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OPEN MASTER THESIS PROJECTS

Research Group on Compiler Technology and Parallel Computing

Prof. Christoph Kessler

Internal Thesis Projects

Note:

All projects in this list require a solid background in either compiler construction or parallel programming (some both); at least one major course (preferably at master level including programming labs) in these areas should be passed successfully.

Note to non-LIU students (FAQ): If you want to do a thesis project with us, you must be registered on a master (or bachelor) program at Linköping university. It is generally not possible to do such projects remotely.

  • [TAKEN] Autotuning hybrid CPU-GPU execution of high-level parallel programs in SkePU (30hp)

    SkePU is a C++ based open-source skeleton programming library for portable high-level programming of heterogeneous multicore systems, being developed by our group at Linköping University in the context of two EU FP7 projects. The different back-ends (basically, implementation variants) provided for the SkePU skeletons allow it to support different types of processing units (PUs), which also opens for automated tuning of the execution flow by selecting the expected fastest implementation at runtime depending on the execution context. The public SkePU distribution currently supports multicore CPUs and GPUs, and there are also experimental back-ends for other parallel architectures.
    An earlier version of SkePU already supports hybrid execution using both CPU and GPU in parallel, see Dastgeer et al. 2011. This project will thoroughly redesign the mechanism for hybrid execution and investigate options for its automated tuning to improve flexibility and reduce run-time overhead. For further information on this project please contact us directly.
    Prerequisites: Multithreaded (OpenMP) and GPU (CUDA, OpenCL) programming (e.g. TDDD56), advanced C++ programming skills, interest in data structures, algorithms, and parallel computer architecture.

    Contact: Christoph Kessler, August Ernstsson.

  • Smarter Containers in SkePU (30hp)

    SkePU is a C++ based open-source skeleton programming library for portable high-level programming of heterogeneous multicore systems, being developed by our group at Linköping University in the context of two EU FP7 projects. The different back-ends (basically, implementation variants) provided for the SkePU skeletons allow it to support different types of processing units (PUs), which also opens for automated tuning of the execution flow by selecting the expected fastest implementation at runtime depending on the execution context. The public SkePU distribution currently supports multicore CPUs and GPUs, and there are also experimental back-ends for MPI and for Movidius Myriad1 developed in previous thesis projects.
    An important feature in SkePU are the so-called smart containers, currently Vector and Matrix, which are generic, STL-like abstractions of aggregate data that transparently perform optimizations of data transfer and memory management at runtime, implementing a generalized software caching scheme with sequential memory consistency. For details, see a recent article or Chapter 4 of Dastgeer's PhD thesis.
    In this project you will extend the smart container idea and implementation to include multiple different representations of data that have different performance implications on different types of execution units (e.g. CPU, GPU). For further information please contact us directly.
    Prerequisites: Multithreaded (OpenMP) and GPU (CUDA, OpenCL) programming (e.g. TDDD56), advanced C++ programming skills, interest in data structures, algorithms, and parallel computer architecture.

  • Smart Copying Techniques for Smart Matrix Containers in SkePU (30hp)
    An important feature in SkePU are the so-called smart containers, currently Vector and Matrix, which are generic, STL-like abstractions of aggregate data that transparently perform optimizations of data transfer and memory management at runtime, implementing a generalized software caching scheme with sequential memory consistency. For details, see a recent article or Chapter 4 of Dastgeer's PhD thesis.
    At read or write accesses to vector/matrix elements, smart containers may trigger data copy operations to update stale local copies of elements before being accessed. For that, a copy plan is calculated to reduce transfer costs. However, the current solution and implementation for 2D data (Matrix container) still has ample room for improvements. In this project you will develop, implement and evaluate smart copying techniques to speed up the coherence copying operations at submatrix accesses.
    This is a research-oriented project. If the result looks publishable, we will encourage you to jointly write and submit a research paper to a conference and support your presentation.
    Prerequisites: Multithreaded (OpenMP) and GPU (CUDA, OpenCL) programming (e.g. TDDD56), advanced C++ programming skills, interest in optimization.

  • Support for generalized stencil computations in SkePU (30hp)
    SkePU is an open-source C++ template library for portable and efficient high-level programming of GPU-based systems, using so-called skeletons. A skeleton is a generic software component modeling a specific pattern of computation; its implementation encapsulates platform-specific technical details such as parallelism and accelerator handling, communication, synchronization etc., while exposing a sequential programming interface to the programmer. SkePU currently provides one task-parallel and a number of data-parallel skeletons, including one that models stencil computations, i.e., computations that update each element of a matrix or image as a filter operation applied to its nearest neighbor elements.
    This project will, as a case study, consider an open-source high-performance computing application from medical image processing that is currently implemented in C++ and CUDA, investigate the requirements for expressing its performance-critical parts with existing (SkePU) skeletons, and develop the possibly required extensions to the SkePU library that allow to more conveniently express the application with SkePU skeletons.
    Prerequisites: TDDD56 Multicore and GPU Programming, or similar course on parallel programming. Advanced C/C++ programming skills.
    Contact: Christoph Kessler

  • Systematic Concurrent Debugging (30hp or 2x30hp)

    Contact: Ahmed Rezine or Christoph Kessler

  • Dynamic Optimization of Interprocessor Communication in the MPI Back-End of the SkePU skeleton programming library (30hp)
    By harnessing the computational power of modern GPUs via General-Purpose Computing on Graphics Processing Units (GPGPU), very fast calculations can be performed with a GPU cluster.
    This thesis project is about extending an existing MPI cluster back-end implementation of the SkePU skeleton programming library by data types that allow for the dynamic optimization of inter-node communication, and evaluating the implementation with several test programs including a computationally intensive application.
    The overall problem includes developing methods for determining the optimal partitioning of the problem, automated performance tuning for the best use of resources, possibly in a non-dedicated environment; also, devising new SkePU skeletons for some computations / communication patterns in the considered scientific computing problem. An application from computational fluid dynamics will be used as a case study.
    This Master thesis project covers the following tasks:
    - Research survey of related work.
    - Design and implementation of new skeleton backends in C/C++, MPI and CUDA/OpenCL.
    - Skeleton-based refactoring of the given benchmark application and experimental evaluation.
    - Documentation of the results in thesis report.
    Begin: ASAP.
    Prerequisites: Courses in programming of parallel computers and GPU computing (TDDC78 and TDDD56 or equivalent). Good background in OpenCL, CUDA, MPI, C/C++, algorithms, Linux.
    Contact: Christoph Kessler.
  • Sparse-Matrix support for the SkePU library for portable CPU/GPU programming (30hp)
    This thesis project will build upon a previous thesis project in our group and extend the sparse-matrix container functionality and optimizations in the SkePU library for high-level, portable programming of GPU-based systems, which was developed in our group.
    A matrix is called sparse if most of its entries are zeroes such that a compressed storage format is more time and space efficient than the traditional 2D array representation. In this master thesis project you will extend SkePU with improved support for sparse matrix computations. In particular, you will design a smart container data structure for the representation and consistent access to generic 2D sparse matrices, and implement several of the data-parallel skeletons of SkePU so that they can be applied to sparse matrices in the same way as to dense matrices, with back-ends in sequential C++, OpenMP, CUDA and OpenCL. The implementation will be evaluated quantitatively on several GPU based platforms, using an iterative linear equation system solver as example application. Further information is available on request, see the contact information below.
    The library is developed in C++, OpenMP, and has implementations for CUDA and OpenCL. The prerequisites for this Master thesis project are good C++ programming skills and knowledge of GPU and parallel programming (e.g., TDDD56 and TDDC78).
    This is a research oriented project.
    Contact: Christoph Kessler.

Further thesis projects in compiler technology and parallel programming
on request (chrke at ida.liu.se).


External Thesis Projects

in cooperation with local industry partners
  • Performance optimization of security functions in IoT devices (30hp)

    See separate project description. In cooperation with Ericsson Research, Lund.
    Prerequisites: Solid background in computer networks, security, embedded systems, multicore architecture and programming, compilers, and C/C++. Ability to work independently.


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Last updated: 2017-12-13