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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

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

Masahiro Tanaka, Du Li, Umesh Chand, Ali Zafar, Haiying Shen, Olatunji Ruwase

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Tags: Computer science, CUDA, Deep learning, Distributed computing, nVidia, nVidia H100, Package, Prefetch

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

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

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

Meng Wang, Jinshuo Liu, Zimeng Xu, Chang Ding, Xiaomeng Huang

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia GeForce RTX 4090, Package

April 13, 2025 by hgpu

GigaAPI for GPU Parallelization

M. Suvarna, O. Tehrani

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Tags: Computer science, CUDA, Image processing, nVidia, nVidia RTX 6000 Ada, Package

April 13, 2025 by hgpu

GPU-centric Communication Schemes for HPC and ML Applications

Naveen Namashivayam

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Tags: Computer science, Deep learning, Heterogeneous systems, HPC, Machine learning, MPI, survey

April 13, 2025 by hgpu

A Power-Efficient Scheduling Approach in a Cpu-Gpu Computing System by Thread-Based Parallel Programming

Biao Hu

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Tags: Computer science, Heterogeneous systems, nVidia GeForce RTX 3080, Optimization, Particle swarm optimization

April 13, 2025 by hgpu

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

Michele Martone, Julia Lawall

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Tags: Computer science, CUDA, HIP, HPC, nVidia, OpenMP, Package, Programming Languages

March 30, 2025 by hgpu

PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch

Abhishek Ghosh, Ajay Nayak, Ashish Panwar, Arkaprava Basu

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia RTX A6000, PyTorch

March 30, 2025 by hgpu

Efficient allocation of image recognition and LLM tasks on multi-GPU system

Marcin Lawenda, Krzesimir Samborski, Kyrylo Khloponin, Łukasz Szustak

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Tags: Computer science, CUDA, Data parallelism, Image recognition, LLM, Machine learning, nVidia, nVidia H100, Performance

March 30, 2025 by hgpu

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Jiyuan Wang

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Tags: Benchmarking, Computer science, FPGA, Heterogeneous systems, oneAPI, Package, Python, Thesis

March 30, 2025 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

Applications

LithOS: An Operating System for Efficient Machine Learning on GPUs

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

Data-efficient LLM Fine-tuning for Code Generation

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

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

GigaAPI for GPU Parallelization

GPU-centric Communication Schemes for HPC and ML Applications

A Power-Efficient Scheduling Approach in a Cpu-Gpu Computing System by Thread-Based Parallel Programming

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch

Efficient allocation of image recognition and LLM tasks on multi-GPU system

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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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