high performance computing on graphics processing units: hgpu.org

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Portable, Scalable Approaches for Improving Asynchronous Many-Task Runtime Node Use

John Keith Holmen

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX Titan X, performance portability, Task scheduling

June 5, 2022 by hgpu

Improving performance of SYCL applications on CPU architectures using LLVM-directed compilation flow

Pietro Ghiglio, Uwe Dolinsky, Mehdi Goli, Kumudha Narasimhan

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Tags: Computer science, CUDA, nVidia, OpenCL, Performance, performance portability, SYCL

May 1, 2022 by hgpu

Benchmarking a Proof-of-Concept Performance Portable SYCL-based Fast Fourier Transformation Library

Vincent R. Pascuzzi, Mehdi Goli

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, FFT, Heterogeneous systems, HIP, HPC, nVidia, nVidia A100, OpenCL, performance portability, SYCL

March 20, 2022 by hgpu

System-Level Optimization and Code Generation for Graphics Processors using a Domain-Specific Language

Bo Qiao

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Tags: Code generation, Computer science, CUDA, Image processing, Machine learning, nVidia, nVidia GeForce GTX 680, nVidia GeForce RTX 2080, OpenCL, Package, performance portability, Tesla K20, Thesis

January 2, 2022 by hgpu

Achieving near native runtime performance and cross-platform performance portability for random number generation through SYCL interoperability

Vincent R. Pascuzzi, Mehdi Goli

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Tags: AMD Radeon RX Vega 56, ATI, Computer science, Heterogeneous systems, nVidia, nVidia A100, performance portability, Pseudo-random number generators, SYCL

September 19, 2021 by hgpu

A Study of Mixed Precision Strategies for GMRES on GPUs

Jennifer A. Loe, Christian A. Glusa, Ichitaro Yamazaki, Erik G. Boman, Sivasankaran Rajamanickam

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Mixed precision, nVidia, performance portability, Tesla V100

September 19, 2021 by hgpu

Performance Portability and Evaluation of Heterogeneous Components of SeisSol Targeted to Upcoming Intel HPC GPUs

Ludwig Kratzl

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Intel, Intel UHD 630, MPI, nVidia, nVidia GeForce RTX 3090, OpenCL, Package, performance portability, SYCL, Thesis

August 29, 2021 by hgpu

kEDM: A Performance-portable Implementation of Empirical Dynamic Modeling using Kokkos

Keichi Takahashi, Wassapon Watanakeesuntorn, Kohei Ichikawa, Joseph Park, Ryousei Takano, Jason Haga, George Sugihara, Gerald M. Pao

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Tags: Computer science, CUDA, HPC, nVidia, OpenCL, Package, Performance, performance portability

May 30, 2021 by hgpu

Experimental Evaluation of Multiprecision Strategies for GMRES on GPUs

Jennifer A. Loe, Christian A. Glusa, Ichitaro Yamazaki, Erik G. Boman, Sivasankaran Rajamanickam

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Tags: Algorithms, CUDA, Linear Algebra, Mathematics, Numerical Analysis, nVidia, performance portability, Tesla V100

May 23, 2021 by hgpu

Optimal program variant generation for hybrid manycore systems

Cristian Urlea

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Tags: Computer science, Design space exploration, FPGA, Heterogeneous systems, OpenCL, performance portability, Thesis

February 21, 2021 by hgpu

HALO 1.0: A Hardware-agnostic Accelerator Orchestration Framework for Enabling Hardware-agnostic Programming with True Performance Portability for Heterogeneous HPC

Michael Riera, Erfan Bank Tavakoli, Masudul Hassan Quraishi, Fengbo Ren

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Tags: Computer science, FPGA, Heterogeneous systems, HPC, MPI, nVidia, nVidia GeForce RTX 2080 Ti, OpenCL, performance portability

November 29, 2020 by hgpu

Ginkgo – A Math Library designed for Platform Portability

Terry Cojean, Yu-Hsiang "Mike" Tsai, Hartwig Anzt

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Tags: Computer science, CUDA, HIP, nVidia, Package, Performance, performance portability

November 22, 2020 by hgpu

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Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

On the Compilation Performance of Current SYCL Implementations

M1 GPUs for (scientific) computations in C++

Seamless GPU acceleration for C++ based physics with the Metal Shading Language on Apple’s M series unified chips

CUB: Cooperative primitives for CUDA C++

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Simulator and workload generator for Edge-to-Cloud environments

FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems

See all packages

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high performance computing on graphics processing units: hgpu.org

Portable, Scalable Approaches for Improving Asynchronous Many-Task Runtime Node Use

Improving performance of SYCL applications on CPU architectures using LLVM-directed compilation flow

Benchmarking a Proof-of-Concept Performance Portable SYCL-based Fast Fourier Transformation Library

System-Level Optimization and Code Generation for Graphics Processors using a Domain-Specific Language

Achieving near native runtime performance and cross-platform performance portability for random number generation through SYCL interoperability

A Study of Mixed Precision Strategies for GMRES on GPUs

Performance Portability and Evaluation of Heterogeneous Components of SeisSol Targeted to Upcoming Intel HPC GPUs

kEDM: A Performance-portable Implementation of Empirical Dynamic Modeling using Kokkos

Experimental Evaluation of Multiprecision Strategies for GMRES on GPUs

Optimal program variant generation for hybrid manycore systems

HALO 1.0: A Hardware-agnostic Accelerator Orchestration Framework for Enabling Hardware-agnostic Programming with True Performance Portability for Heterogeneous HPC

Ginkgo – A Math Library designed for Platform Portability

Recent source codes

Rodinia Benchmarks for DPC++

DCA++: a state of the art implementation of the dynamical cluster approximation

tntorch - Tensor Network Learning with PyTorch

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

M1 GPUs for (scientific) computations in C++

CUB: Cooperative primitives for CUDA C++

Simulator and workload generator for Edge-to-Cloud environments

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