@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.}
}
@article {14931,
	title = {A new deterministic parallel sorting algorithm with an experimental evaluation},
	journal = {Journal of Experimental Algorithmics (JEA)},
	volume = {3},
	year = {1998},
	month = {1998/09//},
	abstract = {We introduce a new deterministic parallel sorting algorithm for distributed memory machines based on the regular sampling approach. The algorithm uses only two rounds of regular all-to-all personalized communication in a scheme that yields very good load balancing with virtually no overhead. Moreover, unlike previous variations, our algorithm efficiently handles the presence of duplicate values without the overhead of tagging each element with a unique identifier. This algorithm was implemented in SPLIT-C and run on a variety of platforms, including the Thinking Machines CM-5, the IBM SP-2-WN, and the Cray Research T3D. We ran our code using widely different benchmarks to examine the dependence of our algorithm on the input distribution. Our experimental results illustrate the efficiency and scalability of our algorithm across different platforms. In fact, the performance compares closely to that of our random sample sort algorithm, which seems to outperform all similar algorithms known to the authors on these platforms. Together, their performance is nearly invariant over the set of input distributions, unlike previous efficient algorithms. However, unlike our randomized sorting algorithm, the performance and memory requirements of our regular sorting algorithm can be deterministically guaranteed.},
	keywords = {generalized sorting, integer sorting, Parallel algorithms, parallel performance, sorting by regular sampling},
	isbn = {1084-6654},
	doi = {10.1145/297096.297128},
	url = {http://doi.acm.org/10.1145/297096.297128},
	author = {Helman,David R. and JaJa, Joseph F. and Bader,David A.}
}
