high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications

Performance Analysis and Automatic Tuning of Hash Aggregation on GPUs

Viktor Rosenfeld, Sebastian Bress, Steffen Zeuch, Tilmann Rabl, Volker Markl

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Tags: Algorithms, AMD Radeon R9 Fury, ATI, Computer science, Hashing, nVidia, nVidia GeForce GXT 1080, nVidia GeForce GXT 980, OpenCL, Performance, Tesla K40, Tesla V100

June 16, 2019 by hgpu

Tensor Processing Units for Financial Monte Carlo

Francois Belletti, Davis King, Kun Yang, Roland Nelet, Yusef Shafi, Yi-Fan Chen, John Anderson

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Tags: Cloud, Deep learning, Finance, Mixed precision, Neural networks, nVidia, Option pricing, Tesla V100, TPU

June 12, 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

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

Saptarshi Soham Mohanta, Collins Assisi

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Tags: Biology, Computer science, CUDA, Deep learning, Differential equations, Machine learning, Neural networks, nVidia, ODEs, Ordinary differential equations, Package, Partial differential equations, PDEs, Python, TensorFlow, Tutorial

June 12, 2019 by hgpu

Temporospatial Epidemic Simulations Using Heterogeneous Computing

Dhananjai M. Rao

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Tags: Earth and Space Sciences, HPC, OpenCL, Package

June 9, 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

PPOpenCL: a performance-portable OpenCL compiler with host and kernel thread code fusion

Ying Liu, Lei Huang, Mingchuan Wu, Huimin Cui, Fang Lv, Xiaobing Feng, Jingling Xue

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Tags: Code generation, Compilers, Computer science, OpenCL

June 9, 2019 by hgpu

Diagnosing Performance Bottlenecks in HPC Applications

Kenneth Czechowski

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Tags: Computer science, HPC, Package, Performance, Thesis

June 9, 2019 by hgpu

LAMDA: Learning-Assisted Multi-Stage Autotuning for FPGA Design Closure

Ecenur Ustun, Shaojie Xiang, Jinny Gui, Cunxi Yu, Zhiru Zhang

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Tags: Computer science, FPGA, OpenCL, Performance

June 9, 2019 by hgpu

ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data

Elias Stehle, Hans-Arno Jacobsen

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Tags: Computer science, CUDA, Data Structures and Algorithms, Databases, nVidia, nVidia GeForce GTX Titan X

June 5, 2019 by hgpu

Dynamic Distribution Pruning for Efficient Network Architecture Search

Xiawu Zheng, Rongrong Ji, Lang Tang, Yan Wan, Baochang Zhang, Yongjian Wu, Yunsheng Wu, Ling Shao

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla V100

June 5, 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

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

Applications

Performance Analysis and Automatic Tuning of Hash Aggregation on GPUs

Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

Temporospatial Epidemic Simulations Using Heterogeneous Computing

A Survey on Evaluating and Optimizing Performance of Intel Xeon Phi

PPOpenCL: a performance-portable OpenCL compiler with host and kernel thread code fusion

Diagnosing Performance Bottlenecks in HPC Applications

LAMDA: Learning-Assisted Multi-Stage Autotuning for FPGA Design Closure

ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data

Dynamic Distribution Pruning for Efficient Network Architecture Search

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

Recent source codes

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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