high performance computing on graphics processing units: hgpu.org

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Improving GPU Performance through Instruction Redistribution and Diversification

Xiang Gong

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Tags: AMD Radeon HD 7970, ATI, Compilers, Computer science, OpenCL, Optimization, Thesis

March 10, 2019 by hgpu

Energy Efficient Parallel K-Means Clustering for an Intel Hybrid Multi-Chip Package

Matheus Souza, Lucas Maciel, Pedro Henrique Penna, Henrique Freitas

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Tags: Clustering, Computer science, Energy-efficient computing, FPGA, Intel Xeon Phi, Machine learning, OpenCL

March 10, 2019 by hgpu

On the Portability of GPU-Accelerated Applications via Automated Source-to-Source Translation

Paul Sathre, Mark Gardner, Wu-chun Feng

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Tags: AMD FirePro S9150, ATI, Computer science, CUDA, FPGA, Intel Xeon Phi, nVidia, OpenCL, Package, performance portability, Tesla K80

March 10, 2019 by hgpu

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang

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Tags: Computer science, Graph theory, Machine learning, nVidia, nVidia GeForce GTX 1080, Tesla P100

March 10, 2019 by hgpu

Custom Code Generation for a Graph DSL

Bikash Gogoi, Unnikrishnan Cheramangalath, Rupesh Nasre

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Tags: Code generation, Computer science, CUDA, DSL, nVidia, Tesla K40

March 10, 2019 by hgpu

Using Compiler Directives for Performance Portability in Scientific Computing: Kernels from Molecular Simulation

Ada Sedova, Andreas Tillack, Arnold Tharrington

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Tags: Chemistry, Molecular dynamics, Molecular simulation, nVidia, OpenACC, performance portability, Physics, Tesla K20

March 3, 2019 by hgpu

Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL

Maria Angelica Davila Guzman, Raul Nozal, Ruben Gran Tejero, Maria Villarroya-Gaudo, Dario Suarez Gracia, Jose Luis Bosque

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Tags: Computer science, FPGA, Heterogeneous systems, load balancing, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package

March 3, 2019 by hgpu

Application level energy measurements and models for hybrid platform with accelerators

Kenneth O'Brien

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, OpenCL, Tesla K40, Thesis

March 3, 2019 by hgpu

Stateful Dataflow Multigraphs: A Data-Centric Model for High-Performance Parallel Programs

Tal Ben-Nun, Johannes de Fine Licht, Alexandros Nikolaos Ziogas, Timo Schneider, Torsten Hoefler

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Tags: Computer science, CUDA, FPGA, nVidia, OpenCL, Optimization, Performance, Tesla P100

March 3, 2019 by hgpu

cuSten – CUDA Finite Difference and Stencil Library

Andrew Gloster, Lennon O'Naraigh

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Tags: Computational Physics, Computer science, CUDA, Finite difference, MPI, nVidia, nVidia GeForce GTX Titan X, Package, PDEs

March 3, 2019 by hgpu

An Empirically Guided Optimization Framework for FPGA OpenCL

Ahmed Sanaullah, Rushi Patel, Martin C. Herbordt

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Tags: Computer science, FPGA, OpenCL, Optimization

February 24, 2019 by hgpu

A Package for Multi-Dimensional Monte Carlo Integration on Multi-GPUs

Hong-zhong Wu, Junjie Zhang, Long-gang Pang, Qun Wang

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Tags: Computational Physics, Monte Carlo integration, nVidia, Package, Physics, Python, TensorFlow, Tesla K40

February 24, 2019 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

Applications

Improving GPU Performance through Instruction Redistribution and Diversification

Energy Efficient Parallel K-Means Clustering for an Intel Hybrid Multi-Chip Package

On the Portability of GPU-Accelerated Applications via Automated Source-to-Source Translation

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

Custom Code Generation for a Graph DSL

Using Compiler Directives for Performance Portability in Scientific Computing: Kernels from Molecular Simulation

Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL

Application level energy measurements and models for hybrid platform with accelerators

Stateful Dataflow Multigraphs: A Data-Centric Model for High-Performance Parallel Programs

cuSten – CUDA Finite Difference and Stencil Library

An Empirically Guided Optimization Framework for FPGA OpenCL

A Package for Multi-Dimensional Monte Carlo Integration on Multi-GPUs

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)