Hybrid Monte Carlo/Deep Learning Methods for Matrix Computation on Advanced Architectures

Hybrid Monte Carlo/Deep Learning Methods for Matrix Computation on Advanced Architectures

Project reference: 1912

The focus of this project will be on enhancing further hybrid (e.g. stochastic/ deterministic) methods for Linear Algebra adding Deep Learning techniques. The focus is on Monte Carlo hybrid methods and algorithms for matrix inversion and solving systems of linear algebraic equations. Recent developments led to effective approaches based on building an efficient stochastic preconditioner and then solving the corresponding System of Linear Algebraic Equations (SLAE) by applying an iterative method. The preconditioner is a Monte Carlo preconditioner based on Markov Chain Monte Carlo (MCMC) methods to compute a rough approximate matrix inverse first. The above Monte Carlo preconditioner is further used to solve systems of linear algebraic equations thus delivering hybrid stochastic/deterministic algorithms. The advantage of the proposed approach is that the sparse Monte Carlo matrix inversion has a computational complexity linear of the size of the matrix.  Current implementations are either pure MPI or mixed MPI/OpenMP ones. There is also a version running on GPUs. The efficiency of the approach is usually tested on a set of different test matrices from several matrix market collections.

The intern has to take the existing codes and will have to integrate some Deep Learning methods such as Stochastic Gradient Descent in the hybrid method and to test the efficiency of the new method applied to a variety of matrices as well as Systems of Linear Equations with the same matrix but different right-hand side.

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Project Mentor: Vassil Alexandrov

Project Co-mentor: Jony Castagna

Site Co-ordinator: Luke Mason

Participant: Mustafa Emre Şahin

Learning Outcomes:
The student will learn to design parallel hybrid  Monte Carlo methods as well as advanced Deep Learning techniques.

The student will learn how to implement these methods on modern computer architectures with latest GPU accelerators as well as how to design and develop mixed MPI/CUDA and/or MPI/OpenMP code.

Student Prerequisites (compulsory):
The introductory level of Linear Algebra, some parallel algorithms design and implementation concepts, parallel programming using MPI and CUDA.

Student Prerequisites (desirable):
Some skills in being able to develop mixed code such as MPI/OpenMP or MPI/CUDA will be an advantage.

Training Materials:
These can be tailored to the student once he/she is selected.

Workplan:

  • Week 1/: Training week
  • Week 2/:  Literature Review Preliminary Report (Plan writing)
  • Week 3 – 7/: Project Development
  • Week8/: Final Report write-up

Final Product Description:
The final product will be a parallel application that can be executed on hybrid architectures with  GPU accelerators or a multicore one.  Ideally, we would like to publish the results in a paper on a conference or a workshop.

Adapting the Project: Increasing the Difficulty:
The project is on the appropriate cognitive level, taking into account the timeframe and the need to submit a final working product and 2 reports.

Adapting the Project: Decreasing the Difficulty:
The topic will be researched and the final product will be designed in full but some of the features may not be developed to ensure a working product with some limited features at the end of the project.

Resources:
The student will need access to a GPU and/or multicore-based machines, standard computing resources (laptop, internet connection).

Organisation:
Hartee Centre – STFC

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