high performance computing on graphics processing units: hgpu.org

hgpu.org » Applications

CRAC: Checkpoint-Restart Architecture for CUDA with Streams and UVM

Twinkle Jain, Gene Cooperman

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, HPC, nVidia, nVidia Quadro K600, Package, Tesla V100

August 30, 2020 by hgpu

8 Steps to 3.7 TFLOP/s on NVIDIA V100 GPU: Roofline Analysis and Other Tricks

Charlene Yang

View

Download (PDF)

Source codes

Tags: Computer science, HPC, nVidia, OpenACC, Optimization, Package, Performance, Tesla V100

August 30, 2020 by hgpu

Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs

Cade Brown, Ahmad Abdelfattah, Stanimire Tomov, Jack Dongarra

View

Download (PDF)

Tags: AMD, AMD Radeon Instinct MI25, AMD Radeon Instinct Mi50, Benchmarking, Computer science, Heterogeneous systems, HIP, HPC, Linear Algebra, Performance, Portability

August 23, 2020 by hgpu

Compiler-Based Tools to Aid in Data Transfer Optimization and On-Chip Debug of Heterogeneous Compute Systems

Matthew B. Ashcraft

View

Download (PDF)

Tags: Compilers, Computer science, FPGA, Heterogeneous systems, HLS, nVidia, nVidia GeForce GTX Titan X, OpenCL, Thesis

August 23, 2020 by hgpu

Modular FPGA Systems with Support for Dynamic Workloads and Virtualisation

Anuj A Vaishnav

View

Download (PDF)

Tags: Computer science, FPGA, OpenCL, Thesis, Virtualization

August 23, 2020 by hgpu

AIPerf: Automated machine learning as an AI-HPC benchmark

Zhixiang Ren, Yongheng Liu, Tianhui Shi, Lei Xie, Yue Zhou, Jidong Zhai, Youhui Zhang, Yunquan Zhang, Wenguang Chen

View

Download (PDF)

Source codes

Tags: Algorithms, Artificial intelligence, Benchmarking, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 1080 Ti, Package, Tesla V100

August 23, 2020 by hgpu

Evaluating the Performance of NVIDIA’s A100 Ampere GPU for Sparse Linear Algebra Computations

Yuhsiang Mike Tsai, Terry Cojean, Hartwig Anzt

View

Download (PDF)

Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia A100, Performance, Sparse, Sparse matrix

August 23, 2020 by hgpu

Heterogeneous parallel computing for image registration and linear algebra applications

Orestis Zachariadis

View

Download (PDF)

Source codes

Tags: CUDA, Heterogeneous systems, Image processing, Image registration, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 1050, nVidia GeForce RTX 2070, Package, Sparse matrix, Thesis

August 9, 2020 by hgpu

Ignite-GPU: a GPU-enabled in-memory computing architecture on clusters

Amir Hossein Sojoodi, Majid Salimi Beni, Farshad Khunjush

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 680, Package

August 9, 2020 by hgpu

Parallel acceleration of CPU and GPU range queries over large data sets

Mitchell Nelson, Zachary Sorenson, Joseph M. Myre, Jason Sawin, David Chiu

View

Download (PDF)

Tags: Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080

August 9, 2020 by hgpu

PERCH 2.0: Fast and Accurate GPU-based Perception via Search for Object Pose Estimation

Aditya Agarwal, Yupeng Han, Maxim Likhachev

View

Download (PDF)

Source codes

Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 1070, Package, Robotics, Tesla P100, Tesla V100

August 9, 2020 by hgpu

AnyHLS: High-Level Synthesis with Partial Evaluation

M. Akif Özkan, Arsène Pérard-Gayot, Richard Membarth, Philipp Slusallek, Roland Leissa, Sebastian Hack, Jürgen Teich, Frank Hannig

View

Download (PDF)

Source codes

Tags: Computer science, FPGA, HLS, Image processing, OpenCL, Package, Programming Languages

August 9, 2020 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

Applications

CRAC: Checkpoint-Restart Architecture for CUDA with Streams and UVM

8 Steps to 3.7 TFLOP/s on NVIDIA V100 GPU: Roofline Analysis and Other Tricks

Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs

Compiler-Based Tools to Aid in Data Transfer Optimization and On-Chip Debug of Heterogeneous Compute Systems

Modular FPGA Systems with Support for Dynamic Workloads and Virtualisation

AIPerf: Automated machine learning as an AI-HPC benchmark

Evaluating the Performance of NVIDIA’s A100 Ampere GPU for Sparse Linear Algebra Computations

Heterogeneous parallel computing for image registration and linear algebra applications

Ignite-GPU: a GPU-enabled in-memory computing architecture on clusters

Parallel acceleration of CPU and GPU range queries over large data sets

PERCH 2.0: Fast and Accurate GPU-based Perception via Search for Object Pose Estimation

AnyHLS: High-Level Synthesis with Partial Evaluation

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)