GTX 295, Package, Partial differential equations, PDEs, Python

December 10, 2011 by hgpu

Load Balancing Utilizing Data Redundancy in Distributed Volume Rendering

S. Frey, T. Ertl

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Rendering

December 9, 2011 by hgpu

A design tool for efficient mapping of multimedia applications onto heterogeneous platforms

Chung-Ching Shen, Hsiang-Huang Wu, Nimish Sane, William Plishker, Shuvra S. Bhattacharyya

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Tags: Computer science, CUDA, Flow analysis, Heterogeneous systems, nVidia, nVidia GeForce GTX 260

December 9, 2011 by hgpu

DRAM Scheduling Policy for GPGPU Architectures Based on a Potential Function

Nagesh B. Lakshminarayana, Jaekyu Lee, Hyesoon Kim, Jinwoo Shin

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Tags: Computer science, Hardware Architecture, Memory

December 9, 2011 by hgpu

GPU implementations of scheduling heuristics for heterogeneous computing environments

Sergio Nesmachnow, Mauro Canabe

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Task scheduling, Tesla C1060

December 9, 2011 by hgpu

Computer Finit-Difference Time-Domain Simulation of Electromagnetic Wave Propagation using GPUs

Anna Bonet Manchado

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Tags: Algorithms, CUDA, Data parallelism, Electrodynamics, FDTD, Finite-difference time-domain, Maxwell's equations, nVidia, nVidia GeForce 8800 GTX, Thesis, Visualization

December 9, 2011 by hgpu

Objective-Driven Workload Allocation in Heterogeneous Computing Systems

Qiang Liu, Wayne Luk

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Performance, Tesla C2070

December 9, 2011 by hgpu

Enhanced Parallel ILU (p)-based Preconditioners for Multi-core CPUs and GPUs-The Power (g)-pattern Method

Vincent Heuveline, Dimitar Lukarski, Jan-Philipp Weiss

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Tags: Computer science, CUDA, Factorization, Numerical simulation, nVidia, Tesla S1070

December 9, 2011 by hgpu

Contributions to Parallel Simulation of Equation-Based Models on Graphics Processing Units

Kristian Stavaker

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce 8600, OpenCL, Tesla C1060, Tesla M2050, Thesis

December 9, 2011 by hgpu

Parallel Computation of 2D Morse-Smale Complexes

Nithin Shivashankar, Senthilnathan M, Vijay Natarajan

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 260, OpenCL, Visualization

December 9, 2011 by hgpu

Exploring the Optimization Space of Multi-Core Architectures with OpenCL Benchmarks

Deepak Mathews Panickal

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Tags: ATI, ATI Radeon HD 5470, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTX, OpenCL, Optimization, Tesla C2070, Thesis

December 9, 2011 by hgpu

High Performance Multi-agent System based Simulations

Prashant Sethia

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Tags: Algorithms, Cloud, Computer science, CUDA, Distributed computing, MapReduce, nVidia, Package, Tesla T10

December 8, 2011 by hgpu

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

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high performance computing on graphics processing units: hgpu.org

Applications

Playdoh: A lightweight Python library for distributed computing and optimisation

Load Balancing Utilizing Data Redundancy in Distributed Volume Rendering

A design tool for efficient mapping of multimedia applications onto heterogeneous platforms

DRAM Scheduling Policy for GPGPU Architectures Based on a Potential Function

GPU implementations of scheduling heuristics for heterogeneous computing environments

Computer Finit-Difference Time-Domain Simulation of Electromagnetic Wave Propagation using GPUs

Objective-Driven Workload Allocation in Heterogeneous Computing Systems

Enhanced Parallel ILU (p)-based Preconditioners for Multi-core CPUs and GPUs-The Power (g)-pattern Method

Contributions to Parallel Simulation of Equation-Based Models on Graphics Processing Units

Parallel Computation of 2D Morse-Smale Complexes

Exploring the Optimization Space of Multi-Core Architectures with OpenCL Benchmarks

Recent source codes

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

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