Grid-based Workflow Management for Automatic Performance Analysis of Massively Parallel Applications

Many Grid infrastructures have begun to offer services to end-users during the past several years with an increasing number of complex scientific applications and software tools that require seamless access to different Grid resources via Grid middleware during one workflow. End-users of the rather hpc-driven deisa Grid infrastructure take not only advantage of Grid workflow management capabilities for massively parallel applications to solve critical problems of high complexity (e.g. protein folding, global weather prediction), but also leverage software tools to achieve satisfactory application performance on contemporary massively parallel machines (e.g., ibmBlue Gene/P). In this context, event tracing is one technique widely used by software tools with a broad spectrum of applications ranging from performance analysis, performance prediction and modeling to debugging. In particular, automatic performance analysis has emerged as an powerful and robust instrument to make the optimization of parallel applications both more effective and more efficient. The approach of automatic performance analysis implies multiple steps that can perfectly leverage the workflow capabilities in Grids. In this paper, we present how this approach is implemented by using the workflow management capabilities of the unicore Grid middleware, which is deployed on deisa, and thus, demonstrate by using a Grid application that the approach taken is feasible.