high performance computing on graphics processing units: hgpu.org

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Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

Giulio Malenza, Valentina Cesare, Marco Edoardo Santimaria, Robert Birke, Alberto Vecchiato, Ugo Becciani, Marco Aldinucci

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Tags: AMD Radeon Instinct MI250X, Astrophysics, ATI, Computer science, CUDA, HIP, HPC, nVidia, nVidia A100, nVidia H100, nVidia V100, OpenMP, Package, Performance, performance portability, SYCL, Tesla T4

November 24, 2024 by hgpu

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

Lennart Steffen, Reinhard Hinkelmann

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, Intel, Intel Data Center GPU Max 1550, Linear Algebra, nVidia, nVidia A100, nVidia GeForce GTX Titan V, Package, performance portability

November 17, 2024 by hgpu

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Nathaniel Morgan, Caleb Yenusah, Adrian Diaz, Daniel Dunning,Jacob Moore,Erin Heilman, Calvin Roth, Evan Lieberman, Steven Walton, Sarah Brown, Daniel Holladay, Marko Knezevic, Gavin Whetstone, Zachary Baker, Robert Robey

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Tags: Computer science, CUDA, Fortran, HIP, nVidia, Package, performance portability, Pthreads

November 10, 2024 by hgpu

Over-synchronization in GPU Programs

Ajay Nayak, Arkaprava Basu

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3090, Package, Performance, PTX

November 10, 2024 by hgpu

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

Pascal Jahan Elahi

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Tags: Computer science, CUDA, MPI, nVidia, nVidia GH200, OpenMP, Package, Performance

November 10, 2024 by hgpu

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Xiaoman Liu

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Tags: Benchmarking, Computer science, Machine learning, nVidia, nVidia GeForce RTX 4090, Package, Performance, TensorFlow

November 3, 2024 by hgpu

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

Matteo Croci, Garth N. Wells

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Tags: AVX, Computer science, Finite element method, Floating point error, Intel, Matrix multiplication, Mixed precision, Package

October 27, 2024 by hgpu

General-Purpose Computing on Tensor Processors

Kuan-Chieh Hsu

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Tags: Artificial intelligence, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, nVidia Jetson Nano, Package, Sparse matrix, Thesis, TPU

October 27, 2024 by hgpu

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

Xiongxiao Xu, Solomon Abera Bekele, Brice Videau, Kai Shu

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Tags: Computer science, Energy-efficient computing, HPC, Intel, Intel Data Center GPU Max 1550, Package

October 20, 2024 by hgpu

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Reza Abbasi, Sernam Lim

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Tags: AI, Artificial intelligence, Computer science, CUDA, LLM, Machine learning, Memory, nVidia, nVidia GeForce RTX 3090, nVidia Quadro RTX 8000, Package

October 20, 2024 by hgpu

Sound and Partially-Complete Static Analysis of Data-Races in GPU Programs

Dennis Liew, Tiago Cogumbreiro, Julien Lange

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Tags: Artificial intelligence, Computer science, CUDA, nVidia, Package, Performance

October 13, 2024 by hgpu

Effects of OpenCL-Based Parallelization Methods on Explicit Numerical Methods to Solve the Heat Equation

Dániel Koics, Endre Kovács 2, Olivér Hornyák

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Tags: nVidia, OpenCL, Package, PDEs, Performance, Physics

October 13, 2024 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

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

General-Purpose Computing on Tensor Processors

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Sound and Partially-Complete Static Analysis of Data-Races in GPU Programs

Effects of OpenCL-Based Parallelization Methods on Explicit Numerical Methods to Solve the Heat Equation

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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