TY - GEN
T1 - MemXCT
T2 - 2019 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2019
AU - Hidayetolu, Mert
AU - Biçer, Tekin
AU - De Gonzalo, Simon Garcia
AU - Ren, Bin
AU - Gürsoy, Doa
AU - Kettimuthu, Rajkumar
AU - Foster, Ian T.
AU - Hwu, Wen Mei W.
N1 - This material was partially supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research and Basic Energy Sciences, under Contract DE-AC02-06CH11357. We gratefully acknowledge the computing resources provided and operated by the Argonne Leadership Computing Facility, which is a U.S. Department of Energy, Office of Science User Facility. We thank Vincent De Andrade from Advanced Photon Source for sharing the mouse brain dataset. This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. This research is based in part upon work supported by: The Center for Applications Driving Architectures (ADA), a JUMP Center co-sponsored by SRC and DARPA. This material is based in part upon work supported by the XPACC Center for Exascale Simulation of Plasma-Coupled Combustion and Department of Energy, under Award Number DE-NA0002374.
PY - 2019/11/17
Y1 - 2019/11/17
N2 - X-ray computed tomography (XCT)is used regularly at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be terabyte-scale. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use exception rather than the rule. We propose here a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures incolving KNL and GPU. MemXCT can reconstruct a large (11K×11K) mouse brain tomogram in ∼10 seconds using 4096 KNL nodes (256K cores), the largest iterative reconstruction achieved in near-real time.
AB - X-ray computed tomography (XCT)is used regularly at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be terabyte-scale. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use exception rather than the rule. We propose here a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures incolving KNL and GPU. MemXCT can reconstruct a large (11K×11K) mouse brain tomogram in ∼10 seconds using 4096 KNL nodes (256K cores), the largest iterative reconstruction achieved in near-real time.
UR - https://www.scopus.com/pages/publications/85076139062
UR - https://www.scopus.com/pages/publications/85076139062#tab=citedBy
U2 - 10.1145/3295500.3356220
DO - 10.1145/3295500.3356220
M3 - Conference contribution
AN - SCOPUS:85076139062
T3 - International Conference for High Performance Computing, Networking, Storage and Analysis, SC
BT - Proceedings of SC 2019
PB - IEEE Computer Society
Y2 - 17 November 2019 through 22 November 2019
ER -