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

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

Exa-scale HPC depends on “hardware accelerators” (Among Top 10 supercomputer as of June 17, 2013) rankName RmaxRpeak Config 1 Tianhe PF54.9 PF 32,000 Intel Xeon E (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

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

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

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

Long-term Vision: ARCHER - A Testbed (Accelerated Radiation-transport Computations in Heterogeneous EnviRonments)