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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

TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitives

Size Zheng, Jin Fang, Xuegui Zheng, Qi Hou, Wenlei Bao, Ningxin Zheng, Ziheng Jiang, Dongyang Wang, Jianxi Ye, Haibin Lin, Li-Wen Chang, Xin Liu

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Tags: Computer science, Deep learning, LLM, nVidia, nVidia H800, PTX

March 30, 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

Mpache: Interaction Aware Multi-level Cache Bypassing on GPUs

Mengyue Xi, Tianyu Guo, Xuanteng Huang, Zejia Lin, Xianwei Zhang

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Tags: cache, Compilers, Computer science, nVidia, PTX

March 10, 2025 by hgpu

cuSZp2: A GPU Lossy Compressor with Extreme Throughput and Optimized Compression Ratio

Yafan Huang, Sheng Di, Guanpeng Li, Franck Cappello

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Tags: Compression, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3080, nVidia GeForce RTX 3090, Package, PTX

February 16, 2025 by hgpu

Fully-Automated Code Generation for Efficient Computation of Sparse Matrix Permanents on GPUs

Deniz Elbek, Kamer Kaya

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia A100, nVidia Quadro GV100, PTX, Sparse matrix

February 3, 2025 by hgpu

Dissecting the NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis

Weile Luo, Ruibo Fan, Zeyu Li, Dayou Du, Hongyuan Liu, Qiang Wang, Xiaowen Chu

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Tags: Benchmarking, Computer science, CUDA, Hardware Architecture, nVidia, nVidia A100, nVidia GeForce RTX 4090, nVidia H800, Performance, PTX

January 27, 2025 by hgpu

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

Manos Pavlidakis, Chris Kitching, Nicholas Tomlinson, Michael Søndergaard

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Tags: ATI, Compilers, Computer science, CUDA, HIP, nVidia, Package, Portability, PTX

January 13, 2025 by hgpu

Utilizing Tensor Cores in Futhark

Kristoffer August Kortbæk, Rune Ejnar Bang Lejbølle

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, High-level Languages, Matrix multiplication, nVidia, nVidia A100, Package, PTX

December 24, 2024 by hgpu

Guardian: Safe GPU Sharing in Multi-Tenant Environments

Manos Pavlidakis, Giorgos Vasiliadis, Anargyros Argyros, Stelios Mavridis, Antony Chazapis, Angelos Bilas

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Tags: Computer science, CUDA, nVidia, nVidia GeForce RTX 3080 Ti, nVidia RTX A4000, PTX, Security

December 8, 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

Using modern C++ to improve CUDA programs

Mythreya Kuricheti

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Tags: Computer science, CUDA, nVidia, nVidia Quadro RTX 5000, Performance, PTX, Thesis

October 27, 2024 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

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

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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high performance computing on graphics processing units: hgpu.org

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

TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitives

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

Mpache: Interaction Aware Multi-level Cache Bypassing on GPUs

cuSZp2: A GPU Lossy Compressor with Extreme Throughput and Optimized Compression Ratio

Fully-Automated Code Generation for Efficient Computation of Sparse Matrix Permanents on GPUs

Dissecting the NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

Utilizing Tensor Cores in Futhark

Guardian: Safe GPU Sharing in Multi-Tenant Environments

Over-synchronization in GPU Programs

Using modern C++ to improve CUDA programs

Recent source codes

CFAL-bench

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

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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