high performance computing on graphics processing units: hgpu.org

Programming

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An Implementation of Real-Time Phased Array Radar Fundamental Functions on a DSP-Focused, High-Performance, Embedded Computing Platform

Xining Yu, Yan Zhang, Ankit Patel, Allen Zahrai, Mark Weber

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Tags: DSP, Filtering, OpenCL, OpenMP, Signal processing

September 10, 2016 by hgpu

GPU-ArraySort: A parallel, in-place algorithm for sorting large number of arrays

Muaaz Awan, Fahad Saeed

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Tags: Algorithms, big data, Computer science, CUDA, nVidia, Package, Sorting, Tesla K40

September 10, 2016 by hgpu

System Design Principles for Heterogeneous Resource Management and Scheduling in Accelerator-Based Systems

Dipanjan Sengupta

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Tags: Algorithms, Cloud, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Quadro 2000, nVidia Quadro 4000, Task scheduling, Tesla C2050, Tesla C2070, Tesla K20, Thesis

September 10, 2016 by hgpu

3D Object Recognition with Convolutional Neural Networks

Alberto Garcia-Garcia

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Tags: CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GT 730, nVidia GeForce GTX Titan X, Tesla K40, Thesis

September 10, 2016 by hgpu

OpenSBLI: A framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures

Christian T. Jacobs, Satya P. Jammy, Neil D. Sandham

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Tags: cfd, Code generation, Computer science, CUDA, Finite difference, Fluid dynamics, Mathematical Software, MPI, nVidia, OpenCL, Tesla K40

September 10, 2016 by hgpu

Caffeine: Towards Uniformed Representation and Acceleration for Deep Convolutional Neural Networks

Chen Zhang, Zhenman Fang, Peipei Zhou, Peichen Pan, Jason Cong

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Tags: Caffe, Computer science, CUDA, Deep learning, DSP, FPGA, Neural networks, nVidia, Tesla K40

September 8, 2016 by hgpu

A Lightweight Approach to Performance Portability with targetDP

Alan Gray, Kevin Stratford

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Tags: Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, performance portability, QCD, Tesla K20, Tesla K40

September 8, 2016 by hgpu

OpenCL/CUDA algorithms for parallel decoding of any irregular LDPC code using GPU

Jan Broulim, Alexander Ayriyan, Vjaceslav Georgiev

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Tags: Algorithms, Computer science, CUDA, Error recovery, nVidia, OpenCL, Tesla K40, Tutorial

September 8, 2016 by hgpu

A Non-linear GPU Thread Map for Triangular Domains

Cristobal A. Navarro, Benjamin Bustos, Nancy Hitschfeld

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 765 M, Performance, Tesla K40

September 8, 2016 by hgpu

QSL Squasher: A Fast Quasi-Separatrix Layer Map Calculator

Svetlin Tassev, Antonia Savcheva

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Tags: AMD FirePro W8100, Astrophysics, ATI, OpenCL, Package, Solar and Stellar Astrophysics

September 8, 2016 by hgpu

cf4ocl: a C framework for OpenCL

Nuno Fachada, Vitor V. Lopes, Rui C. Martins, Agostinho C. Rosa

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Tags: GPGPU, HPC, OpenCL, Profiling

September 6, 2016 by fakenmc

Parallel Dictionary Learning Algorithms for Sparse Representations

Paul Irofti

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Tags: ATI, ATI FirePro V8800, Computer science, Data mining, Machine learning, OpenCL, Signal processing, Thesis

September 5, 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

Programming

An Implementation of Real-Time Phased Array Radar Fundamental Functions on a DSP-Focused, High-Performance, Embedded Computing Platform

GPU-ArraySort: A parallel, in-place algorithm for sorting large number of arrays

3D Object Recognition with Convolutional Neural Networks

OpenSBLI: A framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures

Caffeine: Towards Uniformed Representation and Acceleration for Deep Convolutional Neural Networks

A Lightweight Approach to Performance Portability with targetDP

OpenCL/CUDA algorithms for parallel decoding of any irregular LDPC code using GPU

A Non-linear GPU Thread Map for Triangular Domains

QSL Squasher: A Fast Quasi-Separatrix Layer Map Calculator

cf4ocl: a C framework for OpenCL

Parallel Dictionary Learning Algorithms for Sparse Representations

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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