OPR

The ability to reproduce a parallel execution is desirable for debugging and program reliability purposes. In debugging (13), the programmer needs to manually step back in time, while for resilience (6) this is automatically performed by the the application upon failure. To be useful, replay has to faithfully reproduce the original execution. For parallel programs the main challenge is inferring and maintaining the order of conflicting operations (data races). Deterministic record and replay (R&R) techniques have been developed for multithreaded shared memory programs (5), as well as distributed memory programs (14). Our main interest is techniques for large scale scientific (3; 4) programming models

Parallel processing for scientific computations

The main contribution of the effort in the last two years is the introduction of the MOPPS system. After doing extensive literature search, we introduced the system which is described next. MOPPS employs a new solution to the problem of managing programs which solve scientific and engineering applications on a distributed processing environment. Autonomous computers cooperate efficiently in solving large scientific problems with this solution. MOPPS has the advantage of not assuming the presence of any particular network topology or configuration, computer architecture, or operating system. It imposes little overhead on network and processor resources while efficiently managing programs concurrently. The core of MOPPS is an intelligent program manager that builds a knowledge base of the execution performance of the parallel programs it is managing under various conditions. The manager applies this knowledge to improve the performance of future runs. The program manager learns from experience

On sharing and synchronizing groupware calendars under android platform

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Sharing a calendar of tasks and events is a cornerstone in collaborative group work. Indeed, the individual work of the members of the group as well as the group work as a whole need the calendar to guide their activity and to meet the deadlines, milestones, deliverables of a project, etc. Additionally the members of the group should be able to work both offline and online, which arises when members of the group use smartphones and can eventually run out of Internet connection from time to time, or simply want to develop some activities locally. In the former case, they should have access to the calendar locally, while in the later case they should access the calendar online, shared by all members of the group. In both cases they should be able to see eventually the same information, namely the local calendars of the members should be synchronized with the group calendar. For the case of smartphones under Android system, one solution could be using the Google calendar, however, that is not easily tailorable to collaborative group work. In this paper we present an analysis, design and implementation of group work calendar that meets several requirements such as 1) sharing among all of members of the group, 2) synchronization among local calendars of members and global group calendar, 3) conflict resolution through a voting system, 4) awareness of changes in the entries (tasks, members, events, etc.) of the calendar and 5) all these requirements under proper privacy, confidentiality and security mechanisms. Moreover, we extend the sharing of calendars among different groups, a situation which often arises in enterprises when different groups need to be aware of other projects' development, or, when some members participate in more than one project at the same time.Peer ReviewedPostprint (author's final draft