high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

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

View

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

December 8, 2024 by hgpu

FortranX: Harnessing Code Generation, Portability, and Heterogeneity in Fortran

Sanil Rao, Mike Franusich, , Mohammad Alaul Haque Monil, Het Mankad, Jeffrey S. Vetter, Franz Franchetti

View

Tags: AMD, ATI, Code generation, Computer science, CUDA, Differential equations, Fortran, Heterogeneous systems, HIP, nVidia, OpenCL, OpenMP, Partial differential equations, PDEs

December 8, 2024 by hgpu

Guardian: Safe GPU Sharing in Multi-Tenant Environments

Manos Pavlidakis, Giorgos Vasiliadis, Anargyros Argyros, Stelios Mavridis, Antony Chazapis, Angelos Bilas

View

Tags: Computer science, CUDA, nVidia, nVidia GeForce RTX 3080 Ti, nVidia RTX A4000, PTX, Security

December 8, 2024 by hgpu

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

Simone Balducci

View

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

December 1, 2024 by hgpu

Scaling SU(2) to 1000 GPUs using HiRep

Sofie Martins, Erik Kjellgren, Emiliano Molinaro, Claudio Pica, Antonio Rago

View

Tags: AMD Radeon Instinct MI250X, ATI, CUDA, HEP, High Energy Physics - Lattice, HIP, Monte Carlo simulation, nVidia, nVidia H100, Package, Physics

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

View

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

December 1, 2024 by hgpu

A Distributed-memory Tridiagonal Solver Based on a Specialised Data Structure Optimised for CPU and GPU Architectures

Semih Akkurt, Sébastien Lemaire, Paul Bartholomew, Sylvain Laizet

View

Tags: Computational Physics, Computer science, CUDA, nVidia, nVidia A100, nVidia GH200, nVidia H100, nVidia V100, OpenMP, PDEs

November 24, 2024 by hgpu

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

Bertil Schmidt, Felix Kallenborn, Alexander Wichmann, Alejandro Chacon, Christian Hundt

View

Tags: Bioinformatics, Biology, Computer science, CUDA, FPGA, Genomics, nVidia, nVidia A100, nVidia H100, nVidia L4, nVidia L40s, nVidia V100, Package

November 24, 2024 by hgpu

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

Giulio Malenza, Valentina Cesare, Marco Edoardo Santimaria, Robert Birke, Alberto Vecchiato, Ugo Becciani, Marco Aldinucci

View

Tags: AMD Radeon Instinct MI250X, Astrophysics, ATI, Computer science, CUDA, HIP, HPC, nVidia, nVidia A100, nVidia H100, nVidia V100, OpenMP, Package, Performance, performance portability, SYCL, Tesla T4

November 24, 2024 by hgpu

GPUVM: GPU-driven Unified Virtual Memory

Nurlan Nazaraliyev, Elaheh Sadredini, Nael Abu-Ghazaleh

View

Tags: Computer science, CUDA, Memory, nVidia, Operating systems, Performance, Tesla V100

November 17, 2024 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

View

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

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Nathaniel Morgan, Caleb Yenusah, Adrian Diaz, Daniel Dunning,Jacob Moore,Erin Heilman, Calvin Roth, Evan Lieberman, Steven Walton, Sarah Brown, Daniel Holladay, Marko Knezevic, Gavin Whetstone, Zachary Baker, Robert Robey

View

Tags: Computer science, CUDA, Fortran, HIP, nVidia, Package, performance portability, Pthreads

November 10, 2024 by hgpu

ViVid Project

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

VFLog: vertical + gpu + free join + datalog

Column-Oriented Datalog on the GPU

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

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

GSParLib

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

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

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

KSig: GPU-accelerated computation of popular time series kernels

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

IterApp: Iterative Application Skeleton

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

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

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

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

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

Anari-visionaray

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

See all packages

* * *

* * *

HGPU group © 2010-2025 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: