Chancengerechtigkeit durch Bildung – Chancengerechtigkeit in der Bildung (Auszug)

Der hier mit freundlicher Genehmigung des AWO Bundesverbands abgedruckte Text ist ein Auszug aus der Broschüre: Arbeiterwohlfahrt Bundesverband (Hrsg.): Standpunkte 2006. Chancengerechtigkeit durch Bildung – Chancengerechtigkeit in der Bildung, Bonn 2006. Unser Bildungssystem für die Kinder im Alter von 6 bis 16 Jahren wird den Herausforderungen der Zukunft nicht gerecht. Ein Umsteuern ist dringend notwendig, da ohne Bildung der Wandel in die Wissensgesellschaft nicht zu bewältigen ist. Bildung, Qualifikation und Kompetenzen und das Erlernen von Diskurs- und Konfliktfähigkeit entscheiden über die beruflichen und gesellschaftlichen Chancen eines jeden Menschen und davon abhängig über seine Zukunftschancen. Bildung bedeutet Entwicklung der Persönlichkeit, der Identität. Bildung bedeutet aber auch, die gemeinschaftsfähige Persönlichkeit zu gestalten. Und somit bekommt Bildung gerade in der Lebensphase der 6- bis 16-Jährigen über die eher traditionelle Dimension hinaus auch einen emanzipatorischen Charakter. Wenn Bildung also für den Einzelnen diese entscheidende Rolle spielt, dann bekommt die öffentliche Verantwortung für dieses Bildungswesen eine ganz zentrale Bedeutung. (DIPF/Orig.

MOGO: Model-Oriented Global Optimization of Petascale Applications

The MOGO project was initiated under in 2008 under the DOE Program Announcement for Software Development Tools for Improved Ease-of-Use on Petascale systems (LAB 08-19). The MOGO team consisted of Oak Ridge National Lab, Argonne National Lab, and the University of Oregon. The overall goal of MOGO was to attack petascale performance analysis by developing a general framework where empirical performance data could be efficiently and accurately compared with performance expectations at various levels of abstraction. This information could then be used to automatically identify and remediate performance problems. MOGO was be based on performance models derived from application knowledge, performance experiments, and symbolic analysis. MOGO was able to make reasonable impact on existing DOE applications and systems. New tools and techniques were developed, which, in turn, were used on important DOE applications on DOE LCF systems to show significant performance improvements

MOGO: Model-Oriented Global Optimization of Petascale Applications

The MOGO project was initiated under in 2008 under the DOE Program Announcement for Software Development Tools for Improved Ease-of-Use on Petascale systems (LAB 08-19). The MOGO team consisted of Oak Ridge National Lab, Argonne National Lab, and the University of Oregon. The overall goal of MOGO was to attack petascale performance analysis by developing a general framework where empirical performance data could be efficiently and accurately compared with performance expectations at various levels of abstraction. This information could then be used to automatically identify and remediate performance problems. MOGO was be based on performance models derived from application knowledge, performance experiments, and symbolic analysis. MOGO was able to make reasonable impact on existing DOE applications and systems. New tools and techniques were developed, which, in turn, were used on important DOE applications on DOE LCF systems to show significant performance improvements

Supporting Nested OpenMP Parallelism in the TAU Performance System

Abstract. Nested OpenMP parallelism allows an application to spawn teams of nested threads. This hierarchical nature of thread creation and usage poses problems for performance measurement tools that must determine thread context to properly maintain per-thread performance data. In this paper we describe the problem and a novel solution for identifying threads uniquely. Our approach has been implemented in the TAU performance system and has been successfully used in profiling and tracing OpenMP applications with nested parallelism. We also describe how extensions to the OpenMP standard can help tool developers uniquely identify threads

Hands-On Practical Hybrid Parallel Application Performance Engineering

This tutorial presents state-of-the-art performance tools for leading-edge HPC systems founded on the Score-P community-developed instrumentation and measurement infrastructure, demonstrating how they can be used for performance engineering of effective scientific applications based on standard MPI, OpenMP, hybrid combination of both, and increasingly common usage of accelerators. Parallel performance tools from the Virtual Institute - High Productivity Supercomputing (VI-HPS) are introduced and featured in hands-on exercises with Scalasca, Vampir, and TAU. We present the complete workflow of performance engineering, including instrumentation, measurement (profiling and tracing, timing and PAPI hardware counters), data storage, analysis, and visualization. Emphasis is placed on how tools are used in combination for identifying performance problems and investigating optimization alternatives. Using their own notebook computers with a provided Linux Live-ISO image containing all of the tools (running within a virtual machine or booted directly from DVD/USB) will help to prepare participants to locate and diagnose performance bottlenecks in their own parallel programs