ACLS International Internship Report Norwich Mungkalaton @ University of Edinburgh

University of Edinburgh Internship term: 1 October 2017 to 11 March 2018 University was founded on 1582 - 6th oldest. Notable Alums and Staff Charles Darwin James Clerk Maxwell Alexander Graham Bell King’s Building Campus— James Clerk Maxwell Building House #1 Central Campus King’s Building Campus House #2

University of Edinburgh Supervisor Assistant Professor Dr. Bartlomiej Waclaw Royal Society of Edinburgh Research Fellow

University of Edinburgh School of Physics and Astronomy Institute of Condensed Matters and Complex Systems Physics of Living Matters (Bacteria + Cancer) Statistical Physics and Complexity Population Dynamics Group Dr. Bartlomiej Waclaw

Cancer Growth Simulation Waclaw, B. Nature 2015

Cancer Growth Simulation Paul Quast @ Dr. Bartek Waclaw website

Cancer Growth Simulation 4 Mechanisms of Tumor Evolution Replication – Based on available site, (normal cell/matrix), cancer cell can choose to replicate itself to (fixed position) nearest neighbors. Mutation – The mother cell can induce genetic alteration to the daughter cell. Mutation can be driver mutation or passenger mutation. Dispersal – Cell can detach from the current metastatic lesion and form another lesion and reattach to the main lesion. To find new nutrition resources. Cell Death – Cell can decide to die if there is no more space or nutrition resources have been depleted. Metastatic lesion can also be targeted for treatment.

Cancer Growth Simulation Primers 2D/3D Real time simulation of tumor evolution Stochastic Simulation Lattice-based model Eden Growth Cluster CPU Randomly picked a point in linear 1d array Randomly growing the cluster based on growth probability Cluster can grow dynamically GPU Cluster is fixed size (Power of 2) Need to ask for fixed lattice before execution. 1 Thread = 1 Cell location (2D) 1 Thread = Sub-lattice (3D) 7-10 times performance gains Jason Miller Wired 2017

Cancer Growth Simulation CPU is optimized for serialized execution (low latency/delay) GPU is optimized for high-throughput execution (Multi-thread) NVIDIA

Cancer Growth Simulation Checkerboard decomposition 2D Lattice in GPU yoffset = Height of block xoffset = Width of block y All cell read/write asynchronously. Need to call device synchronize to wait for other block update. XOR update pattern x Dr. Martin Weigel - Winter school on GPU computing (2011)

Cancer Growth Simulation 3D Lattice-based Simulation Need to convert 3d coordinates to 2d texture map. Threads/Blocks/Grids will now responsible for square space read/write. Not efficient due to unable to perform coalesced memory access. GPU Computing Gems 3rd Edition

Cancer Growth Simulation Project Summary Cancer growth + Genotype Mutation + Cell Death (Treatment) Multi-GPU Algorithm GPU struggles with non-deterministic execution model. How can we reduce the non-deterministic process such as random growth to (somewhat) deterministic. Replication Mutation Real-time rendering + Tesla C2070 GTX470 Dr. Bartek Waclaw website

Cancer Growth Simulation 2D Lattice-based Simulation

Cancer Growth Simulation 3D Lattice-based Simulation

Cancer Growth Simulation 3D Lattice-based Simulation (Treatment)

Living & Working in Edinburgh Lessons Learned Mentorship Creativity & Originality Collaboration Research Methodologies Work Critique

THANK YOU ACLS Committee for this Opportunity ACLS Staff - Kumagai-san Prof. Akihiko Konagaya Dr. Bartlomiej Waclaw & Prof. Rosalind Allen