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Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems – a Case Study

Lukas Sommer, Florian Stock, Leonardo Solis-Vasquez, Andreas Koch

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Tags: Automotive, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson TX2, OpenCL, OpenMP

January 26, 2020 by hgpu

Efficient Radial Pattern Keyword Search on Knowledge Graphs in Parallel

Yueji Yang, Anthony K. H. Tung

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 1080 Ti, OpenMP

January 26, 2020 by hgpu

ArborX: A Performance Portable Search Library

D. Lebrun-Grandie, A. Prokopenko, B. Turcksin, S. R. Slattery

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Tags: Algorithms, Computer science, CUDA, Mathematical Software, MPI, OpenMP, Package, performance portability

September 8, 2019 by hgpu

Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

Andre Viebke, Sabri Pllana, Suejb Memeti, Joanna Kolodziej

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Tags: Computer science, Deep learning, Intel Xeon Phi, Neural networks, OpenMP, Performance

June 12, 2019 by hgpu

A Survey on Evaluating and Optimizing Performance of Intel Xeon Phi

Sparsh Mittal, Osho Anand, Visnu P Kumarr

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Tags: Compilers, Computer science, Intel Xeon Phi, OpenMP, survey

June 9, 2019 by hgpu

Raising the Performance of the Tinker-HP Molecular Modeling Package on Intel’s HPC Architectures: a Living Review [Article v1.0]

Luc-Henri Jolly, Alejandro Duran, Louis Lagardere, Jay W. Ponder, Pengyu Ren, Jean-Philip Piquemal

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Tags: Chemical Physics, Computational Physics, Computer science, Intel Xeon Phi, Mathematical Software, Molecular dynamics, Molecular modeling, OpenMP, Package

June 5, 2019 by hgpu

An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language

Pramod Kumbhar, Omar Awile, Liam Keegan, Jorge Blanco Alonso, James King, Michael Hines, Felix Schuermann

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Tags: Biophysics, Code generation, Compilers, Computer science, CUDA, HPC, Intel Xeon Phi, LLVM, nVidia, OpenACC, OpenMP, Package

May 15, 2019 by hgpu

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

Nico Reissmann

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Tags: Computer science, CUDA, nVidia, OpenMP, Package, Performance, performance portability, Tesla K20, Thesis

May 5, 2019 by hgpu

Performance Evaluation of OpenMP’s Target Construct on GPUs: Exploring Compiler Optimizations

Akihiro Hayashi, Jun Shirako, Etorre Tiotto, Robert Ho, Vivek Sarkar

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Tags: Compilers, Computer science, CUDA, LLVM, nVidia, OpenMP, Optimization, Tesla K80, Tesla P100

February 10, 2019 by hgpu

Exploiting OpenMP & OpenACC to Accelerate a Molecular Docking Mini-App in Heterogeneous HPC Nodes

Emanuele Vitali, Davide Gadioli, Gianluca Palermo, Andrea Beccari, Carlo Cavazzoni, Cristina Silvano

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, molecular docking, MPI, nVidia, OpenACC, OpenMP, Tesla K80

January 27, 2019 by hgpu

Supporting mixed-datatype matrix multiplication within the BLIS framework

Field G. Van Zee, Devangi N. Parikh, Robert A. van de Geijn

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Tags: Computer science, Matrix multiplication, OpenMP, Package

January 27, 2019 by hgpu

Towards Automatic Transformation of Legacy Scientific Code into OpenCL for Optimal Performance on FPGAs

Wim Vanderbauwhede, Syed Waqar Nabi

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Tags: Compilers, Computer science, Fortran, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, OpenACC, OpenCL, OpenMP, Package

January 6, 2019 by hgpu

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

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high performance computing on graphics processing units: hgpu.org

Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems – a Case Study

Efficient Radial Pattern Keyword Search on Knowledge Graphs in Parallel

ArborX: A Performance Portable Search Library

Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

A Survey on Evaluating and Optimizing Performance of Intel Xeon Phi

Raising the Performance of the Tinker-HP Molecular Modeling Package on Intel’s HPC Architectures: a Living Review [Article v1.0]

An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

Performance Evaluation of OpenMP’s Target Construct on GPUs: Exploring Compiler Optimizations

Exploiting OpenMP & OpenACC to Accelerate a Molecular Docking Mini-App in Heterogeneous HPC Nodes

Supporting mixed-datatype matrix multiplication within the BLIS framework

Towards Automatic Transformation of Legacy Scientific Code into OpenCL for Optimal Performance on FPGAs

Recent source codes

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

HPX: a C++ Standard Library for Concurrency and Parallelism

TorchQC: Quantum Dynamics and Machine Learning

Matrix multiplication using Tensor Cores in CUDA

CPP Joules: Energy Measurement tool for CPP/CUDA programs

HPC-Coder-v2

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