high performance computing on graphics processing units: hgpu.org

Packages

hgpu.org » Applications

cuSZ(x): Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs

Jiannan Tian, Sheng Di, Xiaodong Yu, Cody Rivera, Kai Zhao, Sian Jin, Yunhe Feng, Xin Liang, Dingwen Tao, Franck Cappello

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Tags: Algorithms, Compression, Computer science, CUDA, Heterogeneous systems, nVidia, Package, Tesla A100, Tesla V100

May 30, 2021 by hgpu

Automatically Exploiting the Memory Hierarchy of GPUs through Just-in-Time Compilation

Michail Papadimitriou, Juan Fumero, Athanasios Stratikopoulos, Christos Kotselidis

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Tags: Benchmarking, Computer science, Java, nVidia, nVidia GeForce GTX 1650, OpenCL, Package

May 23, 2021 by hgpu

Comparison of HPC Architectures for Computing All-Pairs Shortest Paths. Intel Xeon Phi KNL vs NVIDIA Pascal

Manuel Costanzo, Enzo Rucci, Ulises Costi, Franco Chichizola, Marcelo Naiouf

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Tags: Algorithms, All-Pairs Distance, Computer science, CUDA, HPC, Intel, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan X, Package

May 23, 2021 by hgpu

NPBench: A Benchmarking Suite for High-Performance NumPy

Alexandros Nikolaos Ziogas, Tal Ben-Nun, Timo Schneider, Torsten Hoefler

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Tags: Benchmarking, Computer science, CUDA, HPC, Linear Algebra, nVidia, Package, Performance, Python, Tesla V100

May 16, 2021 by hgpu

PeriPy – A High Performance OpenCL Peridynamics Package

B. Boys, T. J. Dodwell, M. Hobbs, M. Girolami

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Tags: Benchmarking, Computer science, Heterogeneous systems, nVidia, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Python

May 16, 2021 by hgpu

Performance Evaluation and Improvements of the PoCL Open-Source OpenCL Implementation on Intel CPUs

Tobias Baumann, Matthias Noack, Thomas Steinke

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Tags: Benchmarking, Computer science, LLVM, OpenCL, Package, Video

May 9, 2021 by hgpu

A fluid simulation system based on the MPS method

André Luiz Buarque Vieira e Silva, Caio José dos Santos Brito, Francisco Paulo Magalhães Simões, Veronica Teichrieb

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Tags: cfd, CUDA, Fluid dynamics, Fluid simulation, nVidia, nVidia GeForce GTX 1080 Ti, OpenMP, Package

May 9, 2021 by hgpu

Easy and Efficient Transformer: Scalable Inference Solution For large NLP mode

Gongzheng li, Yadong Xi, Jingzhen Ding, Duan Wang, Bai Liu, Changjie Fan, Xiaoxi Mao, Zeng Zhao

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Tags: Computer science, CUDA, NLP, nVidia, nVidia GeForce RTX 2080 Ti, Package, Tesla A100

May 2, 2021 by hgpu

Ripple: Simplified Large-Scale Computation on Heterogeneous Architectures with Polymorphic Data Layout

Robert Clucas, Philip Blakely, Nikolaos Nikiforakis

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Package, Tesla V100

April 25, 2021 by hgpu

CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU

Sijun Tan, Brian Knott, Yuan Tian, David J. Wu

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Tags: Computer science, CUDA, Deep learning, Linear Algebra, Machine learning, Neural networks, nVidia, Package, PyTorch, Security, Tesla V100

April 25, 2021 by hgpu

SLATE port to AMD and Intel platforms

Ahmad Abdelfattah, Mohammed Al Farhan, Cade Brown, Mark Gates, Dalal Sukkari, Asim YarKhan, Jack Dongarra

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, OpenCL, Package, SYCL

April 18, 2021 by hgpu

Under the Hood of SYCL – An Initial Performance Analysis With an Unstructured-mesh CFD Application

Istvan Z. Reguly, Andrew M.B. Owenson, Archie Powell, Stephen A. Jarvis, Gihan R. Mudalige

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Tags: Benchmarking, cfd, CUDA, Fluid dynamics, MPI, nVidia, OpenMP, Package, SYCL

April 18, 2021 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Packages

cuSZ(x): Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs

Automatically Exploiting the Memory Hierarchy of GPUs through Just-in-Time Compilation

Comparison of HPC Architectures for Computing All-Pairs Shortest Paths. Intel Xeon Phi KNL vs NVIDIA Pascal

NPBench: A Benchmarking Suite for High-Performance NumPy

PeriPy – A High Performance OpenCL Peridynamics Package

Performance Evaluation and Improvements of the PoCL Open-Source OpenCL Implementation on Intel CPUs

A fluid simulation system based on the MPS method

Easy and Efficient Transformer: Scalable Inference Solution For large NLP mode

Ripple: Simplified Large-Scale Computation on Heterogeneous Architectures with Polymorphic Data Layout

CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU

SLATE port to AMD and Intel platforms

Under the Hood of SYCL – An Initial Performance Analysis With an Unstructured-mesh CFD Application

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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