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Persistent URL http://purl.org/net/epubs/work/30453960
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Record Id 30453960
Title Experiments with sparse Choleskyusing a sequential task-flow implementation
Contributors
Abstract We describe the development of a prototype code for the solution of large sparse symmetric positive definite systems that is efficient on parallel architectures. We implement a DAG-based Cholesky factorization that offers good performance and scalability on multicore architectures. Our approach uses a runtime system to execute the DAG. The runtime system plays the role of a software layer between the application and the architecture and handles the management of task dependencies as well as the task scheduling. In this model, the application is expressed using a high-level API, independent of the hardware details, thus enabling portability across different architectures. Although widely used in dense linear algebra, this approach is nevertheless challenging for sparse algorithms because of the irregularity and variable granularity of the DAGs arising in these systems. We assess the ability of two different Sequential Task Flow implementations to address this challenge: one implemented with the OpenMP standard, and the other with the modern runtime system StarPU. We compare these implementations to the state-of-the-art solver HSL MA87 and demonstrate comparable performance on a multicore architecture.
Organisation STFC , SCI-COMP , SCI-COMP-CM
Keywords
Funding Information NLAFET Project, Horizon 2020 Research and Innovation Programme (671633)
Related Research Object(s):
Language English (EN)
Type Details URI(s) Local file(s) Year
Report RAL Technical Reports RAL-TR-2016-016. 2016. RAL-TR-2016-016.pdf 2016
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