Stacking Framework Research Articles

Operation Stacking for Ensemble Computations With Variable Convergence

Sparse matrix operations achieve only small fractions of peak CPU speeds because of the use of specialized, index-based matrix representations, which degrade cache utilization by imposing irregular memory accesses and increasing the number of overall accesses. Compounding the problem, the small number of floating-point operations in a single sparse iteration leads to low floating-point pipeline utilization. Operation stacking addresses these problems for large ensemble computations that solve multiple systems of linear equations with identical sparsity structure. By combining the data of multiple problems and solving them as one, operation stacking improves locality, reduces cache misses, and increases floating-point pipeline utilization. Operation stacking also requires less memory bandwidth because it involves fewer index array accesses. In this paper we present the Operation Stacking Framework (OSF), an object-oriented framework that provides runtime and code generation support for the development of stacked iterative solvers. OSF’s runtime component provides an iteration engine that supports efficient ejection of converged problems from the stack. It separates the specific solver algorithm from the coding conventions and data representations that are necessary to implement stacking. Stacked solvers created with OSF can be used transparently without requiring significant changes to existing applications. Our results show that stacking can provide speedups up to 1.94× with an average of 1.46×, even in scenarios in which the number of iterations required to converge varies widely within a stack of problems. Our evaluation shows that these improvements correlate with better cache utilization, improved floating-point utilization, and reduced memory accesses.

Combining Information Extraction Systems Using Voting and Stacked Generalization

This article investigates the effectiveness of voting and stacked generalization -also known as stacking- in the context of information extraction (IE). A new stacking framework is proposed that accommodates well-known approaches for IE. The key idea is to perform cross-validation on the base-level data set, which consists of text documents annotated with relevant information, in order to create a meta-level data set that consists of feature vectors. A classifier is then trained using the new vectors. Therefore, base-level IE systems are combined with a common classifier at the meta-level. Various voting schemes are presented for comparing against stacking in various IE domains. Well known IE systems are employed at the base-level, together with a variety of classifiers at the meta-level. Results show that both voting and stacking work better when relying on probabilistic estimates by the base-level systems. Voting proved to be effective in most domains in the experiments. Stacking, on the other hand, proved to be consistently effective over all domains, doing comparably or better than voting and always better than the best base-level systems. Particular emphasis is also given to explaining the results obtained by voting and stacking at the meta-level, with respect to the varying degree of similarity in the output of the base-level systems.