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Automatic computation and data decomposition for multiprocessors
Automatic computation and data decomposition for multiprocessors
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Studying inter-core data reuse in multicores
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IEEE Transactions on Very Large Scale Integration (VLSI) Systems
An energy-aware online task mapping algorithm in NoC-based system
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The problem attacked in this paper is one of automatically mapping an application onto a Network-on-Chip (NoC) based chip multiprocessor (CMP) architecture in a locality-aware fashion. The proposed compiler approach has four major steps: task scheduling, processor mapping, data mapping, and packet routing. In the first step, the application code is parallelized and the resulting parallel threads are assigned to virtual processors. The second step implements a virtual processor-to-physical processor mapping. The goal of this mapping is to ensure that the threads that are expected to communicate frequently with each other are assigned to neighboring processors as much as possible. In the third step, data elements are mapped to memories attached to CMP nodes. The main objective of this mapping is to place a given data item into a node which is close to the nodes that access it. The last step of our approach determines the paths (between memories and processors) for data to travel in an energy efficient manner. In this paper, we describe the compiler algorithms we implemented in detail and present an experimental evaluation of the framework. In our evaluation, we test our entire framework as well as the impact of omitting some of its steps. This experimental analysis clearly shows that the proposed framework reduces energy consumption of our applications significantly (27.41% on average over a pure performance oriented application mapping strategy) as a result of improved locality of data accesses.