high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia

CL-VIS: Visualization Platform for Understanding and Checking the OpenCL Programs

SeongKi Kim, HyukSoo Han

View

Download (PDF)

Tags: Computer science, nVidia, nVidia GeForce GTX 960 M, OpenCL, Visualization

November 10, 2019 by hgpu

KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

Alexander Brandt, Davood Mohajerani, Marc Moreno Maza, Jeeva Paudel, Linxiao Wang

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, LLVM, nVidia, Package, Performance

November 10, 2019 by hgpu

Accelerating Stochastic Simulations on GPUs Using OpenCL

Pilsung Kang

View

Download (PDF)

Tags: Heterogeneous systems, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Physics, Stochastic simulation

November 10, 2019 by hgpu

Code Optimization on GPUs

Changwan Hong

View

Download (PDF)

Tags: Chemistry, Code generation, Computational chemistry, Computer science, CUDA, Intel Xeon Phi, Matrix multiplication, nVidia, Optimization, Sparse matrix, Tesla P100, Thesis

November 3, 2019 by hgpu

Implementing and evaluating an heterogeneous, scalable, tridiagonal linear system solver with OpenCL to target FPGAs, GPUs, and CPUs

Hamish J. Macintosh, Jasmine E. Banks, Neil A. Kelson

View

Download (PDF)

Tags: Computer science, FPGA, Linear Algebra, nVidia, nVidia Quadro M 4000, OpenCL

November 3, 2019 by hgpu

Performance Evaluation of Advanced Features in CUDA Unified Memory

Steven W. D. Chien, Ivy B. Peng, Stefano Markidis

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 1050, Package, Prefetch, Tesla V100

October 27, 2019 by hgpu

SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs

Mohammed Alser, Taha Shahroodi, Juan Gomez-Luna, Can Alkan, Onur Mutlu

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, Package, Sequence alignment

October 27, 2019 by hgpu

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

Filip Petrovič, David Střelák, Jana Hozzová, Jaroslav Oľha, Richard Trembecký, Siegfried Benkner, Jiří Filipovič

View

Download (PDF)

Source codes

Tags: AMD Radeon RX Vega 56, Auto-Tuning, Benchmarking, Computer science, CUDA, Electron microscopy, Intel Xeon Phi, Microscopy, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 750, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Performance, performance portability, Tesla K20

October 27, 2019 by hgpu

AI Benchmark: All About Deep Learning on Smartphones in 2019

Andrey Ignatov, Radu Timofte, Andrei Kulik, Seungsoo Yang, Ke Wang, Felix Baum, Max Wu, Lirong Xu, Luc Van Gool

View

Download (PDF)

Tags: Android, Benchmarking, Computer science, CUDA, Deep learning, nVidia, OpenCL, Overview

October 20, 2019 by hgpu

Blink: Fast and Generic Collectives for Distributed ML

Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil Devanur, Ion Stoica

View

Download (PDF)

Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, Machine learning, nVidia, nVidia DGX-1, Tesla V100

October 20, 2019 by hgpu

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

Ilia Sivkov, Alfio Lazzaro, Jurg Hutter

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Data mining, Linear Algebra, Machine learning, Matrix multiplication, nVidia, Package, Signal processing, Sparse matrix, Tesla P100

October 20, 2019 by hgpu

The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Hamid Reza Zohouri, Satoshi Matsuoka

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, FPGA, HLS, Memory, nVidia, OpenCL, Package, Tesla K20, Tesla V100

October 20, 2019 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

CL-VIS: Visualization Platform for Understanding and Checking the OpenCL Programs

KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

Accelerating Stochastic Simulations on GPUs Using OpenCL

Code Optimization on GPUs

Implementing and evaluating an heterogeneous, scalable, tridiagonal linear system solver with OpenCL to target FPGAs, GPUs, and CPUs

Performance Evaluation of Advanced Features in CUDA Unified Memory

SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

AI Benchmark: All About Deep Learning on Smartphones in 2019

Blink: Fast and Generic Collectives for Distributed ML

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

Most viewed papers (last 30 days)