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CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU

Sijun Tan, Brian Knott, Yuan Tian, David J. Wu

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Tags: Computer science, CUDA, Deep learning, Linear Algebra, Machine learning, Neural networks, nVidia, Package, PyTorch, Security, Tesla V100

April 25, 2021 by hgpu

Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

Hamid Tabani, Fabio Mazzocchetti, Pedro Benedicte, Jaume Abella, Francisco J. Cazorla

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson TX2, Performance

April 25, 2021 by hgpu

SLATE port to AMD and Intel platforms

Ahmad Abdelfattah, Mohammed Al Farhan, Cade Brown, Mark Gates, Dalal Sukkari, Asim YarKhan, Jack Dongarra

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, OpenCL, Package, SYCL

April 18, 2021 by hgpu

Under the Hood of SYCL – An Initial Performance Analysis With an Unstructured-mesh CFD Application

Istvan Z. Reguly, Andrew M.B. Owenson, Archie Powell, Stephen A. Jarvis, Gihan R. Mudalige

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Tags: Benchmarking, cfd, CUDA, Fluid dynamics, MPI, nVidia, OpenMP, Package, SYCL

April 18, 2021 by hgpu

Efficient Large-Scale Language Model Training on GPU Clusters

Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia

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Tags: Computer science, CUDA, Data parallelism, Deep learning, GPU cluster, NLP, nVidia, nVidia DGX-A100, Package

April 18, 2021 by hgpu

A Hybrid Parallelization Approach for Distributed and Scalable Deep Learning

Samson B. Akintoye, Liangxiu Han, Xin Zhang, Haoming Chen, Daoqiang Zhang

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Tags: Computer science, CUDA, Data parallelism, Deep learning, Neural networks, nVidia, Tesla V100

April 18, 2021 by hgpu

Multiple-Tasks on Multiple-Devices (MTMD): Exploiting Concurrency in Heterogeneous Managed Runtimes

Michail Papadimitriou, Eleni Markou, Juan Fumero, Athanasios Stratikopoulos, Florin Blanaru, Christos Kotselidis

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Tags: Computer science, Heterogeneous systems, Java, Machine learning, nVidia, nVidia GeForce GTX 1650, OpenCL

April 11, 2021 by hgpu

Progressive Semantic Segmentation

Chuong Huynh, Anh Tran, Khoa Luu, Minh Hoai

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Tags: Computer vision, Image processing, nVidia, Tesla V100

April 11, 2021 by hgpu

Large Scale GPU Based Simulations of Turbulent Bubbly Flow in a Square Duct

Purushotam Kumar, Surya Pratap Vanka

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Tags: cfd, CUDA, Fluid dynamics, MPI, nVidia, Tesla C2075, Tesla K20, Tesla P100

April 11, 2021 by hgpu

Efficient Video Compression via Content-Adaptive Super-Resolution

Mehrdad Khani, Vibhaalakshmi Sivaraman, Mohammad Alizadeh

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Tags: Compression, Computer science, Deep learning, Neural networks, nVidia, Package, Tesla V100

April 11, 2021 by hgpu

An Investigation of Atomic Synchronization for Sort-Based Group-By Aggregation on GPUs

Bala Gurumurthy, David Broneske, Martin Schaler, Thilo Pionteck, Gunter Saake

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Tags: Computer science, Databases, Hashing, Heterogeneous systems, nVidia, nVidia GeForce GTX 1050 Ti, nVidia GeForce RTX 2080 Ti, OpenCL, Sorting

April 5, 2021 by hgpu

Parallel Arbitrary-precision Integer Arithmetic

Davood Mohajerani

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Tags: Algorithms, Computer science, CUDA, nVidia, OpenCL, Package, Thesis

April 5, 2021 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU

Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

SLATE port to AMD and Intel platforms

Under the Hood of SYCL – An Initial Performance Analysis With an Unstructured-mesh CFD Application

Efficient Large-Scale Language Model Training on GPU Clusters

A Hybrid Parallelization Approach for Distributed and Scalable Deep Learning

Multiple-Tasks on Multiple-Devices (MTMD): Exploiting Concurrency in Heterogeneous Managed Runtimes

Progressive Semantic Segmentation

Large Scale GPU Based Simulations of Turbulent Bubbly Flow in a Square Duct

Efficient Video Compression via Content-Adaptive Super-Resolution

An Investigation of Atomic Synchronization for Sort-Based Group-By Aggregation on GPUs

Parallel Arbitrary-precision Integer Arithmetic

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

Most viewed papers (last 30 days)