Currently, we are developing the user-PC computing (UPC) system based on the master-worker model as a scalable, low-cost, and high-performance computing platform. To run various application programs on personal computers (PCs) with different environments for workers, it adopts Docker technology to bundle every necessary software as one image file. Unfortunately, the Docker file/image are manually generated through multiple steps by a user, which can be the bottleneck. In this paper, we present a web-based Docker image assistant generation (DIAG) tool in the UPC system to assist or reduce these process steps. It adopts Angular JavaScript for offering user interfaces, PHP Laravel for handling logic using RestAPI, MySQL database for storing data, and Shell scripting for speedily running the whole program. In addition, the worker-side code modification function is implemented so that a user can modify the source code of the running job and update the Docker image at a worker to speed up them. For evaluations, we collected 30 Docker files and 10 OpenFOAM jobs through reverse processing from Docker images in Github and generated the Docker images using the tool. Moreover, we modified source codes for network simulations and generated the Docker images in a worker five times. The results confirmed the validity of the proposal.

Currently, the User-PC computingsystem (UPC) has been studied as a low-cost and high-performance distributed computing platform. It uses idling resources of personal computers (PCs) in a group. The job-worker assignment for minimizing makespan is critical to determine the performance of the UPC system. Some applications need to execute a lot of uniform jobs that use the identical program but with slightly different data, where they take the similar CPU time on a PC. Then, the total CPU time of a worker is almost linear to the number of assigned jobs. In this paper, we propose a static assignment algorithm of uniform jobs to workers in the UPC system, using simultaneous linear equations to find the lower bound on makespan, where every worker requires the same CPU time to complete the assigned jobs. For the evaluations of the proposal, we consider the uniform jobs in three applications. In OpenPose, the CNN-based keypoint estimation program runs with various images of human bodies. In OpenFOAM, the physics simulation program runs with various parameter sets. In code testing, two open-source programs run with various source codes from students for the Android programming learning assistance system (APLAS). Using the proposal, we assigned the jobs to six workers in the testbed UPC system and measured the CPU time. The results show that makespan was reduced by $10\%$ on average, which confirms the effectiveness of the proposal.