high performance computing on graphics processing units: hgpu.org

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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

Modernization and Optimization of MPI Codes

Tim Jammer

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Tags: Benchmarking, Computer science, HPC, MPI, OpenMP, Package, Performance, Thesis

February 3, 2025 by hgpu

Column-Oriented Datalog on the GPU

Yihao Sun, Sidharth Kumar, Thomas Gilray, Kristopher Micinski

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Tags: Computer science, CUDA, Databases, nVidia, nVidia H100, Package

January 27, 2025 by hgpu

Exploring data flow design and vectorization with oneAPI for streaming applications on CPU+GPU

Cristian Campos, Rafael Asenjo, Angeles Navarro

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Tags: Computer science, Data parallelism, Heterogeneous systems, Intel, Intel UHD 770, oneAPI, Package, SYCL

January 27, 2025 by hgpu

GSParLib: A multi-level programming interface unifying OpenCL and CUDA for expressing stream and data parallelism

Dinei A. Rockenbach, Gabriell Araujo, Dalvan Griebler, Luiz Gustavo Fernandes

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce RTX 3090, OpenCL, OpenMP, Package

January 20, 2025 by hgpu

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

Naifeng Zhang, Franz Franchetti

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Tags: Code generation, Computer science, CUDA, Linear Algebra, Modular arithmetic, nVidia, nVidia GeForce RTX 4090, nVidia H100, nVidia V100, Package, Security

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

Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

Jonah Ekelund, Stefano Markidis, Ivy Peng

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Tags: Computer science, CUDA, FDTD, HIP, nVidia, nVidia A100, nVidia H100, Package, Performance

January 20, 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

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

Manos Pavlidakis, Chris Kitching, Nicholas Tomlinson, Michael Søndergaard

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Tags: ATI, Compilers, Computer science, CUDA, HIP, nVidia, Package, Portability, PTX

January 13, 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

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

Stefan Zellmann

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Tags: 3D Graphics and Realism, HPC, MPI, nVidia, nVidia H100, Package, Rendering, Visualization

January 13, 2025 by hgpu

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

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

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

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high performance computing on graphics processing units: hgpu.org

Applications

Ilargi: a GPU Compatible Factorized ML Model Training Framework

Modernization and Optimization of MPI Codes

Column-Oriented Datalog on the GPU

Exploring data flow design and vectorization with oneAPI for streaming applications on CPU+GPU

GSParLib: A multi-level programming interface unifying OpenCL and CUDA for expressing stream and data parallelism

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

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

Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

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

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

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

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

Recent source codes

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

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

BoltzGen:Toward Universal Binder Design

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

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

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