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The K-Anonymity Approach in Preserving the Privacy of E-Services that Implement Data Mining

Ion Lungu, Alexandru Pirjan

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Tags: Algorithms, CUDA, Data mining, Databases, Finance, nVidia, nVidia GeForce GTX 480

November 24, 2011 by hgpu

Scalable Simulation of 3D Wave Propagation in Semi-Infinite Domains Using the Finite Difference Method on a GPU Based Cluster

Thales Luis Rodrigues Sabino, Marcelo Zamith, Diego Brandao, Anselmo Montenegro, Esteban Clua, Maurcio Kischinhevksy, Regina C.P. Leal-Toledo, Otton T. Silveira Filho, Andre Bulcao

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Tags: Acoustics, CUDA, Earth and Space Sciences, Finite difference, nVidia, Tesla C1060

November 24, 2011 by hgpu

The Optimization of Algorithms in the Process of Temporal Data Mining Using the Compute Unified Device Architecture

Alexandru Pirjan

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Tags: Algorithms, Computer science, CUDA, Data mining, MapReduce, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Review

November 24, 2011 by hgpu

Accelerating QDP++/Chroma on GPUs

Frank Winter

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Source codes

Tags: CUDA, High Energy Physics - Lattice, nVidia, nVidia GeForce GTX 480, Package, Physics, QCD, Tesla C2070

November 24, 2011 by hgpu

A GPU-Enabled, High-Resolution Cosmological Microlensing Parameter Survey

Nicholas F. Bate, C. J. Fluke

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Tags: Astrophysics, Cosmology and Extragalactic Astrophysics, CUDA, Gravitation, Instrumentation and Methods for Astrophysics, nVidia, Tesla C2070, Tesla M2090

November 24, 2011 by hgpu

Automated architecture-aware mapping of streaming applications onto GPUs

Andrei Hagiescu, Huynh Phung Huynh, Weng-Fai Wong, Rick Siow Mong Goh

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, OpenCL, Performance, Task scheduling, Tesla S1070, Tesla S2050

November 23, 2011 by hgpu

A Parallel Deconvolution Algorithm in Perfusion Imaging

Fan Zhu, David Rodriguez Gonzalez, Trevor Carpenter, Malcolm Atkinson, Joanna Wardlaw

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Tags: Algorithms, Biology, CUDA, Medicine, nVidia, nVidia GeForce GTX 280, Performance, Tesla C1060

November 23, 2011 by hgpu

Soren: Adaptive MapReduce for Programmable GPUs

Reza Mokhtari, Amin Abbasi, Farshad Khunjush, Reza Azimi

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Tags: Computer science, CUDA, MapReduce, nVidia, nVidia GeForce GTX 480

November 23, 2011 by hgpu

Towards solving the Table Maker’s Dilemma on GPU

Pierre Fortin, Mourad Gouicem, Stef Graillat

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Tags: Algorithms, Computer science, CUDA, Elementary functions, nVidia, Tesla C2070

November 23, 2011 by hgpu

Building-Blocks for Performance Oriented DSLs

Tiark Rompf, Arvind K. Sujeeth, HyoukJoong Lee, Kevin J. Brown, Hassan Chafi, Martin Odersky, Kunle Olukotun

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Programming Languages, Tesla C2050

November 23, 2011 by hgpu

TEG: GPU Performance Estimation Using a Timing Model

Junjie Lai, Andre Seznec

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance

November 23, 2011 by hgpu

An efficient mixed-precision, hybrid CPU-GPU implementation of a fully implicit particle-in-cell algorithm

Guangye Chen, Luis Chacon, Daniel C. Barnes

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Tags: Algorithms, Computational Physics, CUDA, nVidia, nVidia GeForce GTX 580, Optimization, Particle-in-cell methods, Physics, Plasma physics

November 23, 2011 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

The K-Anonymity Approach in Preserving the Privacy of E-Services that Implement Data Mining

Scalable Simulation of 3D Wave Propagation in Semi-Infinite Domains Using the Finite Difference Method on a GPU Based Cluster

The Optimization of Algorithms in the Process of Temporal Data Mining Using the Compute Unified Device Architecture

Accelerating QDP++/Chroma on GPUs

A GPU-Enabled, High-Resolution Cosmological Microlensing Parameter Survey

Automated architecture-aware mapping of streaming applications onto GPUs

A Parallel Deconvolution Algorithm in Perfusion Imaging

Soren: Adaptive MapReduce for Programmable GPUs

Towards solving the Table Maker’s Dilemma on GPU

Building-Blocks for Performance Oriented DSLs

TEG: GPU Performance Estimation Using a Timing Model

An efficient mixed-precision, hybrid CPU-GPU implementation of a fully implicit particle-in-cell algorithm

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

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

Most viewed papers (last 30 days)