Simple MapReduce Implementation for Calculating PageRank in C++
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Updated
Dec 7, 2016 - C++
Simple MapReduce Implementation for Calculating PageRank in C++
Implemented parallel and distributed algorithms using OpenMP, Apache Spark and NVIDIA CUDA
A distributed version of map procedure that distributes task to multiple threads running on multiple machines
Performance evaluation of Phoenix++ and MPI + OpenMP equi-join models for processing big data
Distributed Graph Clustering using the experimental Thrill framework
Counts the word occurrences in a file
A single header file containing container utils such as fmap, flatmap, filter, group_by and a generic container-to-string with show.
Mimir is a new implementation of MapReduce over MPI. Mimir inherits the core principles of existing MapReduce frameworks, such as MR-MPI, while redesigning the execution model to incorporate a number of sophisticated optimization techniques that achieve similar or better performance with significant reduction in the amount of memory used.
Performance evaluation of Phoenix++ and MPI + OpenMP equi-join models for processing big data
Implementations for Ceng334-Introduction to Operating Systems Course Assignments
homeworks for Financial Analytics & Big Data
MapReduce implementation with POSIX threads ThreadPool
Collection streaming utilities.
PageRank algorithm implemented using MapReduce libraries using MPI
Operating Systems Projects to demonstrate knowledge of interprocess communication, System calls, and memory mapping
C++ framework for Map/Reduce paradigm
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