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An implementation of NOMAD with UPC++

This repository presents an implementation of Nonlocking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion (NOMAD) in C++ with UPC++. The primary ideas are extracted from this paper

Yun, Hyokun, Hsiang-Fu Yu, Cho-Jui Hsieh, S. V. N. Vishwanathan and Inderjit S. Dhillon. “NOMAD: Nonlocking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion.” Proc. VLDB Endow. 7 (2014): 975-986. https://arxiv.org/abs/1312.0193

UPC++ Installation

UPC++ is a parallel programming library for developing C++ applications with the Partitioned Global Address Space (PGAS) model. UPC++ has three main objectives:

  • Provide an object-oriented PGAS programming model in the context of the popular C++ language
  • Expose useful asynchronous parallel programming idioms unavailable in traditional SPMD models, such as remote function invocation and continuation-based operation completion, to support complex scientific applications
  • Offer an easy on-ramp to PGAS programming through interoperability with other existing parallel programming systems (e.g., MPI, OpenMP, CUDA)

You can setup UPC++ as following the instruction at here

Sparse Matrix Generation

You can generate a random sparse matrix of integers with an assumption that there is at least one non-zero value in each column and each row:

$ g++ -o gen_sparse_mat data/generate_sparse_matrix.cpp
$ ./gen_sparse_mat [NROWS] [NCOLS]

Example: The below command will generate a sparse matrix of integers with 100 rows and 700 columns

$ g++ -o gen_sparse_mat data/generate_sparse_matrix.cpp
$ ./gen_sparse_mat 100 700

NOMAD Execution

You can optionally modify the source code and build the source with UPC++ as simple commands as follow:

$ upcxx -O -o NOMAD-UPC main.cpp worker.cpp

To run this solution, you must specify the number of processes NUM_PROC, the input file for sparse matrix INPUT_FILE and the number of epochs you need to run NUM_EPOCHS

$ upcxx-run -n [NUM_PROC] NOMAD-UPC [INPUT_FILE] [NUM_EPOCHS]

For example: If you want to execute this implementation with 5 processes, the input matrix is store in matrix.txt, and the epoch of running is 5000, then the command should be:

$ upcxx-run -n 5 NOMAD-UPC matrix.txt 5000

The result will be stored in an output text file named: out_[INPUT_FILE]

NOMAD with MovieLens-100K

MovieLens 100K movie ratings. Stable benchmark dataset. 100,000 ratings from 1000 users on 1700 movies. I added an evaluation for Movielen-100K dataset. Training NOMAD with MovieLens on training set X (for X in [1, 2, 3, 4, 5, 'a', 'b']) is performed with following command:

$ upcxx-run -n 5 NOMAD-UPC movielen-100k-data/sparse_u[X].base [NUM_EPOCHS]

To evaluate the RMSE of training set of set X, we execute a command:

$ ./evaluation movielen-100k-data/out_sparse_u[X].base movielen-100k-data/sparse_u[X].base

To evaluate the RMSE of testing set of set X, we execute a command:

$ ./evaluation movielen-100k-data/out_sparse_u[X].base movielen-100k-data/sparse_u[X].test

Notice

There are some slight differences in this implementation as compared to the original idea in the paper:

  • I change the update function (9) and (10) into
    equ
    equ
  • Instead of transfer a pair of equ, I store all matrix equ in the global memory and I only transfer the index of corresponding rows equ of equ
  • I also implemented the mechanism of dynamic load balancing which was mentioned in the paper

Todos

  • Plug-in mmap file reading in C++ for big file reading
  • Visualize the procedure of resources transfer and allocation

License

Free for you, Easy to use