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CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark

Ahmed Heakl, Sarim Hashmi, Gustavo Bertolo Stahl, Seung Hun Eddie Han, Salman Khan, Abdulrahman Mahmoud

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Tags: AI, AMD Radeon RX 7900 XT, ATI, Computer science, CUDA, HIP, Machine learning, nVidia, nVidia A100, OpenCL, Package, Programming Languages, PTX

May 25, 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

Scaling On-Device GPU Inference for Large Generative Models

Jiuqiang Tang, Raman Sarokin, Ekaterina Ignasheva, Grant Jensen, Lin Chen, Juhyun Lee, Andrei Kulik, Matthias Grundmann

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Tags: AI, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia GeForce RTX 4090, OpenCL

May 4, 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

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

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

LLMPerf: GPU Performance Modeling meets Large Language Models

Khoi N.M. Nguyen, Hoang Duy Nguyen Do, Huyen Thao Le, Thanh Tuan Dao

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Tags: Computer science, LLM, Machine learning, nVidia, OpenCL, Performance, Tesla V100

March 23, 2025 by hgpu

Forecasting time series with constraints

Nathan Doumèche, Francis Bach, Éloi Bedek, Gérard Biau, Claire Boyer, Yannig Goude

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Tags: AI, Benchmarking, Computer science, Linear Algebra, Machine learning, nVidia, nVidia L4, Package

February 24, 2025 by hgpu

KernelBench: Can LLMs Write Efficient GPU Kernels?

Anne Ouyang, Simon Guo, Simran Arora, Alex L. Zhang, William Hu, Christopher Ré, Azalia Mirhoseini

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Tags: AI, Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia L40s, Package, PyTorch

February 24, 2025 by hgpu

Ilargi: a GPU Compatible Factorized ML Model Training Framework

Wenbo Sun, Rihan Hai

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Tags: Computer science, CUDA, Linear Algebra, Machine learning, nVidia, nVidia A40, Package

February 10, 2025 by hgpu

On the Partitioning of GPU Power among Multi-Instances

Tirth Vamja, Kaustabha Ray, Felix George, UmaMaheswari C Devi

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Tags: Computer science, CUDA, Energy-efficient computing, Machine learning, Matrix multiplication, nVidia, nVidia A100, nVidia V100, Performance

February 3, 2025 by hgpu

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

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

MSCCL++: A GPU-driven communication stack for scalable AI applications

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

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

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high performance computing on graphics processing units: hgpu.org

CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark

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

Scaling On-Device GPU Inference for Large Generative Models

LithOS: An Operating System for Efficient Machine Learning on GPUs

GPU-centric Communication Schemes for HPC and ML Applications

PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch

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

LLMPerf: GPU Performance Modeling meets Large Language Models

Forecasting time series with constraints

KernelBench: Can LLMs Write Efficient GPU Kernels?

Ilargi: a GPU Compatible Factorized ML Model Training Framework

On the Partitioning of GPU Power among Multi-Instances

Recent source codes

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

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

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