A Vector-space Model for Parallel Workload Characterization

Parallel Workload Characterization for Scientiic Computing Applications

A hierarchical approach to workload characterization for parallel systems

Performance evaluation studies are to be an integral part of the design and tuning of parallel applications Their structure and their behavior are the dominating factors We propose a hierarchical approach to the systematic characterization of the workload of a parallel system to be kept as modular and exible as possible The methodology is based on three di erent but related layers the applicati...

Medea: a tool for workload characterization of parallel systems

Performance of parallel workload is heavily in uenced by how its own characteristics match the systems Experimental ap proaches are particularly suitable although di cult for study ing the behavior of the programs The raw data collected by monitoring tools on real systems need to be processed for ob taining meaningful and easy to interpret information usable in identifying sources of poor perfo...

A parallel workload model and its implications

We develop a workload model based on the observed behavior of parallel computers at the San Diego Supercomputer Center and the Cornell Theory Center. This model gives us insight into the performance of strategies for scheduling malleable jobs on space-sharing parallel computers. We nd that Adaptive Static Partitioning (ASP), which has been reported to work well for other workloads, is inferior ...

Parallel Retrieval of Dense Vectors in the Vector Space Model

Modern information retrieval systems use distributed and parallel algorithms to meet their operational requirements, and commonly operate on sparse vectors. But dimensionality-reducing techniques produce dense and relatively short feature vectors. Motivated by this relevance of dense vectors, we have parallelized the vector space model for dense matrices and vectors. Our algorithm uses a hybrid...