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Gaining Cross-Platform Parallelism for HAL’s Molecular Dynamics Package using SYCL

Viktor Skoblin, Felix Höfling, Steffen Christgau

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Tags: AMD Radeon Instinct MI210, ATI, Computer science, CUDA, Molecular dynamics, nVidia, nVidia A100, nVidia A40, Package, Physics, SYCL

June 9, 2024 by hgpu

Addressing Challenges in Utilizing GPUs for Accelerating Privacy-Preserving Computation

Ardhi Wiratama Baskara Yudha

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, Security, Thesis

June 2, 2024 by hgpu

Evaluation of computational and energy performance in matrix multiplication algorithms on CPU and GPU using MKL, cuBLAS and SYCL

L.A. Torres, Carlos J. Barrios H, Yves Denneulin

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia A100, Package, Performance, SYCL

June 2, 2024 by hgpu

An implementation of tensor product patch smoothers on GPU

Cu Cui, Paul Grosse-Bley, Guido Kanschat, Robert Strzodka

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Tags: CUDA, FEM, Finite element method, Mathematics, Numerical Analysis, nVidia, nVidia A100

June 2, 2024 by hgpu

A Survey of Cloud-Based GPU Threats and Their Impact on AI, HPC, and Cloud Computing

Numaan Huq, Philippe Lin, Roel Reyes, Charles Perine

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Tags: AMD Radeon Pro V520, Artificial intelligence, ATI, Cloud, Computer science, CUDA, Deep learning, nVidia, OpenCL, Security, Tesla T4

June 2, 2024 by hgpu

GPU Implementations for Midsize Integer Addition and Multiplication

Cosmin E. Oancea, Stephen M. Watt

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia A100, Performance, Programming Languages

May 26, 2024 by hgpu

STuning-DL: Model-Driven Autotuning of Sparse GPU Kernels for Deep Learning

Roberto L. Castro, Diego Andrade, Basilio B. Fraguela

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia A100, Tesla T4

May 26, 2024 by hgpu

Kernel-Centric Optimizations for Deep Neural Networks on GPGPU

Zhaodong Chen

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia A100, nVidia V100, Thesis

May 26, 2024 by hgpu

Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach

Urvij Saroliya, Eishi Arima, Dai Liu, Martin Schulz

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia A100, PC cluster, Task scheduling

May 20, 2024 by hgpu

CuPBoP: Making CUDA a Portable Language

Ruobing Han, Jun Chen, Bhanu Garg, Xule Zhou, John Lu, Jeffrey Young, Jaewoong Sim, Hyesoon Kim

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Tags: Computer science, CUDA, HIP, nVidia, nVidia A30, OpenCL, Package, Portability, SYCL

May 12, 2024 by hgpu

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

Ruixin Wang, Minghai Lu, Cody Hao Yu, Yi-Hsiang Lai, Tianyi Zhang

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Tags: Computer science, CUDA, Deep learning, DSL, nVidia, Package, Performance, PyTorch, Tesla T4

May 12, 2024 by hgpu

Optimizing Hardware Resource Partitioning and Job Allocations on Modern GPUs under Power Caps

Eishi Arima, Minjoon Kang, Issa Saba, Josef Weidendorfer, Carsten Trinitis, Martin Schulz

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Tags: Computer science, CUDA, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia A100, Performance

May 12, 2024 by hgpu

GPU-Smash: Compression abstraction library

A Parallel Compression Pipeline for Improving GPU Virtualization Data Transfers

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

pSTL-Bench: Micro-benchmark suite designed to evaluate the scalability and efficiency of parallel C++ STL implementations

Exploring Scalability in C++ Parallel STL Implementations

Noarr Structures

Abstractions for C++ code optimizations in parallel high-performance applications

$3D-fractal-generators$

HiCCL: A hierarchical collective communications library with portable optimizations

HiCCL: A Hierarchical Collective Communication Library

GRAFX: An Open-Source Library for Audio Processing Graphs in PyTorch

In-browser Inference Measurement

Anatomizing Deep Learning Inference in Web Browsers

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high performance computing on graphics processing units: hgpu.org

Gaining Cross-Platform Parallelism for HAL’s Molecular Dynamics Package using SYCL

Addressing Challenges in Utilizing GPUs for Accelerating Privacy-Preserving Computation

Evaluation of computational and energy performance in matrix multiplication algorithms on CPU and GPU using MKL, cuBLAS and SYCL

An implementation of tensor product patch smoothers on GPU

GPU Implementations for Midsize Integer Addition and Multiplication

STuning-DL: Model-Driven Autotuning of Sparse GPU Kernels for Deep Learning

Kernel-Centric Optimizations for Deep Neural Networks on GPGPU

Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach

CuPBoP: Making CUDA a Portable Language

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

Optimizing Hardware Resource Partitioning and Job Allocations on Modern GPUs under Power Caps

Recent source codes

GPU-Smash: Compression abstraction library

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

pSTL-Bench: Micro-benchmark suite designed to evaluate the scalability and efficiency of parallel C++ STL implementations

Noarr Structures

3D-fractal-generators

SyncPerformance

VerCors: a verification toolset for verifying parallel and concurrent software

HiCCL: A hierarchical collective communications library with portable optimizations

GRAFX: An Open-Source Library for Audio Processing Graphs in PyTorch

In-browser Inference Measurement

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