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hgpu.org » Heterogeneous systems

HPVM: Heterogeneous Parallel Virtual Machine

Maria Kotsifakou, Prakalp Srivastava, Matthew D. Sinclair, Rakesh Komuravelli, Vikram Adve, Sarita Adve

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Tags: Code generation, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, OpenCL, performance portability, Virtualization

March 17, 2018 by hgpu

OpenCL-ready High Speed FPGA Network for Reconfigurable High Performance Computing

Ryohei Kobayashi, Yuma Oobata, Norihisa Fujita, Yoshiki Yamaguchi, Taisuke Boku

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Tags: Algorithms, Computer science, FPGA, Heterogeneous systems, OpenCL

March 10, 2018 by hgpu

Programming Abstractions and Optimization Techniques for GPU-based Heterogeneous Systems

Lu Li

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Package, Tesla C1060, Tesla C2050, Tesla K20, Tesla M2050, Thesis

March 10, 2018 by hgpu

Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures

Manolis Loukadakis, Jose Cano, Michael O'Boyle

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Tags: ARM, Computer science, Deep learning, Heterogeneous systems, Neural networks, OpenCL

February 10, 2018 by hgpu

MiMatrix: A Massively Distributed Deep Learning Framework on a Petascale High-density Heterogeneous Cluster

Xin Chen, Hua Zhou, Yuxiang Gao, Yu Zhu, Dongyan Wang

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Tags: Algorithms, Computer science, CUDA, Deep learning, GPU cluster, Heterogeneous systems, nVidia, RDMA, Tesla P100

February 9, 2018 by hgpu

REOH: Runtime Energy Optimization for Heterogeneous Systems

Vi Ngoc-Nha Tran, Tommy Oines, Alexander Horsch, Phuong Hoai Ha

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia Quadro K620, OpenCL, Package

February 3, 2018 by hgpu

SkePU 2: Flexible and Type-Safe Skeleton Programming for Heterogeneous Parallel Systems

August Ernstsson, Christoph Kessler

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, OpenMP, Package, Tesla K20

January 28, 2018 by hgpu

Communication Architectures for Scalable GPU-centric Computing Systems

Benjamin Klenk

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K20, Thesis

January 28, 2018 by hgpu

ImageCL: Language and source-to-source compiler for performance portability, load balancing, and scalability prediction on heterogeneous systems

Thomas L. Falch, Anne C. Elster

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Tags: ATI, ATI Radeon HD 7970, Compilers, Heterogeneous systems, Image processing, Machine learning, nVidia, nVidia GeForce GTX 960, OpenCL, Package, performance portability, Tesla K40

January 13, 2018 by hgpu

Pass a Pointer: Exploring Shared Virtual Memory Abstractions in OpenCL Tools for FPGAs

Felix Winterstein, George Constantinides

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Tags: Computer science, FPGA, Heterogeneous systems, OpenCL, Package

December 24, 2017 by hgpu

Extending OmpSs for OpenCL kernel co-execution in heterogeneous systems

B. Perez, E. Stafford, J. L. Bosque, R. Beivide, S. Mateo, X. Teruel, X. Martorell, E. Ayguade

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, OmpSs, OpenCL, OpenMP, Tesla K20

December 24, 2017 by hgpu

Task Scheduling for Heterogeneous Multicore Systems

Zhuo Chen, Diana Marculescu

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 760 Ti, OpenCL, Task scheduling

December 15, 2017 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

HPVM: Heterogeneous Parallel Virtual Machine

OpenCL-ready High Speed FPGA Network for Reconfigurable High Performance Computing

Programming Abstractions and Optimization Techniques for GPU-based Heterogeneous Systems

Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures

MiMatrix: A Massively Distributed Deep Learning Framework on a Petascale High-density Heterogeneous Cluster

REOH: Runtime Energy Optimization for Heterogeneous Systems

SkePU 2: Flexible and Type-Safe Skeleton Programming for Heterogeneous Parallel Systems

Communication Architectures for Scalable GPU-centric Computing Systems

ImageCL: Language and source-to-source compiler for performance portability, load balancing, and scalability prediction on heterogeneous systems

Pass a Pointer: Exploring Shared Virtual Memory Abstractions in OpenCL Tools for FPGAs

Extending OmpSs for OpenCL kernel co-execution in heterogeneous systems

Task Scheduling for Heterogeneous Multicore 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)