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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

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

Yu Zhao, Wenjie Xue, Weijie Chen, Weizhong Qiang, Deqing Zou, Hai Jin

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Tags: Computer science, CUDA, nVidia, nVidia RTX A4000, Package, Security

September 1, 2024 by hgpu

Bringing Auto-tuning to HIP: Analysis of Tuning Impact and Difficulty on AMD and Nvidia GPUs

Milo Lurati, Stijn Heldens, Alessio Sclocco, Ben van Werkhoven

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Tags: AMD Radeon Instinct MI250X, AMD Radeon Pro W6600, ATI, Computer science, CUDA, HIP, nVidia, nVidia A100, nVidia RTX A4000, Package, Performance, Python

July 28, 2024 by hgpu

Retargeting and Respecializing GPU Workloads for Performance Portability

Ivan R. Ivanov, Oleksandr Zinenko, Jens Domke, Toshio Endo, William S. Moses

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Tags: AMD Radeon Instinct MI210, AMD Radeon RX 6800, ATI, Computer science, CUDA, HIP, HPC, nVidia, nVidia A100, nVidia RTX A4000, Package, performance portability

March 24, 2024 by hgpu

FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs

Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu, Yunhe Feng, Xin Liang, Dingwen Tao, Franck Cappello

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia A100, nVidia RTX A4000, Package, Signal processing

May 7, 2023 by hgpu

GPULZ: Optimizing LZSS Lossless Compression for Multi-byte Data on Modern GPUs

Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu, Martin Swany, Dingwen Tao, Franck Cappello

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia A100, nVidia RTX A4000, Package

April 23, 2023 by hgpu

Cramming: Training a Language Model on a Single GPU in One Day

Jonas Geiping, Tom Goldstein

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Tags: Computer science, Deep learning, NLP, nVidia, nVidia GeForce RTX 2080 Ti, nVidia RTX A4000, nVidia RTX A6000, Package

January 8, 2023 by hgpu

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

Richard Schoonhoven, Bram Veenboer, Ben van Werkhoven, Kees Joost Batenburg

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia RTX A4000, OpenCL, Package, Tesla A100

November 20, 2022 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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high performance computing on graphics processing units: hgpu.org

Guardian: Safe GPU Sharing in Multi-Tenant Environments

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

Bringing Auto-tuning to HIP: Analysis of Tuning Impact and Difficulty on AMD and Nvidia GPUs

Retargeting and Respecializing GPU Workloads for Performance Portability

FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs

GPULZ: Optimizing LZSS Lossless Compression for Multi-byte Data on Modern GPUs

Cramming: Training a Language Model on a Single GPU in One Day

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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