high performance computing on graphics processing units: hgpu.org

Programming

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Highly Efficient Lattice-Boltzmann Multiphase Simulations of Immiscible Fluids at High-Density Ratios on CPUs and GPUs through Code Generation

Markus Holzer, Martin Bauer, Harald Kostler, Ulrich Rüde

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Tags: Algorithms, cfd, Code generation, CUDA, Fluid dynamics, Lattice Boltzmann model, MPI, nVidia, OpenCL, Package, Physics, Programming techniques, Tesla P100, Tesla V100

December 20, 2020 by hgpu

Solving large permutation flow-shop scheduling problems on GPU-accelerated supercomputers

Jan Gmys

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Tags: Algorithms, Computer science, CUDA, nVidia, Optimization, Tesla V100

December 20, 2020 by hgpu

NVIDIA SimNet: an AI-accelerated multi-physics simulation framework

Oliver Hennigh, Susheela Narasimhan, Mohammad Amin Nabian, Akshay Subramaniam, Kaustubh Tangsali, Max Rietmann, Jose del Aguila Ferrandis, Wonmin Byeon, Zhiwei Fang, Sanjay Choudhry

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Tags: cfd, CUDA, Fluid dynamics, Linear Algebra, Machine learning, Neural networks, nVidia, nVidia A100, Partial differential equations, PDEs, Physics, Tesla V100, Video

December 20, 2020 by hgpu

NaturalCC: A Toolkit to Naturalize the Source Code Corpus

Yao Wan, Yang He, Jian-Guo Zhang, Yulei Sui, Hai Jin, Guandong Xu, Caiming Xiong, Philip S. Yu

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, Package, Programming Languages, Python, Video

December 13, 2020 by hgpu

Systolic-CNN: An OpenCL-defined Scalable Run-time-flexible FPGA Accelerator Architecture for Accelerating Convolutional Neural Network Inference in Cloud/Edge Computing

Akshay Dua, Yixing Li, Fengbo Ren

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Tags: Cloud, Computer science, DSP, FPGA, Hardware Architecture, Neural networks, OpenCL, Package

December 13, 2020 by hgpu

Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning

Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-Gon Chun

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Task scheduling, Tesla V100

December 13, 2020 by hgpu

Efficient code generation for hardware accelerators by refining partially specified implementation

Ulysse Beaugnon

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Tags: Algorithms, Code generation, Computer science, CUDA, Linear Algebra, nVidia, Optimization, Package, Thesis

December 13, 2020 by hgpu

Exploring FPGA Optimizations to Compute Sparse Numerical Linear Algebra Kernels

Federico Favaro, Ernesto Dufrechou, Pablo Ezzatti, Juan P. Oliver

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Tags: Computer science, FPGA, Linear Algebra, OpenCL, Optimization, Performance, Sparse

December 6, 2020 by hgpu

Toward Accurate Platform-Aware Performance Modeling for Deep Neural Networks

Chuan-Chi Wang, Ying-Chiao Liao, Ming-Chang Kao, Wen-Yew Liang, Shih-Hao Hung

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Performance

December 6, 2020 by hgpu

Python Workflows on HPC Systems

Dominik Strassel, Philipp Reusch, Janis Keuper

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Tags: Computer science, CUDA, Deep learning, GPU cluster, HPC, Machine learning, nVidia, PC cluster, Python

December 6, 2020 by hgpu

High-Throughput Parallel Viterbi Decoder on GPU Tensor Cores

Alireza Mohammadidoost, Matin Hashemi

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Tags: Algorithms, Computer science, CUDA, nVidia, Signal processing, Tesla V100

December 6, 2020 by hgpu

Evaluating the Performance and Portability of Contemporary SYCL Implementations

Beau Johnston, Jeffrey S. Vetter, Josh Milthorpe

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Tags: AMD Radeon VII, ATI, Benchmarking, Computer science, CUDA, Heterogeneous systems, HIP, Matrix multiplication, nVidia, OpenCL, Package, Performance, Tesla P100

November 29, 2020 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

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

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Highly Efficient Lattice-Boltzmann Multiphase Simulations of Immiscible Fluids at High-Density Ratios on CPUs and GPUs through Code Generation

Solving large permutation flow-shop scheduling problems on GPU-accelerated supercomputers

NVIDIA SimNet: an AI-accelerated multi-physics simulation framework

NaturalCC: A Toolkit to Naturalize the Source Code Corpus

Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning

Efficient code generation for hardware accelerators by refining partially specified implementation

Exploring FPGA Optimizations to Compute Sparse Numerical Linear Algebra Kernels

Toward Accurate Platform-Aware Performance Modeling for Deep Neural Networks

Python Workflows on HPC Systems

High-Throughput Parallel Viterbi Decoder on GPU Tensor Cores

Evaluating the Performance and Portability of Contemporary SYCL Implementations

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

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

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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