high performance computing on graphics processing units: hgpu.org

Yunjae Lee, Juntaek Lim, Jehyeon Bang, Eunyeong Cho, Huijong Jeong, Taesu Kim, Hyungjun Kim, Joonhyung Lee, Jinseop Im, Ranggi Hwang, Se Jung Kwon, Dongsoo Lee, Minsoo Rhu

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Tags: AI, Benchmarking, Computer science, CUDA, Intel, Intel Gaudi-2, nVidia, nVidia A100, Performance

January 6, 2025 by hgpu

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

Avinash Maurya

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Tags: AI, Artificial intelligence, Computer science, CUDA, Deep learning, HPC, nVidia, nVidia DGX-A100, Package, Thesis

December 29, 2024 by hgpu

Hardware Accelerators for Artificial Intelligence

S M Mojahidul Ahsan, Anurag Dhungel, Mrittika Chowdhury, Md Sakib Hasan, Tamzidul Hoque

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Tags: AI, Computer science, Hardware Architecture, Performance, survey

December 1, 2024 by hgpu

SoK: A Systems Perspective on Compound AI Threats and Countermeasures

Sarbartha Banerjee, Prateek Sahu, Mulong Luo, Anjo Vahldiek-Oberwagner, Neeraja J. Yadwadkar, Mohit Tiwari

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Tags: AI, Artificial intelligence, Computer science, LLM, Machine learning, Security

November 24, 2024 by hgpu

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

Krishna Teja Chitty-Venkata, Siddhisanket Raskar, Bharat Kale, Farah Ferdaus, Aditya Tanikanti, Ken Raffenetti, Valerie Taylor, Murali Emani, Venkatram Vishwanath

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Tags: AI, AMD Radeon Instinct MI250, AMD Radeon Instinct MI300X, Artificial intelligence, ATI, Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia A100, nVidia GH200, nVidia H100, OpenCL, Performance

November 10, 2024 by hgpu

Efficient Configuration of Heterogeneous Resources and Task Scheduling Strategies in Deep Learning Auto-Tuning Systems

Pao-Yi Ken, Chao-Chin Wu

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Tags: AI, Artificial intelligence, Computer science, Deep learning, Machine learning, nVidia, nVidia RTX A6000, Task scheduling

October 20, 2024 by hgpu

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high performance computing on graphics processing units: hgpu.org

KernelBench: Can LLMs Write Efficient GPU Kernels?

Seamless acceleration of Fortran intrinsics via AMD AI engines

Forecasting time series with constraints

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

Profiling Apple Silicon Performance for ML Training

Good things come in small packages: Should we adopt Lite-GPUs in AI infrastructure?

Debunking the CUDA Myth Towards GPU-based AI Systems

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

Hardware Accelerators for Artificial Intelligence

SoK: A Systems Perspective on Compound AI Threats and Countermeasures

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

Efficient Configuration of Heterogeneous Resources and Task Scheduling Strategies in Deep Learning Auto-Tuning Systems

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

BoltzGen:Toward Universal Binder Design

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

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

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