Thilina Rathnayake | National School of Business Management (original) (raw)
Related Authors
Lahore University of Management Sciences
Uploads
Papers by Thilina Rathnayake
Lecture Notes in Computer Science, 2015
Self Organizing Maps (SOM) are widely used in data mining and high-dimensional data visualization... more Self Organizing Maps (SOM) are widely used in data mining and high-dimensional data visualization due to its unsupervised nature and robustness. Growing Self Organizing Maps (GSOM) is a variant of SOM algorithm which allows nodes to be grown so that it can represent the input space better. Without using a fixed 2D grid like SOM, GSOM starts with four nodes and keeps track of the quantization error in each node. New nodes are grown from an existing node if its error value exceeds a pre-defined threshold. Ability of the GSOM algorithm to represent input space accurately is vital to extend its applicability to a wider spectrum of problems. This ability can be improved by identifying nodes that represent low probability regions in the input space and removing them periodically from the map. This will improve the homogeneity and completeness of the final clustering result. A new extension to GSOM algorithm based on node deletion is proposed in this paper as a solution to this problem. Furthermore, two new algorithms inspired by cache replacement policies are presented. First algorithm is based on Adaptive Replacement Cache (ARC) and maintains two separate Least Recently Used (LRU) lists of the nodes. Second algorithm is built on Frequency Based Replacement policy (FBR) and maintains a single LRU list. These algorithms consider both recent and frequent trends in the GSOM grid before deciding on the nodes to be deleted. The experiments conducted suggest that the FBR based method for node deletion outperforms the standard algorithm and other existing node deletion methods.
Performance tests and analyses are critical to effective high-performance computing software deve... more Performance tests and analyses are critical to effective high-performance computing software development and are central components in the design and implementation of computational algorithms for achieving faster simulations on existing and future computing architectures for large-scale application problems. In this article, we explore performance and space-time trade-offs for important compute-intensive kernels of large-scale numerical solvers for partial differential equations (PDEs) that govern a wide range of physical applications. We consider a sequence of PDE-motivated bake-off problems designed to establish best practices for efficient high-order simulations across a variety of codes and platforms. We measure peak performance (degrees of freedom per second) on a fixed number of nodes and identify effective code optimization strategies for each architecture. In addition to peak performance, we identify the minimum time to solution at 80% parallel efficiency. The performance a...
Lecture Notes in Computer Science, 2015
Self Organizing Maps (SOM) are widely used in data mining and high-dimensional data visualization... more Self Organizing Maps (SOM) are widely used in data mining and high-dimensional data visualization due to its unsupervised nature and robustness. Growing Self Organizing Maps (GSOM) is a variant of SOM algorithm which allows nodes to be grown so that it can represent the input space better. Without using a fixed 2D grid like SOM, GSOM starts with four nodes and keeps track of the quantization error in each node. New nodes are grown from an existing node if its error value exceeds a pre-defined threshold. Ability of the GSOM algorithm to represent input space accurately is vital to extend its applicability to a wider spectrum of problems. This ability can be improved by identifying nodes that represent low probability regions in the input space and removing them periodically from the map. This will improve the homogeneity and completeness of the final clustering result. A new extension to GSOM algorithm based on node deletion is proposed in this paper as a solution to this problem. Furthermore, two new algorithms inspired by cache replacement policies are presented. First algorithm is based on Adaptive Replacement Cache (ARC) and maintains two separate Least Recently Used (LRU) lists of the nodes. Second algorithm is built on Frequency Based Replacement policy (FBR) and maintains a single LRU list. These algorithms consider both recent and frequent trends in the GSOM grid before deciding on the nodes to be deleted. The experiments conducted suggest that the FBR based method for node deletion outperforms the standard algorithm and other existing node deletion methods.
Performance tests and analyses are critical to effective high-performance computing software deve... more Performance tests and analyses are critical to effective high-performance computing software development and are central components in the design and implementation of computational algorithms for achieving faster simulations on existing and future computing architectures for large-scale application problems. In this article, we explore performance and space-time trade-offs for important compute-intensive kernels of large-scale numerical solvers for partial differential equations (PDEs) that govern a wide range of physical applications. We consider a sequence of PDE-motivated bake-off problems designed to establish best practices for efficient high-order simulations across a variety of codes and platforms. We measure peak performance (degrees of freedom per second) on a fixed number of nodes and identify effective code optimization strategies for each architecture. In addition to peak performance, we identify the minimum time to solution at 80% parallel efficiency. The performance a...