high performance computing on graphics processing units: hgpu.org

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Designing Efficient MPI and UPC Runtime for Multicore Clusters with InfiniBand, Accelerators and Co-Processors

Miao Luo

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, MPI, nVidia, Tesla C2050

March 9, 2014 by hgpu

Converting Data to Task-Parallelism by Rewrites

Ryan R. Newton, Eric Holk, Trevor L. McDonell

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Tags: Computer science, CUDA, Haskell, nVidia, Package, Task scheduling, Tesla C2075

March 7, 2014 by hgpu

Exploring High Performance SQL Databases with Graphics Processing Units

Glen Hordemann

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Tags: Computer science, CUDA, Data mining, Databases, nVidia, nVidia GeForce GTX 680, Text mining, Thesis

March 7, 2014 by hgpu

Interactive Program Debugging and Optimization for Directive-Based, Efficient GPU Computing

Seyong Lee, Dong Li, Jeffrey S. Vetter

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Tags: Benchmarking, Computer science, CUDA, nVidia, OpenACC, Performance, Tesla M2090

March 7, 2014 by hgpu

Dynamic Workload Division in GPU-CPU Heterogeneous Systems

Wei Chen

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla C2070, Thesis

March 7, 2014 by hgpu

OCCA: A unified approach to multi-threading languages

David S Medina, Amik St-Cyr, T. Warburton

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Tags: ATI, ATI Radeon HD 7970, ATI Radeon HD 7990, Computer science, CUDA, Finite difference, nVidia, nVidia GeForce GTX Titan, OpenCL, Spectral elements

March 6, 2014 by hgpu

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

M.G.B. Johnson, D. P. Playne, K.A. Hawick

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 590, nVidia GeForce GTX 660 M, nVidia GeForce GTX 680, Performance, Tesla M2050, Tesla M2070, Tesla M2075, Tesla M2090

March 6, 2014 by hgpu

Performance Analysis for GPU-based Ray-triangle Algorithms

Juan J. Jimenez, Carlos J. Ogayar, Jose M. Noguera, Felix Paulano

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 560 Ti, Performance, Raytracing

March 6, 2014 by hgpu

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

K.A. Hawick, D.P.Playne

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, nVidia GeForce GTX 680, Software Engineering

March 4, 2014 by hgpu

Computational Experiments in Markov Chain Monte Carlo

Alexander D. Kaiser

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Tags: Computer science, Monte Carlo simulation, nVidia, nVidia GeForce GTX 590, OpenCL, Probability, Thesis

March 4, 2014 by hgpu

Increasing programmability of an embedded domain specific language for GPGPU kernels using static analysis

Niklas Ulvinge

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Thesis

March 4, 2014 by hgpu

Performance Optimization of Clustering On GPU

Vijal D. Patel, Sumitra Menaria

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Tags: Algorithms, Clustering, Computer science, CUDA, Hierarchical clustering, nVidia, nVidia GeForce GTX 480, Tesla C2070

March 4, 2014 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

Designing Efficient MPI and UPC Runtime for Multicore Clusters with InfiniBand, Accelerators and Co-Processors

Converting Data to Task-Parallelism by Rewrites

Exploring High Performance SQL Databases with Graphics Processing Units

Interactive Program Debugging and Optimization for Directive-Based, Efficient GPU Computing

Dynamic Workload Division in GPU-CPU Heterogeneous Systems

OCCA: A unified approach to multi-threading languages

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

Performance Analysis for GPU-based Ray-triangle Algorithms

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

Computational Experiments in Markov Chain Monte Carlo

Increasing programmability of an embedded domain specific language for GPGPU kernels using static analysis

Performance Optimization of Clustering On GPU

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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