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A 4-year $2.6 million grant from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), to perform real-time CT imaging dose calculations (2012 2016) 1 Participants: RPI - Xu, Ji, Carothers, and Shephard Mass General Hospital Kalra and Liu GE Global Research FitzGerald LANL - Brown

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Introduction Monte Carlo radiation computing is the gold standard, but time-consuming Traditional parallel schemes use CPUs Multiprocessing multithreading Hardware accelerators are emerging GPU Coprocessor 2

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Exa-scale HPC depends on hardware accelerators (Among Top 10 supercomputer as of June 17, 2013) rankName RmaxRpeak Config 1 Tianhe-2 33.9 PF54.9 PF 32,000 Intel Xeon E5-2692 (12-core) 48,000 Intel Xeon Phi coprocessor 31S1P 2 Titan 17.6 PF27.1 PF 18,688 AMD Opteron 6274 (16-core) 18,688 NVIDIA K20x GPU

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GPU offers: - Massive data-parallel computing power - Cost and energy efficiency - Flexible programming architecture (CUDA) Stream Processors Single Instruction, Multiple Threads (SIMT)

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Preliminary Clinical Results CT images converted to voxelized phantom Patient CT imaging dose calculated by ARCHER - 1 GPU: 7.7 seconds - 6 GPUs: 1.4 seconds real-time speed 5

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DEMO ARCHER in 4s and GPU (12 HT) in 40s 6

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Long-term Vision: ARCHER - A Testbed (Accelerated Radiation-transport Computations in Heterogeneous EnviRonments) www.archer-mc.com