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Fast Frequent Itemset Mining from Uncertain Databases using GPGPU

Yusuke Kozawa, Toshiyuki Amagasa, Hiroyuki Kitagawa

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Tags: Computer science, CUDA, Data mining, Databases, nVidia, nVidia Quadro FX 4800

September 27, 2011 by hgpu

A GPU approach to parallel replica-exchange polymer simulations

Jonathan Gross, Wolfhard Janke, Michael Bachmann

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Tags: CUDA, Monte Carlo simulation, nVidia, Physics, Polymer Systems, Programming techniques

September 27, 2011 by hgpu

A framework to implement a multifrontal scheme on GPU architectures with OpenCL

Federico Edoardo Bonelli

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Tags: BLAS, Computer science, Linear Algebra, nVidia, OpenCL, Performance, Thesis

September 27, 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

PIPS Is not (just) Polyhedral Software

Mehdi Amini, Corinne Ancourt, Fabien Coelho, Beatrice Creusillet, Serge Guelton, Francois Irigoin, Pierre Jouvelot, Ronan Keryell, Pierre Villalon

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL

September 27, 2011 by hgpu

E(A+M)PEC – An OpenCL Atomic and Molecular Plasma Emission Code For Interstellar Medium Simulations

Miguel A. de Avillez, Emanuele Spitoni, Dieter Breitschwerdt

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Tags: Astrophysics, Galaxy Astrophysics, High Energy Astrophysical Phenomena, nVidia, OpenCL, Plasma physics

September 27, 2011 by hgpu

PGEM: Preemptive GPGPU Execution Model for Runtime Engines

Shinpei Kato, Karthik Lakshmanan, Aman Kumar, Mihir Kelkar, Yutaka Ishikawa, Ragunathan (Raj) Rajkumar

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Package, Performance, Task scheduling

September 26, 2011 by hgpu

Manycore high-performance computing in bioinformatics

Jean-Stephane Varre, Bertil Schmidt, Stephane Janot, Mathieu Giraud

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, OpenCL, Review, RNA, Sequence alignment

September 26, 2011 by hgpu

Generating GPU Code from a High-level Representation for Image Processing Kernels

Richard Membarth, Anton Lokhmotov, Jurgen Teich

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Tags: ATI, ATI Radeon HD 5870, Code generation, Compilers, CUDA, Image processing, nVidia, nVidia GeForce GTX 280, nVidia Quadro FX 5800, OpenCL, Optimization, Tesla C2050

September 26, 2011 by hgpu

LLVM to PTX Backend

Marco Keller

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, PTX

September 26, 2011 by hgpu

A Uniform Platform to Support Multigenerational GPUs for High Performance Stream-based Computing

Pablo Lamilla Alvarez, Shinichi Yamagiwa, Masahiro Arai, Koichi Wada

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GT 220, nVidia GeForce GTX 280, OpenCL, OpenGL, Performance

September 26, 2011 by hgpu

High-level GPU computing with jacket for MATLAB and C/C++

Gallagher Pryor, Brett Lucey, Sandeep Maddipatla, Chris McClanahan, John Melonakos, Vishwanath Venugopalakrishnan, Krunal Patel, Pavan Yalamanchili, James Malcolm

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Tags: Computer science, CUDA, FFT, Image processing, Linear Algebra, nVidia, nVidia GeForce 9600, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenGL, Package, PTX, Tesla C1060, Tesla C2070

September 26, 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 plugin for PySCF

Python-Based Quantum Chemistry Calculations with GPU Acceleration

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

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

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