Thông tin chung
  English
  Đề tài NC khoa học
  Bài báo, báo cáo khoa học
  Hướng dẫn Sau đại học
  Sách và giáo trình
  Các học phần và môn giảng dạy
  Giải thưởng khoa học, Phát minh, sáng chế
  Khen thưởng
  Thông tin khác
  Tài liệu tham khảo
  Hiệu chỉnh
 
Số người truy cập: 107,083,328

 Move Based Algorithm for Runtime Mapping of Dataflow Actors on Heterogeneous MPSoCs
Tác giả hoặc Nhóm tác giả: Thanh Dinh Ngo, Kevin J. M. Martin, Jean-Philippe Diguet
Nơi đăng: Journal of Signal Processing Systems for Signal, Image, and Video Technology: Springer Science
cvs weekly sale shauneutsey.com prescription savings cards
; Số: ISSN: 1939-8018 (Print) 1939-8115 (Online);Từ->đến trang: 00:00;Năm: 2015
Lĩnh vực: Khoa học; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Considering the evolution towards highly variable data flow applications based on an increasing impact of dynamic actors, we must target at runtime the best matching between dataflow graphs and heterogeneous multiprocessor platforms. Thus the mapping must be dynamically adapted depending on data and on communication loads between the computation cores. This is typically the case for mobile devices that run multimedia applications. The problem of mapping a dataflow application, e.g. a network of computational actors, on a multiprocessor platform can be modeled as a problem of partitioning where the cells are the dataflow actors and the partitions are the processors. While the benefit of executing a computational part by one processor rather than another one is usually well shown, the migration overhead is also usually not considered. This paper presents a dynamic mapping algorithm that is performed at runtime, based on a single-move possibility that jointly considers the cost and benefit of possible migrations. The method is first applied on a set of randomly generated benchmarks with different features and different scenarios. Then it is applied to a MPEG4 simple profile video decoder with different input sequences. The results systematically show that the runtime mapping significantly improves the initial mapping. It is fast enough to be executed at runtime in order to track the best mapping according to data variations. The other observation is that not considering the migration cost of the new mapping could lead to worst performance than the original one.
ABSTRACT
Considering the evolution towards highly variable data flow applications based on an increasing impact of dynamic actors, we must target at runtime the best matching between dataflow graphs and heterogeneous multiprocessor platforms. Thus the mapping must be dynamically adapted depending on data and on communication loads between the computation cores. This is typically the case for mobile devices that run multimedia applications. The problem of mapping a dataflow application, e.g. a network of computational actors, on a multiprocessor platform can be modeled as a problem of partitioning where the cells are the dataflow actors and the partitions are the processors. While the benefit of executing a computational part by one processor rather than another one is usually well shown, the migration overhead is also usually not considered. This paper presents a dynamic mapping algorithm that is performed at runtime, based on a single-move possibility that jointly considers the cost and benefit of possible migrations. The method is first applied on a set of randomly generated benchmarks with different features and different scenarios. Then it is applied to a MPEG4 simple profile video decoder with different input sequences. The results systematically show that the runtime mapping significantly improves the initial mapping. It is fast enough to be executed at runtime in order to track the best mapping according to data variations. The other observation is that not considering the migration cost of the new mapping could lead to worst performance than the original one.
© Đại học Đà Nẵng
 
 
Địa chỉ: 41 Lê Duẩn Thành phố Đà Nẵng
Điện thoại: (84) 0236 3822 041 ; Email: dhdn@ac.udn.vn