This work focuses on forecasting future license usage for high-performance computing environments and using such predictions to improve the effectiveness of job scheduling. Specifically, we propose a model that carries out both short-term and long-term license usage forecasting and a method of using forecasts to improve job scheduling. Our long-term forecasting model achieves a Mean Absolute Percentage Error (MAPE) as low as 0.26 for a 12-month forecast of daily peak license usage. Our job scheduling experimental results also indicate that wasted work from jobs with insufficient licenses can be reduced by up to 92% without increasing the average license-using job completion times, during periods of high license usage, with our proposed license-aware scheduler.

MOTIVATION: Automated profiling of cell-cell interactions from high-throughput time-lapse imaging microscopy data of cells in nanowell grids (TIMING) has led to fundamental insights into cell-cell interactions in immunotherapy. This application note aims to enable widespread adoption of TIMING by (i) enabling the computations to occur on a desktop computer with a graphical processing unit instead of a server; (ii) enabling image acquisition and analysis to occur in the laboratory avoiding network data transfers to/from a server and (iii) providing a comprehensive graphical user interface. RESULTS: On a desktop computer, TIMING 2.0 takes 5 s/block/image frame, four times faster than our previous method on the same computer, and twice as fast as our previous method (TIMING) running on a Dell PowerEdge server. The cell segmentation accuracy (f-number = 0.993) is superior to our previous method (f-number = 0.821). A graphical user interface provides the ability to inspect the video analysis results, make corrective edits efficiently (one-click editing of an entire nanowell video sequence in 5-10 s) and display a summary of the cell killing efficacy measurements. AVAILABILITY AND IMPLEMENTATION: Open source Python software (GPL v3 license), instruction manual, sample data and sample results are included with the Supplement (https://github.com/RoysamLab/TIMING2). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary Characterization of rock properties is vital in producing oil and gas from shale reservoirs in an economically viable fashion. The nano‐pore structure and ultralow permeability in shale reservoirs present challenges to the traditional experimental characterization methods. Digital rock physics for the estimation of rock properties, especially for shale reservoirs, has become a powerful tool that greatly complements to lab experiments by combining advance imaging techniques with numerical simulations. The lattice Boltzmann method (LBM) is a well‐applied numerical method to simulate the fluid flow in pore structures at multiple length scales. Usually, the LBM simulation is resource intense because of its computation complexity and is facing great numerical challenges in extremely large‐cale computation. In this paper, we propose a multi‐GPU parallel implementation of 3D LBM on a hybrid high‐performance computing cluster to perform large‐scale simulations in reconstructed digital rocks. The program provides multiscale solution, pore scale and representative elementary volume (REV) scale based on the resolution of digital rock images. Optimization strategies are applied on partitioning simulation domain, improving data communication efficiency and maximizing CUDA occupancy. When running on a cluster of 32 GPUs, the proposed parallel implementation achieves a speedup of 1074x comparing to the in‐house sequential program.