| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Estadual Campinas, UNICAMP, Inst Comp, Av Albert Einstein 1251, Cidade Univ, Campinas, SP - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION; v. 15, n. 3 OCT 2018. |
| Web of Science Citations: | 1 |
| Abstract | |
Computation offloading is a programming model in which program fragments (e.g., hot loops) are annotated so that their execution is performed in dedicated hardware or accelerator devices. Although offloading has been extensively used to move computation to GPUs, through directive-based annotation standards like OpenMP, offloading computation to very large computer clusters can become a complex and cumbersome task. It typically requires mixing programming models (e.g., OpenMP and MPI) and languages (e.g., C/C++ and Scala), dealing with various access control mechanisms from different cloud providers (e.g., AWS and Azure), and integrating all this into a single application. This article introduces computer cluster nodes as simple OpenMP offloading devices that can be used either from a local computer or from the cluster head-node. It proposes a methodology that transforms OpenMP directives to Spark runtime calls with fully integrated communication management, in a way that a cluster appears to the programmer as yet another accelerator device. Experiments using LLVM 3.8, OpenMP 4.5 on well known cloud infrastructures (Microsoft Azure and Amazon EC2) show the viability of the proposed approach, enable a thorough analysis of its performance, and make a comparison with an MPI implementation. The results show that although data transfers can impose overheads, cloud offloading from a local machine can still achieve promising speedups for larger granularity: up to 115x in 256 cores for the 2MM benchmark using 1GB sparse matrices. In addition, the parallel implementation of a complex and relevant scientific application reveals a 80x speedup on a 320 core machine when executed directly from the headnode of the cluster. (AU) | |
| FAPESP's process: | 14/25694-8 - Loop and task parallelization using cloud clusters map-reduce for scientific workloads |
| Grantee: | Hervé Yviquel |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 17/21339-7 - Loop and task parallelization on heterogeneous cloud clusters using map-reduce for scientific workloads |
| Grantee: | Hervé Yviquel |
| Support Opportunities: | Scholarships abroad - Research Internship - Post-doctor |