high performance computing on graphics processing units: hgpu.org

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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

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

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

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

Radostin Stoyanov, Viktória Spišaková, Jesus Ramos, Steven Gurfinkel, Andrei Vagin, Adrian Reber, Wesley Armour, Rodrigo Bruno

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Tags: AMD Radeon Instinct MI210, ATI, Computer science, CUDA, Deep learning, nVidia, nVidia A100, nVidia H100, nVidia RTX A6000, Package, ROCm

March 3, 2025 by hgpu

The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition

Robert Tjarko Lange, Aaditya Prasad, Qi Sun, Maxence Faldor, Yujin Tang, David Ha

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Tags: AI, Computer science, CUDA, LLM, nVidia, nVidia H100, Package, Performance

February 24, 2025 by hgpu

Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs

Youhe Jiang, Fangcheng Fu, Xiaozhe Yao, Guoliang He, Xupeng Miao, Ana Klimovic, Bin Cui, Binhang Yuan, Eiko Yoneki

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Tags: Benchmarking, Cloud, Computer science, Heterogeneous systems, LLM, nVidia, nVidia GeForce RTX 4090, nVidia H100, nVidia RTX A6000, Package

February 10, 2025 by hgpu

Column-Oriented Datalog on the GPU

Yihao Sun, Sidharth Kumar, Thomas Gilray, Kristopher Micinski

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Tags: Computer science, CUDA, Databases, nVidia, nVidia H100, Package

January 27, 2025 by hgpu

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

Naifeng Zhang, Franz Franchetti

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Tags: Code generation, Computer science, CUDA, Linear Algebra, Modular arithmetic, nVidia, nVidia GeForce RTX 4090, nVidia H100, nVidia V100, Package, Security

January 20, 2025 by hgpu

Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

Jonah Ekelund, Stefano Markidis, Ivy Peng

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Tags: Computer science, CUDA, FDTD, HIP, nVidia, nVidia A100, nVidia H100, Package, Performance

January 20, 2025 by hgpu

CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection

Ruijun Feng, Hammond Pearce, Pietro Liguori, Yulei Sui

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Tags: Computer science, CUDA, LLM, nVidia, nVidia H100, Python, PyTorch, Security

January 13, 2025 by hgpu

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

Stefan Zellmann

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Tags: 3D Graphics and Realism, HPC, MPI, nVidia, nVidia H100, Package, Rendering, Visualization

January 13, 2025 by hgpu

high performance computing on graphics processing units: hgpu.org

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

LithOS: An Operating System for Efficient Machine Learning on GPUs

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

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

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition

Column-Oriented Datalog on the GPU

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

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