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Improving the Performance of the Contextual Spaces Re-Ranking Algorithm on Heterogeneous Systems

Flavia Pisani, Daniel C. G. Pedronette, Ricardo da S. Torres, Edson Borin

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Tags: Algorithms, ATI, ATI Radeon HD 6550, Computer science, Heterogeneous systems, List ranking, OpenCL

September 5, 2016 by hgpu

Hybrid Multicore Algorithms for Some Semi-Numerical Applications and Graphs

Dip Sankar Banerjee

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Tags: Algorithms, Cell processor, Computer science, CUDA, Distributed computing, List ranking, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 580, OpenCL, Sorting, Thesis

January 19, 2015 by hgpu

High-Performance Computing Algorithms for Constructing Inverted Files on Emerging Multicore Processors

Zheng Wei

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Information Retrieval, List ranking, nVidia, nVidia GeForce GTX 480, Tesla C1060, Tesla C2050, Thesis

October 16, 2012 by hgpu

An On-Demand Fast Parallel Pseudo Random Number Generator with Applications

Dip Sankar Banerjee, Aman Kumar Bahl, Kishore Kothapalli

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Tags: Algorithms, Computer science, CUDA, List ranking, Monte Carlo simulation, nVidia, Pseudo-random number generators, Tesla C1060

April 21, 2012 by hgpu

Cost Efficient PageRank Computation using GPU

Praveen K., Vamshi Krishna K., Anil Sri Harsha B., S. Balasubramanian, P.K. Baruah

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Tags: Algorithms, Computer science, CUBLAS, CUDA, List ranking, nVidia, Sparse matrix, Tesla T20

January 24, 2012 by hgpu

Hybrid Algorithms for List Ranking and Graph Connected Components

Dip Sankar Banerjee, Kishore Kothapalli

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Tags: Algorithms, Computer science, CUDA, List ranking, nVidia, Sorting, Tesla C1060

January 8, 2012 by hgpu

Efficient PageRank and SpMV Computation on AMD GPUs

Tianji Wu, Bo Wang, Yi Shan, Feng Yan, Yu Wang, Ningyi Xu

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Tags: ATI, ATI CAL, ATI IL, ATI Radeon HD 5870, ATI Stream, Computer science, List ranking, OpenCL, Sparse matrix

April 1, 2011 by hgpu

Fast and scalable list ranking on the GPU

M. Suhail Rehman, Kishore Kothapalli, P. J. Narayanan

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Tags: Algorithms, Computer science, CUDA, List ranking, nVidia, nVidia GeForce GTX 280

October 27, 2010 by hgpu

SolidTextureNets

On Demand Solid Texture Synthesis Using Deep 3D Networks

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

deepnetts: Deep Netts Pure Java Deep Learning Engine

Towards High Performance Java-based Deep Learning Frameworks

PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

TACO: The Tensor Algebra Compiler (taco) computes sparse tensor expressions on CPUs and GPUs

A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra

OpenCL-FPGA-LLVM: LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

CUDAnative.jl: Julia support for native CUDA programming

Abstractions for Programming Graphics Processors in High-Level Programming Languages

GPU explicit-state model checking

Porting tree-based hash table compression to GPGPU model checking

OCLADock: OpenCL Accelerated Molecular Docking

Accelerating Molecular Docking by Parallelized Heterogeneous Computing – A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs

jax-md: Differentiable, Hardware Accelerated, Molecular Dynamics

JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python

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2020

April

27-29

Munich, Germany

8th International Workshop on OpenCL, including SYCLCon, 2019

2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

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