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SMCGen: Generating Reconfigurable Design for Sequential Monte Carlo Applications

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

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

Andrew L. Beam, Alison Motsinger-Reif, Jon Doyle

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Tags: Bayesian, Biology, CUDA, Machine learning, Neural networks, nVidia, Package, Python

April 17, 2014 by hgpu

Exploring the power of GPU’s for training Deep Belief Networks

Vivek Kulkarni

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 570

April 9, 2014 by hgpu

Parallel Support Vector Machines in Practice

Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, Package, Tesla C2075

April 6, 2014 by hgpu

Parallel Spectral Graph Partitioning on CUDA

Orhan Firat, Alptekin Temizel

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Tags: Algorithms, CUDA, Image processing, Machine learning, nVidia, Tesla C2070

February 23, 2014 by hgpu

Fast American Basket Option Pricing on a multi-GPU Cluster

Michael Benguigui, Francoise Baude

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Tags: Finance, GPU cluster, Machine learning, nVidia, OpenCL, Option pricing, Tesla M2050, Tesla M2075, Tesla S1070

February 17, 2014 by hgpu

Implementing the Projected Spatial Rich Features on a GPU

Andrew D. Ker

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Tags: Computer science, CUDA, Machine learning, nVidia, Tesla K20

February 11, 2014 by hgpu

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Ying Zhang, Saizheng Zhang

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Neural networks, nVidia

February 2, 2014 by hgpu

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization

NVIDIA Object Oriented Agents

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

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high performance computing on graphics processing units: hgpu.org

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

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

Exploring the power of GPU’s for training Deep Belief Networks

Parallel Support Vector Machines in Practice

Parallel Spectral Graph Partitioning on CUDA

Fast American Basket Option Pricing on a multi-GPU Cluster

Implementing the Projected Spatial Rich Features on a GPU

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Recent source codes

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

TensorCorePTX: PTX Tensor Core GEMM Exploration

FlashPDE: Triton-fused PDE stencil kernels for physics-informed neural networks

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

NVIDIA Object Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

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