Asynchronous and corrected-asynchronous numerical solutions of parabolic PDES on MIMD multiprocessors

A major problem in achieving significant speed-up on parallel machines is the overhead involved with synchronizing the concurrent process. Removing the synchronization constraint has the potential of speeding up the computation. The authors present asynchronous (AS) and corrected-asynchronous (CA) finite difference schemes for the multi-dimensional heat equation. Although the discussion concentrates on the Euler scheme for the solution of the heat equation, it has the potential for being extended to other schemes and other parabolic partial differential equations (PDEs). These schemes are analyzed and implemented on the shared memory multi-user Sequent Balance machine. Numerical results for one and two dimensional problems are presented. It is shown experimentally that the synchronization penalty can be about 50 percent of run time: in most cases, the asynchronous scheme runs twice as fast as the parallel synchronous scheme. In general, the efficiency of the parallel schemes increases with processor load, with the time level, and with the problem dimension. The efficiency of the AS may reach 90 percent and over, but it provides accurate results only for steady-state values. The CA, on the other hand, is less efficient, but provides more accurate results for intermediate (non steady-state) values

Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep‐Learning

In in vitro fertilization (IVF) treatments, early identification of embryos with high implantation potential is required for shortening time to pregnancy while avoiding clinical complications to the newborn and the mother caused by multiple pregnancies. Current classification tools are based on morphological and morphokinetic parameters that are manually annotated using time‐lapse video files. However, manual annotation introduces interobserver and intraobserver variability and provides a discrete representation of preimplantation development while ignoring dynamic features that are associated with embryo quality. A fully automated and standardized classifiers are developed by training deep neural networks directly on the raw video files of >6200 blastulation‐labeled and >5500 implantation‐labeled embryos. Prediction of embryo implantation is more accurate than the current state‐of‐the‐art morphokientic classifier. Embryo classification improves with video length where the most predictive images show only partial association with morphological features. Deep learning substitute to human evaluation of embryo developmental competence thus contributes to implementing single embryo transfer methodology