Details of Grant 

EPSRC Reference:	EP/N024494/1
Title:	Exploration of machine learning for pre-emptive scheduling in single-instruction multiple-data mega-kernel designs
Principal Investigator:	Kainz, Dr B
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department:	Computing
Organisation:	Imperial College London
Scheme:	First Grant - Revised 2009
Starts:	01 September 2016	Ends:	31 October 2017	Value (£):	98,669
EPSRC Research Topic Classifications:	Artificial Intelligence Medical Imaging Parallel Computing
EPSRC Industrial Sector Classifications:	Healthcare
Related Grants:
Panel History:	Panel Date Panel Name Outcome 15 Mar 2016 EPSRC ICT Prioritisation Panel - Mar 2016 Announced
Summary on Grant Application Form
This project is about exploring the question if we can use machine learning to make parallel computing units like graphics processing units (GPUs) more efficient. GPUs have become powerful parallel processors, but using this power is often difficult. Currently, GPUs are mostly used for algorithms that are easy to parallelize. However, I believe that it is possible for more algorithms to benefit from the power of GPUs, if we offer new ways for GPU programming and more intelligent task scheduling strategies. In the past I have been researching novel frameworks to allow the parallelisation of computational methods that are usually hard to translate for an execution of parallel hardware like GPUs. For that I used a parallel programming concept called Mega-Kernel. The essence of this concept is that a few computing units out of several thousand are responsible for scheduling computational tasks with different priorities. The Mega-Kernel concept has been demonstrated to provide a powerful extension of conventional kernel based program execution management that can deliver significant performance enhancement from single instruction multiple data (SIMD) approaches. However, to date the queue optimisation capabilities that are at the core of the approach use static rule based decision processes and in particular do not provide optimal hardware utilization or automatic intelligent preemptive scheduling. There are many real world applications for which performance could potentially be transformed by more dynamically adaptive scheduling capabilities and the SIMD architecture itself provides an opportunity to realise this. In this project we will explore statistical machine learning at the parallel hardware level to automatically predict the priorities of tasks in complex real world data analysis tasks such as the reconstruction and motion correction of n-dimensional motion corrupted medical image data. In particular, we will explore the real-time capabilities of machine learning supported preemptively scheduled reconstruction for the direct integration of motion correction into the scan process of fetal magnetic resonance data. Motion correction is the only way to provide comprehensive investigation of all fetal organs at high resolution. However, the currently used algorithmic pipeline is unidirectional, slow and consists of error-prone post-scan-processing steps. The lack of interactivity makes manual corrections or an integration into the scan process impossible at the moment. Automatically prioritised preemptive scheduling of the algorithm's tasks on commodity hardware like GPUs will likely provide a way to introduce real-time capabilities for such extremely complex computing methods. It will be feasible during the 14 months of this project to explore if machine learning could be an option to predict the hardware utilisation of the tasks of a complex example algorithm like motion correction with potential corrective user input. If successful, the results of this project are likely to introduce a new paradigm in high-performance computing and will contribute to a paradigm-shift in medical image acquisition of moving objects.
Key Findings
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Potential use in non-academic contexts
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Organisation Website:	http://www.imperial.ac.uk