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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

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

A hybrid algorithm for parallel molecular dynamics simulations

Chris M. Mangiardi, Ralf Meyer

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Tags: Condensed matter, Intel Xeon Phi, Materials Science, Molecular dynamics

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

A Survey of Techniques for Architecting TLBs

Sparsh Mittal

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Tags: cpu, CPU-GPU system, GPU, Power management, survey, TLB, virtual memory

November 2, 2016 by sparsh0mittal

Classification Performance of Convolutional Neural Networks

Niklas Mattsson

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Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 960, nVidia Tegra TX1, Performance, Python, Theano, Thesis

November 1, 2016 by hgpu

Towards Automating Multi-dimensional Data Decomposition for Executing a Single-GPU Code on a Multi-GPU System

Ryotaro Sakai, Fumihiko Ino, Kenichi Hagihara

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, Performance, Tesla K40

November 1, 2016 by hgpu

Programming Heterogeneous Systems from an Image Processing DSL

Jing Pu, Steven Bell, Xuan Yang, Jeff Setter, Stephen Richardson, Jonathan Ragan-Kelley, Mark Horowitz

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Tags: ARM, Computer science, Computer vision, CUDA, DSL, FPGA, Image processing, nVidia, nVidia Jetson TK1, OpenGL, Package

November 1, 2016 by hgpu

LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

Xiang Li, Tao Qin, Jian Yang, Tie-Yan Liu

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Tags: Algorithms, Computer science, Deep learning, Neural networks, NLP, nVidia, RNN, Tesla K20

November 1, 2016 by hgpu

Performance Optimization of 3-D Lattice Boltzmann Flow Solver on a GPU

Nhat-Phuong Tran, Myungho Lee, Sugwon Hong

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Tags: Computer science, CUDA, Fluid dynamics, Lattice Boltzmann model, Numerical simulation, nVidia, Performance, Tesla K20

November 1, 2016 by hgpu

Design and Analysis of Soft-Error Resilience Mechanisms for GPU Register File

Sparsh Mittal, Haonan Wang, Adwait Jog and Jeffrey Vetter

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Tags: GPU, Register file, Reliability

November 1, 2016 by sparsh0mittal

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

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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

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high performance computing on graphics processing units: hgpu.org

Applications

Extensions and Limitations of the Neural GPU

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

MILC staggered conjugate gradient performance on Intel KNL

A hybrid algorithm for parallel molecular dynamics simulations

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

A Survey of Techniques for Architecting TLBs

Classification Performance of Convolutional Neural Networks

Towards Automating Multi-dimensional Data Decomposition for Executing a Single-GPU Code on a Multi-GPU System

Programming Heterogeneous Systems from an Image Processing DSL

LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

Performance Optimization of 3-D Lattice Boltzmann Flow Solver on a GPU

Design and Analysis of Soft-Error Resilience Mechanisms for GPU Register File

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

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

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

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

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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