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A Workload Balanced MapReduce Framework on GPU Platforms

Yue Zhang

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Tags: Benchmarking, Computer science, CUDA, MapReduce, nVidia, Performance, Tesla K40, Thesis

January 12, 2016 by hgpu

A time-energy performance analysis of MapReduce on heterogeneous systems with GPUs

Dumitrel Loghin, Lavanya Ramapantulu, Oana Barbu, Yong Meng Teo

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Tags: Computer science, CUDA, Heterogeneous systems, MapReduce, nVidia, nVidia GeForce GTX 750 Ti, nVidia Jetson TK1

December 22, 2015 by hgpu

Accelerating Applications with Pattern-specific Optimizations on Accelerators and Coprocessors

Linchuan Chen

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Tags: Computer science, CUDA, Heterogeneous systems, MapReduce, Matrix multiplication, MPI, nVidia, Sparse matrix, Task scheduling, Tesla M2070, Thesis

October 13, 2015 by hgpu

MRCUDA: MapReduce Acceleration Framework Based on GPU

Jie Wang, Yanshuo Yu, Hang Cui, Shenglai Yang

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Tags: Algorithms, Computer science, CUDA, MapReduce, nVidia, nVidia GeForce GTX 460, Sorting

May 15, 2015 by hgpu

Counting Triangles in Large Graphs on GPU

Adam Polak

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Source codes

Tags: Clustering, Computer science, CUDA, Graph theory, MapReduce, nVidia, Package, Tesla C2050

March 3, 2015 by hgpu

Performance Analysis and Optimization of a Distributed Processing Framework for Data Mining Accelerated with Graphics Processing Units

Edgar Isaac Hiroshi Leon Saiki

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Tags: Algorithms, Computer science, CUDA, Data mining, MapReduce, nVidia, nVidia GeForce GTX Titan, Tesla K20, Thesis

February 1, 2015 by hgpu

Revisit Long Short-Term Memory: An Optimization Perspective

Qi Lyu, Jun Zhu

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Tags: Computer science, CUDA, Machine learning, MapReduce, Neural networks, nVidia, nVidia GeForce GTX 770

January 23, 2015 by hgpu

Accelerating mahout on heterogeneous clusters using HadoopCL

Xiangyu Li

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Tags: Cluster computing, Computer science, GPU cluster, Heterogeneous systems, Java, Machine learning, MapReduce, nVidia, OpenCL, Tesla K20, Thesis

January 19, 2015 by hgpu

Accelerated Runtime Verification of LTL Specifications with Counting Semantics

Ramy Medhat, Yogi Joshi, Borzoo Bonakdarpour, Sebastian Fischmeister

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Tags: Algorithms, Computer science, CUDA, Logic in Computer Science, MapReduce, nVidia, Tesla K20, Thrust

November 12, 2014 by hgpu

A Review of CUDA, MapReduce, and Pthreads Parallel Computing Models

Kato Mivule, Benjamin Harvey, Crystal Cobb, Hoda El Sayed

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Tags: Computer science, CUDA, MapReduce, nVidia, Overview, Pthreads, Review, Tesla K20

October 18, 2014 by hgpu

Scalable approximate k-NN in multidimensional big data

Hisham Mohamed

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Tags: Algorithms, Computer science, CUDA, Data mining, Data Structures and Algorithms, Information Retrieval, Machine learning, MapReduce, MPI, Nearest neighbour, nVidia, nVidia Quadro NVS 5200 M, Thesis, Visualization

October 13, 2014 by hgpu

Embedding GPU Computations in Hadoop

Jie Zhu, Hai Jiang, Juanjuan Li, Erikson Hardesty, Kuan-Ching Li, Zhongwen Li

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Tags: Cloud, Computer science, CUDA, Hadoop, Heterogeneous systems, Java, MapReduce, nVidia, nVidia GeForce GTX 480, Task scheduling

October 6, 2014 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

A Workload Balanced MapReduce Framework on GPU Platforms

A time-energy performance analysis of MapReduce on heterogeneous systems with GPUs

Accelerating Applications with Pattern-specific Optimizations on Accelerators and Coprocessors

MRCUDA: MapReduce Acceleration Framework Based on GPU

Counting Triangles in Large Graphs on GPU

Performance Analysis and Optimization of a Distributed Processing Framework for Data Mining Accelerated with Graphics Processing Units

Revisit Long Short-Term Memory: An Optimization Perspective

Accelerating mahout on heterogeneous clusters using HadoopCL

Accelerated Runtime Verification of LTL Specifications with Counting Semantics

A Review of CUDA, MapReduce, and Pthreads Parallel Computing Models

Scalable approximate k-NN in multidimensional big data

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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