high performance computing on graphics processing units: hgpu.org

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Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code

Guillermo Vigueras, Manuel Carro, Salvador Tamarit, Julio Marino

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Tags: Computer science, Heterogeneous systems, Machine learning, OpenCL, Programming Languages

March 15, 2016 by hgpu

A portable platform for accelerated PIC codes and its application to GPUs using OpenACC

F. Hariri, T.M. Tran, A. Jocksch, E. Lanti, J. Progsch, P. Messmer, S. Brunner, G. Gheller, L. Villard

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Tags: Computational Physics, Heterogeneous systems, nVidia, OpenACC, Particle-in-cell methods, Physics, Plasma physics, Tesla K20

March 12, 2016 by hgpu

Automatic Data Layout Generation and Kernel Mapping for CPU+GPU Architectures

Deepak Majeti, Kuldeep S. Meel, Rajkishore Barik, Vivek Sarkar

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Tags: Algorithms, Compilers, Computer science, Heterogeneous systems, nVidia, OpenCL, Programming techniques, Tesla M2050

March 10, 2016 by hgpu

Hadoop Mapreduce OpenCL Plugin

Vivek Viswanathan

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Tags: AMD Radeon R9 290X, ATI, Computer science, Heterogeneous systems, Java, MapReduce, OpenCL

March 3, 2016 by hgpu

DIANNE: Distributed Artificial Neural Networks for the Internet of Things

Elias De Coninck, Tim Verbelen, Bert Vankeirsbilck, Steven Bohez, Sam Leroux, Pieter Simoens

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Source codes

Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Java, Neural networks, nVidia, nVidia GeForce GTX 980, nVidia Jetson TK1, Package

February 25, 2016 by hgpu

Parallel Approaches to Shortest-Path Problems for Multilevel Heterogeneous Computing

Hector Ortega Arranz

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, Path problems, Thesis

February 25, 2016 by hgpu

Automatic Command Queue Scheduling for Task-Parallel Workloads in OpenCL

Ashwin M. Aji, Antonio J. Pena, Pavan Balaji, Wu-chun Feng

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Tags: Computer science, Heterogeneous systems, nVidia, OpenCL, Performance, Seismology, Task scheduling, Tesla C2050

February 23, 2016 by hgpu

VirtCL: a framework for OpenCL device abstraction and management

Yi-Ping You, Hen-Jung Wu, Yeh-Ning Tsai, Yen-Ting Chao

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Tags: Computer science, Heterogeneous systems, Memory model, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 590, OpenCL, Performance

February 23, 2016 by hgpu

HeSP: a simulation framework for solving the task scheduling-partitioning problem on heterogeneous architectures

Anton Rey, Francisco D. Igual, Manuel Prieto-Matias

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Tags: Algorithms, ARM, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 980, OpenCL, Task scheduling

February 19, 2016 by hgpu

Automatic and portable mapping of data parallel programs to OpenCL for GPU-based heterogeneous systems

Zheng Wang, Dominik Grewe, Michael F.P. O'Boyle

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Tags: ATI, ATI Radeon HD 7970, Code generation, Compilers, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 580, OpenCL, OpenMP, Performance

February 19, 2016 by hgpu

SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures

Alexandros Koliousis, Matthias Weidlich, Raul Castro Fernandez, Alexander L. Wolf, Paolo Costa, Peter Pietzuch

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Tags: Computer science, Databases, Heterogeneous systems, nVidia, nVidia Quadro K5200, OpenCL

February 16, 2016 by hgpu

Towards Improving Programmability of Heterogeneous Parallel Architectures

Michele Scandale

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Tags: Compilers, Computer science, Heterogeneous systems, LLVM, OpenCL, Thesis

February 16, 2016 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

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high performance computing on graphics processing units: hgpu.org

Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code

A portable platform for accelerated PIC codes and its application to GPUs using OpenACC

Automatic Data Layout Generation and Kernel Mapping for CPU+GPU Architectures

Hadoop Mapreduce OpenCL Plugin

DIANNE: Distributed Artificial Neural Networks for the Internet of Things

Parallel Approaches to Shortest-Path Problems for Multilevel Heterogeneous Computing

Automatic Command Queue Scheduling for Task-Parallel Workloads in OpenCL

VirtCL: a framework for OpenCL device abstraction and management

HeSP: a simulation framework for solving the task scheduling-partitioning problem on heterogeneous architectures

Automatic and portable mapping of data parallel programs to OpenCL for GPU-based heterogeneous systems

SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures

Towards Improving Programmability of Heterogeneous Parallel Architectures

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)