high performance computing on graphics processing units: hgpu.org

hgpu.org » MapReduce

Accelerating MapReduce on a coupled CPU-GPU architecture

Linchuan Chen, Xin Huo, Gagan Agrawal

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Tags: APU, ATI, ATI Radeon HD 6550, Computer science, Data mining, Heterogeneous systems, Machine learning, MapReduce, OpenCL

November 20, 2012 by hgpu

MR-API: A Comprehensive API Framework for Heterogeneous Multi-core Systems using Map Reduce Programming Model

Prashanth Thinakaran, Srinivas Avireddy, Prasanna Ranganathan, Sumalatha Ramachandran

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Tags: Benchmarking, Cloud, Computer science, Heterogeneous systems, MapReduce, nVidia, nVidia GeForce GTX 275

October 22, 2012 by hgpu

AeminiumGPU: An Intelligent Framework for GPU Programming

Alcides Fonseca, Bruno Cabral

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Tags: Computer science, Heterogeneous systems, Java, MapReduce, nVidia, nVidia GeForce GTX 285, OpenCL, Package

October 13, 2012 by hgpu

A Map-Reduce-Like System for Programming and Optimizing Data-Intensive Computations on Emerging Parallel Architectures

Wei Jiang

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Tags: Computer science, CUDA, Data mining, GPU cluster, Heterogeneous systems, MapReduce, MPI, nVidia, Tesla C2050, Thesis

September 1, 2012 by hgpu

A Research of MapReduce with GPU Acceleration

Miao Xin, Hao Li, Joan Lu

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Tags: ATI, ATI Radeon HD 5450, Computer science, Distributed computing, Heterogeneous systems, MapReduce, OpenCL

August 28, 2012 by hgpu

Optimizing MapReduce for GPUs with effective shared memory usage

Linchuan Chen, Gagan Agrawal

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Tags: Computer science, Heterogeneous systems, MapReduce, Memory, nVidia, nVidia Quadro FX 5800, Performance

July 16, 2012 by hgpu

A GPU-Accelerated Algorithm for Self-Organizing Maps in a Distributed Environment

Peter Wittek, Sandor Daranyi

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Tags: Algorithms, Computer science, CUDA, MapReduce, nVidia, Package, Self-organizing map, Tesla M2050

May 10, 2012 by hgpu

Hadoop+Aparapi: Making heterogenous MapReduce programming easier

Yu Lin, Semih Okur, Cosmin Radoi

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Tags: Algorithms, Cloud, Computer science, Java, MapReduce, OpenCL

April 10, 2012 by hgpu

Shredder: GPU-Accelerated Incremental Storage and Computation

Pramod Bhatotia, Rodrigo Rodrigues, Akshat Verma

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Tags: Cloud, Computer science, CUDA, MapReduce, nVidia, Storage system, Tesla C2050

February 24, 2012 by hgpu

Towards a Tunable Multi-Backend Skeleton Programming Framework for Multi-GPU Systems

Johan Enmyren, Usman Dastgeer, Christoph W. Kessler

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Tags: Code generation, Computer science, CUDA, Data parallelism, MapReduce, nVidia, nVidia GeForce GTS 250, OpenCL, Performance, Tesla C1060

January 30, 2012 by hgpu

Leveraging on High-Performance Computing and Cloud Technologies in Digital Libraries: A Case Study

Peter Wittek, Sandor Daranyi

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Tags: Algorithms, Cloud, Computer science, Machine learning, MapReduce, Text mining

January 20, 2012 by hgpu

Providing Source Code Level Portability Between CPU and GPU with MapCG

Chun-Tao Hong, De-Hao Chen, Yu-Bei Chen, Wen-Guang Chen, Wei-Min Zheng, Hai-Bo Lin

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Tags: Code generation, Computer science, CUDA, MapReduce, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, OpenCL

January 9, 2012 by hgpu

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

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

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

Accelerating MapReduce on a coupled CPU-GPU architecture

AeminiumGPU: An Intelligent Framework for GPU Programming

A Map-Reduce-Like System for Programming and Optimizing Data-Intensive Computations on Emerging Parallel Architectures

A Research of MapReduce with GPU Acceleration

Optimizing MapReduce for GPUs with effective shared memory usage

A GPU-Accelerated Algorithm for Self-Organizing Maps in a Distributed Environment

Towards a Tunable Multi-Backend Skeleton Programming Framework for Multi-GPU Systems

Providing Source Code Level Portability Between CPU and GPU with MapCG

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

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gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

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

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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