high performance computing on graphics processing units: hgpu.org

Thomas C.P. Chau, Maciej Kurek, James S. Targett, Jake Humphrey, George Skouroupathis, Alison Eele, Jan Maciejowski, Benjamin Cope, Kathryn Cobden, Philip Leong, Peter Y.K. Cheung, Wayne Luk

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Tags: Algorithms, Computer science, CUDA, FPGA, Machine learning, nVidia, Package, Tesla C2070

May 29, 2014 by hgpu

Automatic Scheduling of Compute Kernels Across Heterogeneous Architectures

Robert F. Lyerly

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Tags: Compilers, Computer science, Heterogeneous systems, Intel Xeon Phi, Machine learning, nVidia, OpenCL, OpenMP, Task scheduling, Tesla C2075

May 9, 2014 by hgpu

Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models

Robert T. McGibbon, Bharath Ramsundar, Mohammad M. Sultan, Gert Kiss, Vijay S. Pande

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Tags: Algorithms, Biology, Biophysics, CUDA, Distributed computing, Machine learning, Molecular dynamics, nVidia, nVidia GeForce GTX Titan

May 9, 2014 by hgpu

One weird trick for parallelizing convolutional neural networks

Alex Krizhevsky

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Tags: Computer science, CUDA, Machine learning, Neural and Evolutionary Computing, nVidia, Tesla K20

April 25, 2014 by hgpu

Measuring the evolving Internet ecosystem with exchange points

Mohammad Zubair Ahmad

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Tags: Computer science, CUDA, Machine learning, Networks, nVidia, nVidia GeForce 9400, nVidia GeForce GTX 260 M, Thesis

April 22, 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

Random Forests of Very Fast Decision Trees on GPU for Mining Evolving Big Data Streams

Increasing Deep Neural Network Acoustic Model Size for Large Vocabulary Continuous Speech Recognition

Feature Generation for Quantification of Visual Similarity

MIC-SVM: Designing A Highly Efficient Support Vector Machine For Advanced Modern Multi-Core and Many-Core Architectures

GPU Implementation of Gaussian Processes

Mapping parallel programs to heterogeneous multi-core systems

Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers

SMCGen: Generating Reconfigurable Design for Sequential Monte Carlo Applications

Automatic Scheduling of Compute Kernels Across Heterogeneous Architectures

Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models

One weird trick for parallelizing convolutional neural networks

Measuring the evolving Internet ecosystem with exchange points

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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