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Machine-Learning-based Performance Heuristics for Runtime CPU/GPU Selection

Akihiro Hayashi, Kazuaki Ishizaki, Vivek Sarkar, Gita Koblents

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Tags: Code generation, Compilers, Computer science, CUDA, Java, Machine learning, nVidia, OpenCL, Tesla K40

October 27, 2015 by ahayashi

Overlap fermions on GPUs

Nigel Cundy, Weonjong Lee

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Tags: CUDA, High Energy Physics - Lattice, nVidia, nVidia GeForce GTX Titan, Physics

October 27, 2015 by hgpu

ZNN – A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-Core and Many-Core Shared Memory Machines

Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung

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Tags: CNN, Computer science, Computer vision, Deep learning, FFT, Intel Xeon Phi, Machine learning, Package

October 25, 2015 by hgpu

Execution of Compound Multi-Kernel OpenCL Computations in Multi-CPU/Multi-GPU Environments

Fabio Soldado, Fernando Alexandre, Herve Paulino

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Tags: ATI, ATI Radeon HD 7950, Computer science, Heterogeneous systems, OpenCL, Performance

October 25, 2015 by hgpu

Multi-GPU Distributed Parallel Bayesian Differential Topic Modelling

Aaron Q Li

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Bayesian, Computer science, GPU cluster, Machine learning, NLP, nVidia, nVidia GeForce GTX 480, OpenCL, Thesis

October 25, 2015 by hgpu

Modern Gyrokinetic Particle-In-Cell Simulation of Fusion Plasmas on Top Supercomputers

Bei Wang, Stephane Ethier, William Tang, Khaled Ibrahim, Kamesh Madduri, Samuel Williams, Leonid Oliker

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Tags: CUDA, Heterogeneous systems, Intel Xeon Phi, Laplace and Poisson equation, nVidia, Particle-in-cell methods, Physics, Plasma physics, Poisson equation, Tesla K20

October 25, 2015 by hgpu

Join Algorithms on GPUs: A Revisit After Seven Years

Ran Rui, Hao Li, Yi-Cheng Tu

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia

October 25, 2015 by hgpu

Sequential Code Parallelization for Multi-core Embedded Systems: A Survey of Models, Algorithms and Tools

Jorge Alberto Castro Godinez

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Tags: Computer science, Parallelization, Review, Thesis

October 22, 2015 by hgpu

A linguistic approach to concurrent, distributed, and adaptive programming across heterogeneous platforms

Paul Harvey

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Tags: Computer science, Heterogeneous systems, OpenCL, Thesis

October 22, 2015 by hgpu

Stadium Hashing: Scalable and Flexible Hashing on GPUs

Farzad Khorasani, Mehmet E. Belviranli, Rajiv Gupta, Laxmi N. Bhuyan

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Tags: Computer science, CUDA, Hashing, nVidia, nVidia GeForce GTX 780

October 22, 2015 by hgpu

BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing

Linnan Wang, Wei Wu, Jianxiong Xiao, Yi Yang

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Tags: Benchmarking, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX Titan X, Package, Performance, Tesla K40

October 22, 2015 by hgpu

Neurokernel: An Open Source Platform for Emulating the Fruit Fly Brain

Lev E. Givon, Aurel A. Lazar

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Tags: Biology, Computer science, CUDA, nVidia, OpenMPI, Tesla K20

October 22, 2015 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

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

Machine-Learning-based Performance Heuristics for Runtime CPU/GPU Selection

Overlap fermions on GPUs

ZNN – A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-Core and Many-Core Shared Memory Machines

Execution of Compound Multi-Kernel OpenCL Computations in Multi-CPU/Multi-GPU Environments

Multi-GPU Distributed Parallel Bayesian Differential Topic Modelling

Modern Gyrokinetic Particle-In-Cell Simulation of Fusion Plasmas on Top Supercomputers

Join Algorithms on GPUs: A Revisit After Seven Years

Sequential Code Parallelization for Multi-core Embedded Systems: A Survey of Models, Algorithms and Tools

A linguistic approach to concurrent, distributed, and adaptive programming across heterogeneous platforms

Stadium Hashing: Scalable and Flexible Hashing on GPUs

BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing

Neurokernel: An Open Source Platform for Emulating the Fruit Fly Brain

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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