C4.5: programs for machine learning
C4.5: programs for machine learning
Introduction to parallel computing: design and analysis of algorithms
Introduction to parallel computing: design and analysis of algorithms
Analyzing scalability of parallel algorithms and architectures
Journal of Parallel and Distributed Computing - Special issue on scalability of parallel algorithms and architectures
Parallel matrix-vector product using approximate hierarchical methods
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Highly Scalable Parallel Algorithms for Sparse Matrix Factorization
IEEE Transactions on Parallel and Distributed Systems
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
DBMS Research at a Crossroads: The Vienna Update
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Combined DRAM and logic chip for massively parallel systems
ARVLSI '95 Proceedings of the 16th Conference on Advanced Research in VLSI (ARVLSI'95)
EXECUBE-A New Architecture for Scaleable MPPs
ICPP '94 Proceedings of the 1994 International Conference on Parallel Processing - Volume 01
Relationships Between Efficiency and Execution Time of Full Multigrid Methods on Parallel Computers
IEEE Transactions on Parallel and Distributed Systems
High Performance Implementation of Binomial Option Pricing
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Cache-optimal algorithms for option pricing
ACM Transactions on Mathematical Software (TOMS)
Upper and lower I/O bounds for pebbling r-pyramids
IWOCA'10 Proceedings of the 21st international conference on Combinatorial algorithms
Upper and lower I/O bounds for pebbling r-pyramids
Journal of Discrete Algorithms
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We outline a hierarchical architecture for machines capable of over 100 teraOps in a 10 year time-frame. The motivating factors for the design are technological feasibility and economic viability. The envisioned architecture can be built largely from commodity components. The development costs of the machine will therefore be shared by the market. To obtain sustained performance from the machine, we propose a heterogeneous programming environment for the machine. The programming environment optimally uses the power of the hierarchy. Programming models for the stronger machine models existing at the lower levels are tuned for ease of programming. Higher levels of the hierarchy place progressively greater emphasis on locality of data reference. The envisioned machine architecture requires new algorithm design methodologies. We propose to develop hierarchical parallel algorithms and scalability metrics for evaluating such algorithms. We identify three important application areas: large scale numerical simulations, problems in particle dynamics and boundary element methods, and emerging large-scale applications such as data-mining. We briefly outline the process of hierarchical algorithm design for each of these application areas.