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Stellar Mergers with HPX-Kokkos and SYCL: Methods of using an Asynchronous Many-Task Runtime System with SYCL

Gregor Daiß, Patrick Diehl, Hartmut Kaiser, Dirk Pflüger

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Tags: AMD Radeon Instinct MI100, Astrophysics, Computer science, CUDA, HIP, nVidia, nVidia A100, Package, SYCL

March 19, 2023 by hgpu

Statistical Computing With Graphics Processing Units

Ruoyong Xu

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Tags: Computer science, Package, Pseudo-random number generators, R, Statistics, Thesis

March 19, 2023 by hgpu

Towards a Benchmarking Suite for Kernel Tuners

Jacob O. Tørring, Ben van Werkhoven, Filip Petrovic, Floris-Jan Willemsen, Jirí Filipovic, Anne C. Elster

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, nVidia GeForce RTX 3060, nVidia GeForce RTX 3090, nVidia Titan RTX, Package, performance portability

March 19, 2023 by hgpu

Machine Learning-Driven Adaptive OpenMP For Portable Performance on Heterogeneous Systems

Giorgis Georgakoudis, Konstantinos Parasyris, Chunhua Liao, David Beckingsale, Todd Gamblin, Bronis de Supinski

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Tags: AMD Radeon Instinct Mi50, ATI, Benchmarking, Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, OpenMP, performance portability, Tesla P100, Tesla V100

March 19, 2023 by hgpu

ARK: GPU-driven Code Execution for Distributed Deep Learning

Changho Hwang, KyoungSoo Park, Ran Shu, Xinyuan Qu, Peng Cheng, Yongqiang Xiong

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Tags: Computer science, Deep learning, Distributed computing, nVidia, nVidia V100

March 12, 2023 by hgpu

BenchDirect: A Directed Language Model for Compiler Benchmarks

Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim Hazelwood, Ajitha Rajan, Hugh Leather

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Tags: Benchmarking, Compilers, Computer science, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

March 12, 2023 by hgpu

A Deep Learning Model for Loop Interchange

Lina Mezdour, Khadidja Kadem, Massinissa Merouani, Amina Selma Haichour, Saman Amarasinghe, Riyadh Baghdadi

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Tags: Compilers, Computer science, Deep learning, nVidia, nVidia DGX-A100, Package

March 12, 2023 by hgpu

Bridging Control-Centric and Data-Centric Optimization

Tal Ben-Nun, Berke Ates, Alexandru Calotoiu, Torsten Hoefler

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Tags: Code generation, Compilers, Computer science, Package

March 12, 2023 by hgpu

Runtime Support for Performance Portability on Heterogeneous Distributed Platforms

Polykarpos Thomadakis, Nikos Chrisochoides

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, performance portability, Tesla V100

March 12, 2023 by hgpu

Harmonic CUDA: Asynchronous Programming on GPUs

Jonathan Wapman, Sean Treichler, Serban D. Porumbescu, John D. Owens

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Tags: Computer science, CUDA, GEMM, Linear Algebra, Matrix multiplication, nVidia, nVidia A100

March 5, 2023 by hgpu

EvoTorch: Scalable Evolutionary Computation in Python

Nihat Engin Toklu, Timothy Atkinson, Vojtěch Micka, Paweł Liskowski, Rupesh Kumar Srivastava

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Tags: Artificial intelligence, Computer science, CUDA, Evolutionary Computations, Neural and Evolutionary Computing, nVidia, Package, Python, PyTorch, Tesla V100

March 5, 2023 by hgpu

DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

Mahdi Taheri, Mohamad Riazati, Mohammad Hasan Ahmadilivani, Maksim Jenihhin, Masoud Daneshtalab, Jaan Raik, Mikael Sjodin, Bjorn Lisper

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Tags: Computer science, Deep learning, Design space exploration, FPGA, HLS, Neural networks, Package

March 5, 2023 by hgpu

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

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high performance computing on graphics processing units: hgpu.org

Stellar Mergers with HPX-Kokkos and SYCL: Methods of using an Asynchronous Many-Task Runtime System with SYCL

Statistical Computing With Graphics Processing Units

Towards a Benchmarking Suite for Kernel Tuners

Machine Learning-Driven Adaptive OpenMP For Portable Performance on Heterogeneous Systems

ARK: GPU-driven Code Execution for Distributed Deep Learning

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Runtime Support for Performance Portability on Heterogeneous Distributed Platforms

Harmonic CUDA: Asynchronous Programming on GPUs

EvoTorch: Scalable Evolutionary Computation in Python

DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

Recent source codes

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