high performance computing on graphics processing units: hgpu.org

hgpu.org » Tesla V100

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

Christopher Noel Hesse

View

Tags: Benchmarking, Computer science, Deep learning, FPGA, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce RTX 2070, nVidia Jetson Nano, Performance, Tesla V100

December 5, 2021 by hgpu

Atos: A Task-Parallel GPU Dynamic Scheduling Framework for Dynamic Irregular Computations

Yuxin Chen, Benjamin Brock, Serban Porumbescu, Aydın Buluç, Katherine Yelick, John D. Owens

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, Task scheduling, Tesla V100

December 5, 2021 by hgpu

HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

Ji Liu, Zhihua Wu, Dianhai Yu, Yanjun Ma, Danlei Feng, Minxu Zhang, Xinxuan Wu, Xuefeng Yao, Dejing Dou

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, Distributed computing, Heterogeneous systems, Neural networks, nVidia, Package, Tesla V100

November 28, 2021 by hgpu

BAT: A Benchmark suite for AutoTuners

Ingunn Sund, Knut A. Kirkhorn, Jacob O. Tørring, Anne C. Elster

View

Download (PDF)

Source codes

Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 980, Package, Performance, Tesla T4, Tesla V100

November 21, 2021 by hgpu

General purpose lattice QCD code set Bridge++ 2.0 for high performance computing

Yutaro Akahoshi, Sinya Aoki, Tatsumi Aoyama, Issaku Kanamori, Kazuyuki Kanaya, Hideo Matsufuru, Yusuke Namekawa, Hidekatsu Nemura, Yusuke Taniguchi

View

Download (PDF)

Source codes

Tags: GPU cluster, HEP, High Energy Physics - Lattice, Numerical simulation, nVidia, OpenACC, Package, Physics, QCD, Tesla V100

November 14, 2021 by hgpu

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

Zane Fink, Simeng Liu, Jaemin Choi, Matthias Diener, Laxmikant V. Kale

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Distributed computing, HPC, Machine learning, MPI, nVidia, Package, Performance, Python, Tesla V100

November 14, 2021 by hgpu

Collage: Automated Integration of Deep Learning Backends

Byungsoo Jeon, Sunghyun Park, Peiyuan Liao, Sheng Xu, Tianqi Chen, Zhihao Jia

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 2070, Tesla V100

November 7, 2021 by hgpu

Principles towards Real-Time Simulation of Material Point Method on Modern GPUs

Yun Fei, Yuhan Huang, Ming Gao

View

Download (PDF)

Tags: Computer science, CUDA, Graphics, nVidia, nVidia DGX-1, Physics-based simulation, Tesla V100

November 7, 2021 by hgpu

Mixed precision in Graphics Processing Unit

Quentin Gallouédec

View

Download (PDF)

Tags: Computer science, Machine learning, Mixed precision, Neural networks, nVidia, Tesla V100

October 31, 2021 by hgpu

The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding

Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry

View

Download (PDF)

Tags: Compilers, Computer science, CUDA, Deep learning, nVidia, Tesla V100

October 24, 2021 by hgpu

Monitoring Collective Communication Among GPUs

Muhammet Abdullah Soyturk, Palwisha Akhtar, Erhan Tezcan, Didem Unat

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, MPI, nVidia, Package, Performance, Tesla V100

October 24, 2021 by hgpu

Principal Kernel Analysis: A Tractable Methodology to Simulate Scaled GPU Workloads

Cesar A. Baddouh, Mahmoud Khairy, Roland Green, Mathias Payer, Timothy G. Rogers

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, Performance, Tesla V100

October 24, 2021 by hgpu

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

Atos: A Task-Parallel GPU Dynamic Scheduling Framework for Dynamic Irregular Computations

HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

BAT: A Benchmark suite for AutoTuners

General purpose lattice QCD code set Bridge++ 2.0 for high performance computing

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

Collage: Automated Integration of Deep Learning Backends

Principles towards Real-Time Simulation of Material Point Method on Modern GPUs

Mixed precision in Graphics Processing Unit

The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding

Monitoring Collective Communication Among GPUs

Principal Kernel Analysis: A Tractable Methodology to Simulate Scaled GPU Workloads

Recent source codes

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Most viewed papers (last 30 days)