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| Author(s): |
Rodolfo Guilherme Wottrich
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | Campinas, SP. |
| Institution: | Universidade Estadual de Campinas (UNICAMP). Instituto de Computação |
| Defense date: | 2014-09-04 |
| Examining board members: |
Guido Costa Souza de Araújo;
Alexandro José Baldassin;
Luiz Fernando Bittencourt
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| Advisor: | Guido Costa Souza de Araújo; Rodolfo Jardim de Azevedo |
| Abstract | |
The pursuit of parallelism has always been an important goal in the design of computer systems, driven mainly by the constant interest in reducing program execution time. Parallel programming is an active research area, which has grown in interest due to the emergence of multicore architectures. On the other hand, harnessing the large computing and storage capabilities of the cloud and its desirable flexibility and scaling features offers a number of interesting opportunities to address some relevant research problems in scientific computing. Unfortunately, in many cases the implementation of applications on the cloud demands specific knowledge of parallel programming interfaces and APIs, which may become a burden when programming complex applications. To overcome such limitations, in this work we propose OpenMR, an execution model based on the syntax and principles of the OpenMP API which eases the task of programming distributed systems (i.e. local clusters or remote cloud). Specifically, this work addresses the problem of performing loop parallelization, using OpenMR, in a distributed environment, through the mapping of loop iterations to MapReduce nodes. By doing so, the cloud programming interface becomes the programming language itself, freeing the developer from the task of worrying about the details of distributing workload and data. To assess the validity of the proposal, we modified benchmarks from the SPEC OMP2012 suite to fit the proposed model, developed other I/O-bound toy benchmarks and executed them in two settings: (a) a computer cluster locally available through a standard LAN; and (b) clusters remotely available through the Amazon AWS services. We compare the results to the execution using OpenMP in an SMP architecture and show that the proposed parallelization technique is feasible and demonstrates good scalability (AU) | |
| FAPESP's process: | 12/17278-9 - Optimization techniques for instruction issue targeting processor energy efficiency |
| Grantee: | Rodolfo Guilherme Wottrich |
| Support Opportunities: | Scholarships in Brazil - Master |