high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia

A Tensor Compiler for Unified Machine Learning Prediction Serving

Supun Nakandala Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Machine learning, nVidia, Package, Tesla K80, Tesla P100

October 18, 2020 by hgpu

Mastering Atari with Discrete World Models

Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, Jimmy Ba

View

Download (PDF)

Source codes

Tags: Artificial intelligence, Computer science, Deep learning, Machine learning, nVidia, Package, Python, TensorFlow, Tesla V100

October 11, 2020 by hgpu

Efficient Inference For Neural Machine Translation

Yi-Te Hsu, Sarthak Garg, Yi-Hsiu Liao, Ilya Chatsviorkin

View

Download (PDF)

Tags: Computer science, NLP, nVidia, Tesla V100

October 11, 2020 by hgpu

Transparent Acceleration of Java-based Deep Learning Engines

Athanasios Stratikopoulos, Mihai-Cristian Olteanu, Ian Vaughan, Zoran Sevarac, Nikos Foutris, Juan Fumero, Christos Kotselidis

View

Download (PDF)

Source codes

Tags: AMD RX Vega 64, ATI, Computer science, Computer vision, Deep learning, Java, nVidia, nVidia GeForce GTX 1050 Ti, OpenCL, Package

October 4, 2020 by hgpu

Flexible Performant GEMM Kernels on GPUs

Thomas Faingnaert, Tim Besard, Bjorn De Sutter

View

Download (PDF)

Source codes

Tags: Computer science, CUBLAS, CUDA, Julia, Machine learning, Mathematical Software, Matrix multiplication, Mixed precision, nVidia, nVidia GeForce RTX 2080 Ti, Package, Performance

October 4, 2020 by hgpu

Accelerating Sparse Matrix-Matrix Multiplication with GPU Tensor Cores

Orestis Zachariadis, Nitin Satpute, Juan Gómez-Luna, Joaquín Olivares

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, Matrix multiplication, Mixed precision, nVidia, nVidia GeForce RTX 2070, nVidia Titan RTX, Package, Performance, Sparse matrix

October 4, 2020 by hgpu

Hybrid MPI and CUDA Parallelization for CFD Applications on Multi-GPU HPC Clusters

Jianqi Lai, Hang Yu, Zhengyu Tian, Hua Li

View

Download (PDF)

Tags: cfd, CUDA, Fluid dynamics, GPU cluster, HPC, MPI, nVidia, nVidia GeForce GTX 1070, OpenMP, Tesla V100

September 27, 2020 by hgpu

Performance Evaluation of Mixed Precision Algorithms for Solving Sparse Linear Systems

Mawussi Zounon, Nicholas J. Higham, Craig Lucas, Françoise Tisseur

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Factorization, Mixed precision, nVidia, Tesla P100, Tesla V100

September 27, 2020 by hgpu

Designing Efficient Barriers and Semaphores for Graphics Processing Units

Rohan Mahapatra

View

Download (PDF)

Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan V, nVidia GeForce RTX 2080 Ti, Thesis

September 20, 2020 by hgpu

A Comparison of Optimal Scanline Voxelization Algorithms

Tim Håkansson

View

Download (PDF)

Source codes

Tags: 3D Graphics and Realism, CUDA, GLSL, nVidia, OpenGL, Package, Tesla T4, Thesis, Voxelization

September 20, 2020 by hgpu

WarpCore: A Library for fast Hash Tables on GPUs

Daniel Jünger, Robin Kobus, André Müller, Christian Hundt, Kai Xu, Weiguo Liu, Bertil Schmidt

View

Download (PDF)

Tags: Bioinformatics, Computer science, CUDA, Hashing, nVidia, nVidia Quadro GV100, Tesla V100

September 20, 2020 by hgpu

PySchedCL: Leveraging Concurrency in Heterogeneous Data-Parallel Systems

Anirban Ghose, Siddharth Singh, Vivek Kulaharia, Lokesh Dokara, Srijeeta Maity, Soumyajit Dey

View

Download (PDF)

Source codes

Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 970, OpenCL, Package

September 20, 2020 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

* * *

high performance computing on graphics processing units: hgpu.org

A Tensor Compiler for Unified Machine Learning Prediction Serving

Mastering Atari with Discrete World Models

Efficient Inference For Neural Machine Translation

Transparent Acceleration of Java-based Deep Learning Engines

Flexible Performant GEMM Kernels on GPUs

Accelerating Sparse Matrix-Matrix Multiplication with GPU Tensor Cores

Hybrid MPI and CUDA Parallelization for CFD Applications on Multi-GPU HPC Clusters

Performance Evaluation of Mixed Precision Algorithms for Solving Sparse Linear Systems

Designing Efficient Barriers and Semaphores for Graphics Processing Units

A Comparison of Optimal Scanline Voxelization Algorithms

WarpCore: A Library for fast Hash Tables on GPUs

PySchedCL: Leveraging Concurrency in Heterogeneous Data-Parallel Systems

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)