Loop and task parallelization using cloud clusters map-reduce for scientific workloads

• Grant number: 14/25694-8
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: April 01, 2015
• End date: November 30, 2017
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Guido Costa Souza de Araújo
• Grantee: Hervé Yviquel
• Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 13/08293-7 - CCES - Center for Computational Engineering and Sciences, AP.CEPID
• Associated scholarship(s): 17/21339-7 - Loop and task parallelization on heterogeneous cloud clusters using map-reduce for scientific workloads, BE.EP.PD

Abstract

The goal of this project is the creation of a compiler-generated parallel code runtime model for remote, distributed execution in computer clusters of the cloud. The ideas here discussed aim at implementing and consolidating OpenMR, a novel programming model, proposed at IC-Unicamp, that extends the existing Open Multi Processing (OpenMP) Application Program Interface (API), to allow a programmer to transparently parallelise loops for execution in the cloud using MapReduce. The preliminary implementation of an OpenMR prototype demonstrated that the proposal is feasible, as it produced encouraging experimental results, and pointed to a number of novel opportunities for future work, which will de developed under this project. One of the attractiveness of the OpenMR model is the simplicity of its use. Its goal is to provide a seamless programming model to the application programmer. An important insight from prototyping OpenMR is that the cloud can be modeled as an additional level in the computing hierarchy. The idea of a computing hierarchy has been established over many years through the development of computer architecture and a software stack that together execute the code of a computer program. Nevertheless, distributed computation in the cloud has not yet been integrated into this stack because there is a significant utilization gap that hinders the transparent execution of code remotely. For instance, an open problem that must be addressed to better integrate the cloud into this hierarchy is the data movement. With higher communication costs and potentially different programming models, algorithms to optimize this data movement must be investigated. Another problem is related to the investigation of optimization techniques which can hide communication latency by means of pipelined computation, and other parallelisation algorithms.

Scientific publications

(4)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

YVIQUEL, HERVE; CRUZ, LAURO; ARAUJO, GUIDO. Cluster Programming using the OpenMP Accelerator Model. ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION, v. 15, n. 3, OCT 2018. (14/25694-8, 17/21339-7)

MORAIS, LUCAS; SILVA, VITOR; GOLDMAN, ALFREDO; ALVAREZ, CARLOS; BOSCH, JAUME; FRANK, MICHAEL; ARAUJO, GUIDO; ASSOC COMP MACHINERY. Adding Tightly-Integrated Task Scheduling Acceleration to a RISC-V Multi-core Processor. MICRO'52: THE 52ND ANNUAL IEEE/ACM INTERNATIONAL SYMPOSIUM ON MICROARCHITECTURE, v. N/A, p. 12-pg., 2019-01-01. (17/02682-2, 14/25694-8, 18/00687-0)

YVIQUEL, HERV; ARAUJO, GUIDO; IEEE. The Cloud as an OpenMP Offloading Device. 2017 46TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING (ICPP), v. N/A, p. 10-pg., 2017-01-01. (14/25694-8)

MORTATTI, MATHEUS; YVIQUEL, HERVE; ARAUJO, GUIDO; IEEE. Automatic Ray-Tracer Cloud Offloading in OpenMP. 2018 30TH INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE AND HIGH PERFORMANCE COMPUTING (SBAC-PAD 2018), v. N/A, p. 8-pg., 2018-01-01. (17/21339-7, 14/25694-8)