Accelerating Sequence Calculations on Parallel GPU Architecture

Hossain, Roksana

Accelerating Sequence Calculations on Parallel GPU Architecture

dc.contributor.advisor	Magierowski, Sebastian K.
dc.contributor.advisor	Messier, Geoffrey G.
dc.contributor.author	Hossain, Roksana
dc.contributor.committeemember	Nielsen, Jørgen S.
dc.contributor.committeemember	Yanushkevich, Svetlana N.
dc.date	2019-06
dc.date.accessioned	2019-03-01T23:13:37Z
dc.date.available	2019-03-01T23:13:37Z
dc.date.issued	2019-02-28
dc.description.abstract	In this thesis, I have implemented a GPU (graphics processor unit) based sequencing algorithm that finds a sequence or order among data points to optimize a given objective. I have studied the sequencing algorithm as a path planner for an unmanned aerial vehicle (UAV) and also, a basecaller for a miniature DNA sequencer. Parallel implementation utilizing GPU enables faster processing and decision making that are important when data is quite large and a real-time response is critical e.g., in UAV based transportation. The goal of using UAV in my thesis is to construct a wireless sensor network in a remote location by deploying wireless sensor nodes. The proposed path planner, also known as a sequencer, is designed to find the shortest path that is also a safe path using travelling salesman problem. A path is considered safe when the vehicle would not collide with any obstacle. Two sets of heuristic algorithms, one for generating a sequence of waypoints (sequence generator) and another one for constructing a path between two waypoints (path explorer), are used to find the near optimal solution. The highly-parallel multicore GPU is used for the real-time implementation that offloads compute-intensive portions from the traditional CPU to the GPU to make the decision-making process faster. In this thesis, the parallel execution of the sequence generator and the path explorer achieved a 4.82x and 164x speed-up compared to the CPU-only approach respectively. The second sequencer is a palm-sized miniature DNA sequencer, the so-called MinION device. In the case of the MinION, a vast multitude of DNA strands are introduced into the device and converted into noisy electronic time-series signals; these measurements are essentially physical signatures related to the molecular make-up of the sensed DNA. Among a long "pipeline" of analysis steps to be performed on such measurement sequences, the first, and arguably most intensive, is the so-called basecalling step which analyzes the time-series and converts it into the equivalent monomeric base of the DNA under test using the Viterbi algorithm.	en_US
dc.identifier.citation	Hossain, R. (2019). Accelerating sequence calculations on parallel GPU architecture (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/36155
dc.identifier.uri	http://hdl.handle.net/1880/109923
dc.language.iso	en	en_US
dc.publisher.faculty	Schulich School of Engineering	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	GPU	en_US
dc.subject	parallel computing	en_US
dc.subject	path planner	en_US
dc.subject	basecalling	en_US
dc.subject.classification	Artificial Intelligence	en_US
dc.subject.classification	Engineering	en_US
dc.subject.classification	Robotics	en_US
dc.title	Accelerating Sequence Calculations on Parallel GPU Architecture	en_US
dc.type	doctoral thesis	en_US
thesis.degree.discipline	Engineering – Electrical & Computer	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Doctor of Philosophy (PhD)	en_US
ucalgary.item.requestcopy	true