high performance computing on graphics processing units: hgpu.org

hgpu.org » Tesla T4

Exploring Scalability in C++ Parallel STL Implementations

Ruben Laso, Diego Krupitza, Sascha Hunold

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Ampere A2, OpenMP, Package, Performance, Tesla T4

September 1, 2024 by hgpu

Data-driven Forecasting of Deep Learning Performance on GPUs

Seonho Lee, Amar Phanishayee, Divya Mahajan

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, nVidia, nVidia A100, nVidia H100, nVidia P100, nVidia V100, Performance, PyTorch, Tesla T4

August 4, 2024 by hgpu

Helix: Distributed Serving of Large Language Models via Max-Flow on Heterogeneous GPUs

Yixuan Mei, Yonghao Zhuang, Xupeng Miao, Juncheng Yang, Zhihao Jia, Rashmi Vinayak

View

Download (PDF)

Tags: Computer science, GPU cluster, Heterogeneous systems, nVidia, nVidia A100, nVidia V100, Tesla T4

June 9, 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

View

Download (PDF)

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

June 2, 2024 by hgpu

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

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

View

Download (PDF)

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

May 26, 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

View

Download (PDF)

Source codes

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

May 12, 2024 by hgpu

Using AI libraries for Incompressible Computational Fluid Dynamics

Boyang Chen, Claire E. Heaney, Christopher C. Pain

View

Download (PDF)

Tags: AI, Artificial intelligence, cfd, CUDA, Fluid dynamics, Neural networks, nVidia, Partial differential equations, PDEs, Tesla T4

March 3, 2024 by hgpu

UniFL: Accelerating Federated Learning Using Heterogeneous Hardware Under a Unified Framework

Biyao Che, Zixiao Wang, Ying Chen, Liang Guo, Yuan Liu, Yuan Tian, Jizhuang Zhao

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, FPGA, nVidia, OpenCL, Security, Tesla T4

January 7, 2024 by hgpu

Comparative Performance and Scalability Analysis of GPU-accelerated Database Operations

Carl Andersson, Jonathan Nilsson

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, Databases, nVidia, nVidia A10, nVidia M60, Performance, Tesla T4, Thesis

December 24, 2023 by hgpu

Predicting the Execution Time of a kernel on a specific GPU using PTX code

Monika Dagar, Jorge Roldan

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1650, nVidia GeForce GTX Titan XP, Performance, PTX, Tesla K20, Tesla K80, Tesla M60, Tesla P100, Tesla T4, Tesla V100

October 22, 2023 by hgpu

A Model Extraction Attack on Deep Neural Networks Running on GPUs

Jonah G. O'Brien Weiss

View

Download (PDF)

Tags: Computer science, Neural networks, nVidia, nVidia Quadro RTX 8000, Security, Tesla T4

August 13, 2023 by hgpu

Anatomy of High-Performance GEMM with Online Fault Tolerance on GPUs

Shixun Wu, Yujia Zhai, Jinyang Liu, Jiajun Huang, Zizhe Jian, Bryan M. Wong, Zizhong Chen

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, CUDA, GEMM, Linear Algebra, Matrix multiplication, nVidia, nVidia A100, Package, Performance, Reliability, Tesla T4

May 7, 2023 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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Exploring Scalability in C++ Parallel STL Implementations

Data-driven Forecasting of Deep Learning Performance on GPUs

Helix: Distributed Serving of Large Language Models via Max-Flow on Heterogeneous GPUs

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

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

Using AI libraries for Incompressible Computational Fluid Dynamics

UniFL: Accelerating Federated Learning Using Heterogeneous Hardware Under a Unified Framework

Comparative Performance and Scalability Analysis of GPU-accelerated Database Operations

Predicting the Execution Time of a kernel on a specific GPU using PTX code

A Model Extraction Attack on Deep Neural Networks Running on GPUs

Anatomy of High-Performance GEMM with Online Fault Tolerance on GPUs

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

Most viewed papers (last 30 days)