high performance computing on graphics processing units: hgpu.org

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Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Zhonggen Li, Xiangyu Ke, Yifan Zhu, Yunjun Gao, Feifei Li

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia A100, Package, Performance, Prefetch

May 18, 2025 by hgpu

GPU Performance Portability needs Autotuning

Burkhard Ringlein, Thomas Parnell, Radu Stoica

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Tags: AMD Radeon Instinct MI250, ATI, Auto-Tuning, Computer science, CUDA, DSL, HIP, LLM, nVidia, nVidia A100, Performance, performance portability

May 18, 2025 by hgpu

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Aashaka Shah, Abhinav Jangda, Binyang Li, Caio Rocha, Changho Hwang, Jithin Jose, Madan Musuvathi, Olli Saarikivi, Peng Cheng, Qinghua Zhou, Roshan Dathathri, Saeed Maleki, Ziyue Yang

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Tags: AI, AMD Radeon Instinct MI300X, ATI, Computer science, CUDA, Heterogeneous systems, HIP, nVidia, nVidia A100, nVidia H100, Package

April 27, 2025 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

Weijie Lv, Xuan Xia, Sheng-Jun Huang

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia A100, Package, Python, PyTorch

April 27, 2025 by hgpu

LithOS: An Operating System for Efficient Machine Learning on GPUs

Patrick H. Coppock, Brian Zhang, Eliot H. Solomon, Vasilis Kypriotis, Leon Yang, Bikash Sharma, Dan Schatzberg, Todd C. Mowry, Dimitrios Skarlatos

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia A100, nVidia H100, Operating systems

April 27, 2025 by hgpu

Scalability Evaluation of HPC Multi-GPU Training for ECG-based LLMs

Dimitar Mileski, Nikola Petrovski, Marjan Gusev

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Tags: Computer science, CUDA, Deep learning, HPC, LLM, nVidia, nVidia A100, PTX, PyTorch

April 13, 2025 by hgpu

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

Timothée David--Cléris, Guillaume Laibe, Yona Lapeyre

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Tags: AMD, AMD Radeon Instinct MI250X, Astrophysics, CUDA, MPI, nVidia, nVidia A100, OpenMP, Package, Physics, PTX, ROCm, SYCL

March 23, 2025 by hgpu

Concurrent Scheduling of High-Level Parallel Programs on Multi-GPU Systems

Fabian Knorr, Philip Salzmann, Peter Thoman, Thomas Fahringer

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Tags: Benchmarking, Computer science, HPC, nVidia, nVidia A100, Package, performance portability, SYCL, Task scheduling

March 23, 2025 by hgpu

A Microbenchmark Framework for Performance Evaluation of OpenMP Target Offloading

Mohammad Atif, Tianle Wang, Zhihua Dong, Charles Leggett, Meifeng Lin

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Tags: Benchmarking, Computer science, Heterogeneous systems, nVidia A100, nVidia RTX A6000, nVidia V100, OpenMP, Package

March 10, 2025 by hgpu

SUperman: Efficient Permanent Computation on GPUs

Deniz Elbek, Fatih Taşyaran, Bora Uçar, Kamer Kaya

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Tags: Computer science, CUDA, HPC, Numerical Analysis, nVidia, nVidia A100, nVidia Quadro GV100, OpenMPI, Package

March 10, 2025 by hgpu

Can Tensor Cores Benefit Memory-Bound Kernels? (No!)

Lingqi Zhang, Jiajun Huang, Sheng Di, Satoshi Matsuoka, Mohamed Wahib

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Tags: Computer science, CUDA, nVidia, nVidia A100, Performance

March 10, 2025 by hgpu

pyATF: Constraint-Based Auto-Tuning in Python

Richard Schulze, Sergei Gorlatch, Ari Rasch

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Tags: Auto-Tuning, Compilers, Computer science, CUDA, nVidia A100, OpenCL, Package, Performance, Python

March 3, 2025 by hgpu

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

LC Framework

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

OpScanner

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

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high performance computing on graphics processing units: hgpu.org

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

GPU Performance Portability needs Autotuning

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Data-efficient LLM Fine-tuning for Code Generation

LithOS: An Operating System for Efficient Machine Learning on GPUs

Scalability Evaluation of HPC Multi-GPU Training for ECG-based LLMs

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

Concurrent Scheduling of High-Level Parallel Programs on Multi-GPU Systems

A Microbenchmark Framework for Performance Evaluation of OpenMP Target Offloading

SUperman: Efficient Permanent Computation on GPUs

Can Tensor Cores Benefit Memory-Bound Kernels? (No!)

pyATF: Constraint-Based Auto-Tuning in Python

Recent source codes

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

pplx-garden: Perplexity open source garden for inference technology

LC Framework

OpScanner

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

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