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2018•Unpublished venueRequires access

An Automatic Task Partition Method for Multi-core System

Minge Jing, Yujie Huang, Yibo Edward Fan, Xiaoyong Xue, Xiaoyang Zeng, Zhiyi Yu

Open publisher page 2 citations

Abstract

In this paper, an automated task partition method for multi-core system is proposed. To explore the full parallelism of an application written in sequential languages such as C/C++, we first present a coarse-grain intermediate representation called Function-ANd-Statement (FANS) which takes function call structure as well as statement structure into account. Based on the FANS intermediate representation, we propose a node fusion technique called Stratify And Grain-Controlled Fusion (SAGCF) to partition the whole application into many subtasks with the goal of maximizing parallelism in space and time dimensions as well as minimizing communication. All of these proposed techniques are implemented in an open source Automatic Task Partition Framework (ATPF). Finally, the feasibility of the proposed method is demonstrated by several cases.

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What this paper is about

In this paper, an automated task partition method for multi-core system is proposed. To explore the full parallelism of an application written in sequential languages such as C/C++, we first present a coarse-grain intermediate representation called Function-ANd-Statement (FANS) which takes function call structure as well as statement structure into account. Based on the FANS intermediate representation, we propose a node fusion technique called Stratify And Grain-Controlled Fusion (SAGCF) to partition the whole application into many subtasks with the goal of maximizing parallelism in space and time dimensions as well as minimizing communication. All of these proposed techniques are implemented in an open source Automatic Task Partition Framework (ATPF). Finally, the feasibility of the proposed method is demonstrated by several cases.

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Available abstract

In this paper, an automated task partition method for multi-core system is proposed. To explore the full parallelism of an application written in sequential languages such as C/C++, we first present a coarse-grain intermediate representation called Function-ANd-Statement (FANS) which takes function call structure as well as statement structure into account. Based on the FANS intermediate representation, we propose a node fusion technique called Stratify And Grain-Controlled Fusion (SAGCF) to partition the whole application into many subtasks with the goal of maximizing parallelism in space and time dimensions as well as minimizing communication. All of these proposed techniques are implemented in an open source Automatic Task Partition Framework (ATPF). Finally, the feasibility of the proposed method is demonstrated by several cases.

Key concepts: Computer science, Partition (number theory), Statement (logic), Task (project management), Representation (politics), Intermediate language, Parallelism (grammar), Task analysis

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