Presentation
Enhancing Scientific Discovery via Reliability vs Performance Trade-Offs for Scientific Computing at Sun-Earth L2
DescriptionNASA's Habitable Worlds Observatory (HWO) will directly image Earth-like exoplanets by suppressing starlight by a factor of 10−10 using a coronagraph instrument. Maintaining this suppression requires a closed-loop control system—high-order wavefront sensing and control (HOWFSC)—that continuously corrects optical aberrations by commanding deformable mirrors. The dominant computational kernel is a dense matrix-vector multiply (GEMV) with a precomputed gain matrix exceed- ing 106 GB in double precision at flight scale. The memory bandwidth demanded by this kernel at the required control frequency exceeds radiation-hardened processors by orders of magnitude, motivating deployment on commercial off-the-shelf (COTS) hardware aboard a co-flying satellite at Sun-Earth L2—outside Earth's magnetosphere, where single-event upsets (SEUs) from galactic cosmic rays and solar particles can corrupt computation. We apply Algorithm-Based Fault Tolerance (ABFT) to protect this GEMV. The gain matrix is severely ill-conditioned (singular values spanning 66 decades), causing checksum noise floors that challenge naive ABFT. We address this with row- scaling preconditioning and analytically derived Higham-bound adaptive thresholds that require no empirical tuning. Using physically realistic gain matrices from a FALCO coronagraph model at two actuator scales, we demonstrate 100% detection of all science-threatening faults with zero false positives and ∼6 orders of magnitude of margin between the noise floor and the dangerous-fault threshold. We further show that the gain matrix can be stored in reduced precision—as few as 23 mantissa bits with block floating point—reducing memory by 62% while consuming less than 0.03% of the contrast error budget.
Event Type
Research Special Session
TimeTuesday, July 2812:00pm - 12:30pm PDT
LocationMtg Room 201A