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- All Subjects: Computer Engineering
- Genre: Masters Thesis
Description
Stream computing has emerged as an importantmodel of computation for embedded system applications particularly in the multimedia and network processing domains. In recent past several programming languages and embedded multi-core processors have been proposed for streaming applications. This thesis examines the execution and dynamic scheduling of stream programs on embedded multi-core processors. The thesis addresses the problem in the context of a multi-tasking environment with a time varying allocation of processing elements for a particular streaming application. As a solution the thesis proposes a two step approach where the stream program is compiled to gather key application information, and to generate re-targetable code. A light weight dynamic scheduler incorporates the second stage of the approach. The dynamic scheduler utilizes the static information and available resources to assign or partition the application across the multi-core architecture. The objective of the dynamic scheduler is to maximize the throughput of the application, and it is sensitive to the resource (processing elements, scratch-pad memory, DMA bandwidth) constraints imposed by the target architecture. We evaluate the proposed approach by compiling and scheduling benchmark stream programs on a representative embedded multi-core processor. We present experimental results that evaluate the quality of the solutions generated by the proposed approach by comparisons with existing techniques.
ContributorsLee, Haeseung (Author) / Chatha, Karamvir (Thesis advisor) / Vrudhula, Sarma (Committee member) / Chakrabarti, Chaitali (Committee member) / Wu, Carole-Jean (Committee member) / Arizona State University (Publisher)
Created2013
Description
Limited Local Memory (LLM) multicore architectures are promising powerefficient architectures will scalable memory hierarchy. In LLM multicores, each core can access only a small local memory. Accesses to a large shared global memory can only be made explicitly through Direct Memory Access (DMA) operations. Standard Template Library (STL) is a powerful programming tool and is widely used for software development. STLs provide dynamic data structures, algorithms, and iterators for vector, deque (double-ended queue), list, map (red-black tree), etc. Since the size of the local memory is limited in the cores of the LLM architecture, and data transfer is not automatically supported by hardware cache or OS, the usage of current STL implementation on LLM multicores is limited. Specifically, there is a hard limitation on the amount of data they can handle. In this article, we propose and implement a framework which manages the STL container classes on the local memory of LLM multicore architecture. Our proposal removes the data size limitation of the STL, and therefore improves the programmability on LLM multicore architectures with little change to the original program. Our implementation results in only about 12%-17% increase in static library code size and reasonable runtime overheads.
ContributorsLu, Di (Author) / Shrivastava, Aviral (Thesis advisor) / Chatha, Karamvir (Committee member) / Dasgupta, Partha (Committee member) / Arizona State University (Publisher)
Created2012