TACO — Template Based Collections for Distributed Computing Platforms

Parallel Matching and Sorting with TACO's Distributed Collections - A Case Study from Molecular Biology Research

TACO is a template library that implements higher-order parallel operations on distributed object sets by means of reusable topology classes and C++ function templates. In this paper we discuss an experimental application that exploits TACO’s distributed object groups and collective operations for computing the similarity between groups of molecular sequences, a computationally intensive core p...

TACO-Exploiting Cluster Networks for High-Level Collective Operations

TACO (Topologies and Collections) is a template library that introduces the flavour of distributed data parallel processing by means of reusable topology classes and C++ templates. This paper introduces TACO’s basic abstractions and provides a performance analysis for basic collective operations on various cluster architectures with several different networks.

TACO -- Dynamic Distributed Collections with Templates and Topologies

Template Based Structured Collections

Collective operations on distributed data sets foster a high-level data-parallel programming style that eases many aspects of parallel programming significantly. In this paper we describe how higher-order collective operations on distributed object sets can be introduced in a structured way by means of reusable topology classes and C++ templates.

A Portable MPI-Based Parallel Vector Template Library

This paper discusses the design and implementation of a polymorphiccollection library for distributed address-space parallel computers. The library provides a data-parallel programming model for C++ by providing three main components: a single generic collection class, generic algorithms over collections, and generic algebraic combining functions. Collection elements are the fourth component of...