high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

Tamp: A Library for Compact Deep Neural Networks with Structured Matrices

Bingchen Gong, Brendan Jou, Felix Yu, Shih-Fu Chang

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Tags: Caffe, Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, TensorFlow

November 8, 2016 by hgpu

Accelerate Deep Learning Inference with MCTS in the game of Go on the Intel Xeon Phi

Ching-Nung Lin, Shi-Jim Yen

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Tags: Computer science, Deep learning, Intel Xeon Phi, Neural networks

November 8, 2016 by hgpu

Vispark: GPU-Accelerated Distributed Visual Computing Using Spark

Woohyuk Choi, Sumin Hong, Won-Ki Jeong

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Tags: Clustering, Computer science, CUDA, Distributed computing, DSL, GPU cluster, Image processing, MapReduce, nVidia, nVidia GeForce GTX Titan, Python, Rendering, Spark

November 8, 2016 by hgpu

UNICORN: A Bulk Synchronous Programming Model, Framework and Runtime for Hybrid CPU-GPU Clusters

Tarun Beri

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Tags: Computer science, CUDA, FFT, GPU cluster, Linear Algebra, Matrix multiplication, nVidia, Package, Prefetch, Tesla M2070, Thesis

November 5, 2016 by hgpu

Molecular Activity Prediction using Deep Learning Software Library

Yoshiki Kato, Shinji Hamada, Hitoshi Goto

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Tags: Bioinformatics, Chemistry, Computer science, CUDA, Deep learning, nVidia, Package, Python, Tesla K20

November 5, 2016 by hgpu

HPVM: A Portable Virtual Instruction Set for Heterogeneous Parallel Systems

Prakalp Srivastava, Maria Kotsifakou, Vikram Adve

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Tags: Code generation, Computer science, Data parallelism, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, OpenCL, performance portability

November 5, 2016 by hgpu

A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs

Elias Stehle, Hans-Arno Jacobsen

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan X, Sorting

November 5, 2016 by hgpu

grim: A Flexible, Conservative Scheme for Relativistic Fluid Theories

Mani Chandra, Francois Foucart, Charles F. Gammie

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Tags: Astrophysics, Magnetohydrodynamics, nVidia, OpenCL, Package, Physics, Tesla K20

November 5, 2016 by hgpu

Extensions and Limitations of the Neural GPU

Eric Price, Wojciech Zaremba, Ilya Sutskever

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Tags: Algorithms, Computer science, CUDA, Deep learning, nVidia, Package, Python

November 3, 2016 by hgpu

Diplomat: Mapping of multi-kernel applications using a static dataflow abstraction

Bruno Bodin, Luigi Nardi, Paul H. J. Kelly, Michael F. P. O'Boyle

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Tags: ARM, Computer science, Computer vision, Heterogeneous systems, OpenCL

November 3, 2016 by hgpu

Hybrid CPU-GPU generation of the Hamiltonian and Overlap matrices in FLAPW methods

Diego Fabregat-Traver, Davor Davidovic, Markus Hohnerbach, Edoardo Di Napoli

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Tags: Computer science, CUDA, nVidia, Performance, Tesla K20, Tesla K40, Tesla K80

November 3, 2016 by hgpu

MILC staggered conjugate gradient performance on Intel KNL

Carleton DeTar, Douglas Doerfler, Steven Gottlieb, Ashish Jha, Dhiraj Kalamkar, Ruizi Li, Doug Toussaint

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Tags: Computational Physics, Data parallelism, High Energy Physics - Lattice, Intel Xeon Phi, MPI, OpenMP, Physics

November 3, 2016 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

Applications

Tamp: A Library for Compact Deep Neural Networks with Structured Matrices

Accelerate Deep Learning Inference with MCTS in the game of Go on the Intel Xeon Phi

Vispark: GPU-Accelerated Distributed Visual Computing Using Spark

UNICORN: A Bulk Synchronous Programming Model, Framework and Runtime for Hybrid CPU-GPU Clusters

Molecular Activity Prediction using Deep Learning Software Library

HPVM: A Portable Virtual Instruction Set for Heterogeneous Parallel Systems

A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs

grim: A Flexible, Conservative Scheme for Relativistic Fluid Theories

Extensions and Limitations of the Neural GPU

Diplomat: Mapping of multi-kernel applications using a static dataflow abstraction

Hybrid CPU-GPU generation of the Hamiltonian and Overlap matrices in FLAPW methods

MILC staggered conjugate gradient performance on Intel KNL

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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