high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

Noise Removal from Remote Sensed Images by NonLocal Means with OpenCL Algorithm

Donatella Granata, Angelo Palombo, Federico Santini, Umberto Amato

View

Download (PDF)

Tags: Algorithms, Heterogeneous systems, nVidia, nVidia GeForce GTX 1650, OpenCL, Sensing, Signal processing

February 2, 2020 by hgpu

Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems – a Case Study

Lukas Sommer, Florian Stock, Leonardo Solis-Vasquez, Andreas Koch

View

Download (PDF)

Tags: Automotive, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson TX2, OpenCL, OpenMP

January 26, 2020 by hgpu

Hardware/Software Co-Design for Data-Intensive Genomics Workloads

Nicola Cadenelli

View

Download (PDF)

Tags: Algorithms, Computer science, FPGA, nVidia, OpenCL, performance portability, Tesla K40, Thesis

January 26, 2020 by hgpu

Automatically Harnessing Sparse Acceleration

Philip Ginsbach, Bruce Collie, Michael F.P. O'Boyle

View

Download (PDF)

Tags: AMD Radeon R7, ATI, Computer science, CUDA, Fortran, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance

January 26, 2020 by hgpu

Efficient Radial Pattern Keyword Search on Knowledge Graphs in Parallel

Yueji Yang, Anthony K. H. Tung

View

Download (PDF)

Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 1080 Ti, OpenMP

January 26, 2020 by hgpu

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

Xiaolong Ma, Wei Niu, Tianyun Zhang, Sijia Liu, Fu-Ming Guo, Sheng Lin, Hongjia Li, Xiang Chen, Jian Tang, Kaisheng Ma, Bin Ren, Yanzhi Wang

View

Download (PDF)

Source codes

Tags: ARM, Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 2080 Ti, OpenCL, Package

January 26, 2020 by hgpu

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

Ari Rasch, Richard Schulze, Sergei Gorlatch

View

Download (PDF)

Source codes

Tags: Code generation, Compilers, Computer science, nVidia, OpenCL, Package, performance portability, Tesla V100

January 19, 2020 by hgpu

On Demand Solid Texture Synthesis Using Deep 3D Networks

Jorge Gutierrez, Julien Rabin, Bruno Galerne, Thomas Hurtut

View

Download (PDF)

Source codes

Tags: 3D Graphics and Realism, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce RTX 2080 Ti, OpenGL, Package, PyTorch

January 19, 2020 by hgpu

GPU Tensor Cores for fast Arithmetic Reductions

Cristóbal A. Navarro, Roberto Carrasco, Ricardo J. Barrientos, Javier A. Riquelme, Raimundo Vega

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, nVidia, Tesla V100, TPU

January 19, 2020 by hgpu

Hardware Implementation and Quantization of Tiny-Yolo-v2 using OpenCL

Yap June Wai, Zulkalnain bin Mohd Yussof, Sani Irwan bin Md Salim

View

Download (PDF)

Tags: Computer science, Computer vision, Deep learning, DSP, FPGA, Neural networks, OpenCL

January 19, 2020 by hgpu

Towards High Performance Java-based Deep Learning Frameworks

Athanasios Stratikopoulos, Juan Fumero, Zoran Sevarac, Christos Kotselidis

View

Download (PDF)

Source codes

Tags: Computer science, Computer vision, CUDA, Data mining, Deep learning, Heterogeneous systems, Java, Machine learning, nVidia, OpenCL, Package

January 19, 2020 by hgpu

Static Analysis and Dynamic Adaptation of Parallelism

Pierre Huchant

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, nVidia, OpenCL, Performance, Tesla K20, Tesla M2075, Thesis

January 12, 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

Applications

Noise Removal from Remote Sensed Images by NonLocal Means with OpenCL Algorithm

Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems – a Case Study

Hardware/Software Co-Design for Data-Intensive Genomics Workloads

Automatically Harnessing Sparse Acceleration

Efficient Radial Pattern Keyword Search on Knowledge Graphs in Parallel

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

On Demand Solid Texture Synthesis Using Deep 3D Networks

GPU Tensor Cores for fast Arithmetic Reductions

Hardware Implementation and Quantization of Tiny-Yolo-v2 using OpenCL

Towards High Performance Java-based Deep Learning Frameworks

Static Analysis and Dynamic Adaptation of Parallelism

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)