high performance computing on graphics processing units: hgpu.org

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Enhancing Data Locality for Dynamic Simulations through Asynchronous Data Transformations and Adaptive Control

Bo Wu, Eddy Z. Zhang, Xipeng Shen

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Tags: Algorithms, Benchmarking, CMP, Computer science, CUDA, Heterogeneous systems, nVidia, Optimization, Tesla S1070

September 30, 2011 by hgpu

GPU Accelerated Computation of the ICON Model

Christian Conti, William Sawyer

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Tags: ATI, ATI Radeon HD 6990, Earth and Space Sciences, nVidia, nVidia GeForce GTX 285, OpenCL, Tesla M2050, Tesla S1070, Weather prediction

September 27, 2011 by hgpu

Hauberk: Lightweight Silent Data Corruption Error Detector for GPGPU

Keun Soo Yim, Cuong Pham, Mushfiq Saleheen, Zbigniew Kalbarczyk, Ravishankar Iyer

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Tags: Computer science, CUDA, Error recovery, Fault tolerance, nVidia, Tesla S1070

September 25, 2011 by hgpu

Implementing the PGI Accelerator model

Michael Wolfe

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, OpenMP, Optimization, Programming Languages, Tesla C1060, Tesla S1070

August 23, 2011 by hgpu

Maestro: Data Orchestration and Tuning for OpenCL Devices

Kyle Spafford, Jeremy Meredith, Jeffrey Vetter

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Tags: ATI, ATI Radeon HD 5870, Computer science, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTX, OpenCL, Performance, Tesla C1060, Tesla S1070

August 18, 2011 by hgpu

THOR: A Transparent Heterogeneous Open Resource framework

J.L. Vazquez-Poletti, J. Perhac, J. Ryan, A.C. Elster

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Tags: Algorithms, Benchmarking, Cloud, Computer science, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 470, OpenCL, Tesla S1070

August 10, 2011 by hgpu

Pricing the American Option Using Reconfigurable Hardware

Chris Wynnyk, Malik Magdon-Ismail

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Tags: Algorithms, CUDA, Finance, Finite difference, FPGA, nVidia, Tesla S1070

August 10, 2011 by hgpu

Data-Aware Task Scheduling on Multi-accelerator Based Platforms

Cedric Augonnet, Jerome Clet-Ortega, Samuel Thibault, Raymond Namyst

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Source codes

Tags: Algorithms, Benchmarking, Computer science, CUDA, Heterogeneous systems, MPI, nVidia, nVidia Quadro FX 5800, Package, Prefetch, Task scheduling, Tesla S1070

July 26, 2011 by hgpu

Out-of-core cone beam reconstruction using multiple GPUs

Fumihiko Ino, Yusuke Okitsu, Taketo Kishi, Syuhei Ohnishi, Kenichi Hagihara

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Tags: Algorithms, CUDA, Image reconstruction, Medicine, nVidia, Tesla S1070

July 24, 2011 by hgpu

Linpack evaluation on a supercomputer with heterogeneous accelerators

Toshio Endo, Akira Nukada, Satoshi Matsuoka, Naoya Maruyama

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Tags: Benchmarking, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Performance, Tesla S1070

July 17, 2011 by hgpu

A Sparse Matrix Personality for the Convey HC-1

Krishna K. Nagar, Jason D. Bakos

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Tags: Computer science, CUDA, FPGA, nVidia, Performance, Sparse matrix, Tesla S1070

July 14, 2011 by hgpu

Acceleration of Streamed Tensor Contraction Expressions on GPGPU-Based Clusters

Wenjing Ma, Sriram Krishnamoorthy, Oreste Villa, Karol Kowalski

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Tags: Computational chemistry, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, Presentation, Tesla C1070, Tesla S1070

July 7, 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

Enhancing Data Locality for Dynamic Simulations through Asynchronous Data Transformations and Adaptive Control

GPU Accelerated Computation of the ICON Model

Hauberk: Lightweight Silent Data Corruption Error Detector for GPGPU

Implementing the PGI Accelerator model

Maestro: Data Orchestration and Tuning for OpenCL Devices

Pricing the American Option Using Reconfigurable Hardware

Data-Aware Task Scheduling on Multi-accelerator Based Platforms

Out-of-core cone beam reconstruction using multiple GPUs

Linpack evaluation on a supercomputer with heterogeneous accelerators

A Sparse Matrix Personality for the Convey HC-1

Acceleration of Streamed Tensor Contraction Expressions on GPGPU-Based Clusters

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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