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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

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

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

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

Profiling Apple Silicon Performance for ML Training

Dahua Feng, Zhiming Xu, Rongxiang Wang, Felix Xiaozhu Lin

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Tags: AI, Apple M2 Max, Apple M2 Pro, Apple M2 Ultra, Computer science, CUDA, Linear Algebra, LLM, Machine learning, nVidia, nVidia GeForce RTX 4090, nVidia GeFroce RTX 2080 Ti, nVidia Quadro RTX 4000, nVidia RTX A6000, Performance, PyTorch

February 3, 2025 by hgpu

Adaptive Optimization Techniques for High-Performance Computing

Gulsum Gudukbay Akbulut

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Tags: Computer science, CUDA, Deep learning, HPC, Machine learning, nVidia, Optimization, Performance, Tesla K80, Thesis

January 27, 2025 by hgpu

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

Rémi Genet, Hugo Inzirillo

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 4090, Package, Python, TensorFlow

January 20, 2025 by hgpu

A User’s Guide to KSig: GPU-Accelerated Computation of the Signature Kernel

Csaba Tóth, Danilo Jr Dela Cruz, Harald Oberhauser

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia A100, Package, Python

January 20, 2025 by hgpu

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Jiaping Wang, Simiao Zhang, Qiao-Chu He, Yifan Chen

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Tags: Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia A100, nVidia RTX A6000, Package, Python, PyTorch

January 13, 2025 by hgpu

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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high performance computing on graphics processing units: hgpu.org

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

KernelBench: Can LLMs Write Efficient GPU Kernels?

Forecasting time series with constraints

Ilargi: a GPU Compatible Factorized ML Model Training Framework

On the Partitioning of GPU Power among Multi-Instances

Profiling Apple Silicon Performance for ML Training

Adaptive Optimization Techniques for High-Performance Computing

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

A User’s Guide to KSig: GPU-Accelerated Computation of the Signature Kernel

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

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