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A Cloud Based Super-Optimization Method to Parallelize the Sequential Code’s Nested Loops
Åbo Akademi, Finland.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
University of Turku, Finland.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
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2019 (English)In: IEEE 13th International Symposium on Embedded Multicore/Many-core Systems-on-Chip MCSoC-2019, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Advances in hardware architecture regarding multi-core processors make parallel computing ubiquitous. To achieve the maximum utilization of multi-core processors, parallel programming techniques are required. However, there are several challenges standing in front of parallel programming. These problems are mainly divided into three major groups. First, although recent advancements in parallel programming languages (e.g. MPI, OpenCL, etc.) assist developers, still parallel programming is not desirable for most programmers. The second one belongs to the massive volume of old software and applications, which have been written in serial mode. However, converting millions of line of serial codes to parallel codes is highly time-consuming and requiring huge verification effort. Third, the production of software and applications in parallel mode is very expensive since it needs knowledge and expertise. Super-optimization provided by super compilers is the process of automatically determine the dependent and independent instructions to find any data dependency and loop-free sequence of instructions. Super compiler then runs these instructions on different processors in the parallel mode, if it is possible. Super-optimization is a feasible solution for helping the programmer to get relaxed from parallel programming workload. Since the most complexity of the sequential codes is in the nested loops, we try to parallelize the nested loops by using the idea of super-optimization. One of the underlying stages in the super-optimization is scheduling tiled space for iterating nested loops. Since the problem is NP-Hard, using the traditional optimization methods are not feasible. In this paper, we propose a cloud-based super-optimization method as Software-as-a-Service (SaaS) to reduce the cost of parallel programming. In addition, it increases the utilization of the processing capacity of the multi-core processor. As the result, an intermediate programmer can use the whole processing capacity of his/her system without knowing anything about writing parallel codes or super compiler functions by sending the serial code to a cloud server and receiving the parallel version of the code from the cloud server. In this paper, an evolutionary algorithm is leveraged to solve the scheduling problem of tiles. Our proposed super-optimization method will serve as software and provided as a hybrid (public and private) deployment model.

Place, publisher, year, edition, pages
2019.
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-45148OAI: oai:DiVA.org:mdh-45148DiVA, id: diva2:1348846
Conference
IEEE 13th International Symposium on Embedded Multicore/Many-core Systems-on-Chip MCSoC-2019, 01 Oct 2019, Singapore, Sweden
Projects
DeepMaker: Deep Learning Accelerator on Commercial Programmable DevicesAvailable from: 2019-09-05 Created: 2019-09-05 Last updated: 2019-09-05Bibliographically approved

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Loni, MohammadDaneshtalab, Masoud

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