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Petascale Direct Numerical Simulation of Blood Flow on 200K Cores and Heterogeneous Architectures

Abtin Rahimian, Ilya Lashuk, Shravan Veerapaneni, Aparna Chandramowlishwaran, Dhairya Malhotra, Logan Moon, Rahul Sampath, Aashay Shringarpure, Jeffrey Vetter, Richard Vuduc, Denis Zorin, George Biros

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Tags: CUDA, Fluid dynamics, GPU cluster, Medicine, MPI, N-body simulation, nVidia, Tesla S1070

April 28, 2011 by hgpu

An 80-Fold Speedup, 15.0 TFlops Full GPU Acceleration of Non-Hydrostatic Weather Model ASUCA Production Code

Takashi Shimokawabe, Takayuki Aoki, Chiashi Muroi, Junichi Ishida, Kohei Kawano, Toshio Endo, Akira Nukada, Naoya Maruyama, Satoshi Matsuoka

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Tags: CUDA, Earth and Space Sciences, GPU cluster, nVidia, Tesla S1070, Weather Prediction Model

April 28, 2011 by hgpu

Improving numerical reproducibility and stability in large-scale numerical simulations on GPUs

Michela Taufer, Omar Padron, Philip Saponaro, Sandeep Patel

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Tags: Chemistry, CUDA, Floating point error, Molecular dynamics, nVidia, Tesla S1070

April 25, 2011 by hgpu

Automatically generating and tuning GPU code for sparse matrix-vector multiplication from a high-level representation

Dominik Grewe, Anton Lokhmotov

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Tags: ATI, ATI Radeon HD 5970, Code generation, Computer science, CUDA, High-level Languages, Linear Algebra, nVidia, OpenCL, Sparse matrix, Tesla S1070

April 21, 2011 by hgpu

Hybrid Map Task Scheduling for GPU-Based Heterogeneous Clusters

Koichi Shirahata, Hitoshi Sato, Satoshi Matsuoka

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Presentation, Task scheduling, Tesla S1070

April 16, 2011 by hgpu

A high-performance fault-tolerant software framework for memory on commodity GPUs

Naoya Maruyama, Akira Nukada, Satoshi Matsuoka

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Tags: Computer science, CUDA, Fault simulation, FFT, Matrix multiplication, N-body simulation, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 285, Tesla S1070

April 14, 2011 by hgpu

A Multi-GPU Spectrometer System for Real-Time Wide Bandwidth Radio Signal Analysis

Hirofumi Kondo, Eric Heien, Masao Okita, Dan Werthimer, Kenichi Hagihara

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Tags: CUDA, FFT, nVidia, Signal processing, Tesla C1060, Tesla S1070

April 13, 2011 by hgpu

DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI

Yongchao Liu, Bertil Schmidt, Douglas Maskell

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Source codes

Tags: Bioinformatics, Biology, CUDA, MPI, Next-Generation sequencing, nVidia, OpenMP, Package, Tesla S1070

April 11, 2011 by hgpu

Fast seismic modeling and Reverse Time Migration on a GPU cluster

Rached Abdelkhalek, Henri Calendra, Olivier Coulaud, Jean Roman, Guillaume Latu

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Tags: CUDA, Earth and Space Sciences, Finite difference, GPU cluster, nVidia, Seismic modeling, Seismology, Tesla S1070

April 10, 2011 by hgpu

Scaling Hierarchical N-body Simulations on GPU Clusters

Pritish Jetley, Lukasz Wesolowski, Filippo Gioachin, Laxmikant V. Kale, Thomas R. Quinn

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Tags: Astrophysics, Cosmology, CUDA, GPU cluster, N-body simulation, nVidia, nVidia GeForce 8800 GTS, Physics, Tesla S1070

April 7, 2011 by hgpu

Neuromorphic models on a GPGPU cluster

Bing Han, T.M. Taha

Tags: Biology, GPU cluster, MPI, Neuroscience, nVidia, Tesla S1070

April 5, 2011 by hgpu

Exploring utilisation of GPU for database applications

Slawomir Walkowiak, Konrad Wawruch, Marita Nowotka, Lukasz Ligowski, Witold Rudnicki

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Tags: Computer science, CUDA, Databases, nVidia, Tesla S1070

March 19, 2011 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

Petascale Direct Numerical Simulation of Blood Flow on 200K Cores and Heterogeneous Architectures

An 80-Fold Speedup, 15.0 TFlops Full GPU Acceleration of Non-Hydrostatic Weather Model ASUCA Production Code

Improving numerical reproducibility and stability in large-scale numerical simulations on GPUs

Automatically generating and tuning GPU code for sparse matrix-vector multiplication from a high-level representation

Hybrid Map Task Scheduling for GPU-Based Heterogeneous Clusters

A high-performance fault-tolerant software framework for memory on commodity GPUs

A Multi-GPU Spectrometer System for Real-Time Wide Bandwidth Radio Signal Analysis

DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI

Fast seismic modeling and Reverse Time Migration on a GPU cluster

Scaling Hierarchical N-body Simulations on GPU Clusters

Neuromorphic models on a GPGPU cluster

Exploring utilisation of GPU for database applications

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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