AMD Radeon Instinct MI250

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GPU Performance Portability needs Autotuning

Burkhard Ringlein, Thomas Parnell, Radu Stoica

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Tags: AMD Radeon Instinct MI250, ATI, Auto-Tuning, Computer science, CUDA, DSL, HIP, LLM, nVidia, nVidia A100, Performance, performance portability

May 18, 2025 by hgpu

LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning

Neha Prakriya, Zijian Ding, Yizhou Sun, Jason Cong

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Tags: AMD Radeon Instinct MI250, ATI, Computer science, FPGA, HLS, LLM, Machine learning, Neural networks, PyTorch

May 4, 2025 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

Testing GPU Numerics: Finding Numerical Differences Between NVIDIA and AMD GPUs

Anwar Hossain Zahid, Ignacio Laguna, Wei Le

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Tags: AMD Radeon Instinct MI250, ATI, Computer science, CUDA, HIP, Numerical Analysis, nVidia, nVidia V100, Programming Languages

October 20, 2024 by hgpu

OpenMP Kernel Language Extensions for Performance Portable GPU Codes

Shilei Tian, Tom Scogland, Barbara Chapman, Johannes Doerfert

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Tags: AMD Radeon Instinct MI250, ATI, Benchmarking, Compilers, Computer science, CUDA, HIP, nVidia, nVidia A100, OpenMP, performance portability

October 1, 2023 by hgpu

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

Engineering Supercomputing Platforms for Biomolecular Applications

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

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

Can Large Language Models Predict Parallel Code Performance?

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high performance computing on graphics processing units: hgpu.org

GPU Performance Portability needs Autotuning

LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning

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

Testing GPU Numerics: Finding Numerical Differences Between NVIDIA and AMD GPUs

OpenMP Kernel Language Extensions for Performance Portable GPU Codes

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hpcbench: A set of benchmarking utilities for biomolecular simulation tools

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

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microSYCL: SYCL micro-benchmarks repository

XaaS containers

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

SYCL Container

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

Can Large Language Models Predict Parallel Code Performance?

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