@conference {19295,
	title = {Buffer management for multi-application image processing on multi-core platforms: Analysis and case study},
	booktitle = {2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)},
	year = {2010},
	month = {2010},
	pages = {1662 - 1665},
	abstract = {Due to the limited amounts of on-chip memory, large volumes of data, and performance and power consumption overhead associated with interprocessor communication, efficient management of buffer memory is critical to multi-core image processing. To address this problem, this paper develops new modeling and analysis techniques based on dataflow representations, and demonstrates these techniques on a multi-core implementation case study involving multiple, concurrently-executing image processing applications. Our techniques are based on careful representation and exploitation of frame- or block-based operations, which involve repeated invocations of the same computations across regularly- arranged subsets of data. Using these new approaches to manage block-based image data, this paper demonstrates methods to analyze synchronization overhead and FIFO buffer sizes when mapping image processing applications onto heterogeneous, multi core architectures.},
	keywords = {block-based image data, buffer memory management, buffer storage, concurrently-executing image processing application, data flow computing, data flow representation, data subset, Dataflow, Digital signal processing, Energy consumption, Energy management, Engineering management, FIFO buffer sizes, Hardware, Image analysis, IMAGE PROCESSING, image processing application, image representation, interprocessor communication, memory architecture, Memory management, multiapplication image processing, multicore image processing, multiprocessing, on-chip memory, power consumption, power consumption overhead, Runtime, scheduling, set theory, shared memory, storage management chips, synchronization overhead},
	author = {Ko,Dong-Ik and Won, N. and Bhattacharyya, Shuvra S.}
}
@article {15024,
	title = {Simple: A Methodology for Programming High Performance Algorithms on Clusters of Symmetric Multiprocessors (SMPs)},
	journal = {Journal of Parallel and Distributed Computing},
	volume = {58},
	year = {1999},
	month = {1999/07//},
	pages = {92 - 108},
	abstract = {We describe a methodology for developing high performance programs running on clusters of SMP nodes. The SMP cluster programming methodology is based on a small prototype kernel (Simple) of collective communication primitives that make efficient use of the hybrid shared and message-passing environment. We illustrate the power of our methodology by presenting experimental results for sorting integers, two-dimensional fast Fourier transforms (FFT), and constraint-satisfied searching. Our testbed is a cluster of DEC AlphaServer 2100 4/275 nodes interconnected by an ATM switch.},
	keywords = {cluster computing, communication primitives, experimental parallel algorithms, message passing (MPI), Parallel algorithms, parallel performance, shared memory, symmetric multiprocessors (SMP)},
	isbn = {0743-7315},
	doi = {10.1006/jpdc.1999.1541},
	url = {http://www.sciencedirect.com/science/article/pii/S0743731599915411},
	author = {Bader,David A. and JaJa, Joseph F.}
}
