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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

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

Huanting Wang, Patrick Lenihan, Zheng Wang

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Tags: Artificial intelligence, Computer science, Machine learning, OpenCL, Optimization, Package, Software Engineering

January 6, 2025 by hgpu

Finding Missed Code Size Optimizations in Compilers using LLMs

Davide Italiano, Chris Cummins

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Tags: Computer science, LLM, Machine learning, Optimization, Programming Languages, Software Engineering

January 6, 2025 by hgpu

TorchQC – A framework for efficiently integrating machine and deep learning methods in quantum dynamics and control

Dimitris Koutromanos, Dionisis Stefanatos, Emmanuel Paspalakis

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Tags: Deep learning, Machine learning, Package, Physics, Python, PyTorch, Quantum Physics

December 29, 2024 by hgpu

A survey on FPGA-based accelerator for ML models

Feng Yan, Andreas Koch, Oliver Sinnen

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Tags: Computer science, FPGA, Hardware Architecture, Machine learning, survey

December 29, 2024 by hgpu

Towards Performance-Aware Allocation for Accelerated Machine Learning on GPU-SSD Systems

Ayush Gundawar, Euijun Chung, Hyesoon Kim

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Tags: Computer science, Hardware Architecture, Machine learning, Performance

December 15, 2024 by hgpu

Automating Energy-Efficient GPU Kernel Generation: A Fast Search-Based Compilation Approach

Yijia Zhang, Zhihong Gou, Shijie Cao, Weigang Feng, Sicheng Zhang, Guohao Dai, Ningyi Xu

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Tags: Computer science, CUDA, Energy-efficient computing, Machine learning, Neural networks, nVidia, nVidia A100, nVidia GeForce RTX 4090, Performance

December 8, 2024 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

Simone Balducci

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Tags: Astrophysics, ATI, Clustering, CUDA, FPGA, HIP, Machine learning, nVidia, Package, performance portability, Physics, Python, Tesla T4, Thesis

December 1, 2024 by hgpu

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

Xiaoteng (Frank) Liu, Pavly Halim

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Tags: Computer science, CUDA, Energy-efficient computing, GEMM, Machine learning, Matrix multiplication, nVidia, nVidia GeForce RTX 4070, Package, Performance

December 1, 2024 by hgpu

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization

NVIDIA Object Oriented Agents

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

See all packages

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high performance computing on graphics processing units: hgpu.org

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

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

Finding Missed Code Size Optimizations in Compilers using LLMs

TorchQC – A framework for efficiently integrating machine and deep learning methods in quantum dynamics and control

A survey on FPGA-based accelerator for ML models

Towards Performance-Aware Allocation for Accelerated Machine Learning on GPU-SSD Systems

Automating Energy-Efficient GPU Kernel Generation: A Fast Search-Based Compilation Approach

CLUEstering: a high-performance density-based clustering library for scientific computing

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

Recent source codes

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

TensorCorePTX: PTX Tensor Core GEMM Exploration

FlashPDE: Triton-fused PDE stencil kernels for physics-informed neural networks

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

NVIDIA Object Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

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