Accelerated computing on computational grid infrastructures

Abstract

Grid infrastructures have been central to some of the largest computationally intensive scientific investigations in recent times. As the complexity of computational simulations and models increases, the dependency on faster processing power and massively parallel computing also increases. In synergy, faster processing power drives advancements in computational simulations and models. The emergence of several new types of computational resources, such as GPGPUs, Intel’s Xeon Phi, FPGAs and other types of accelerators are a manifestation of this. These resources have diverse characteristics (e.g. physical properties) and there is currently no uniform way to discover or specify them in a grid job. The diversity of the systems that make up Grids is managed by adherence to common standards that define how systems, services and users interact with Grids. While this helps provide for long-term stability, the downside is that Grids cannot handle rapid changes to the standards and grid middleware. Thus, a Resource Integration Problem arises, which can be summed up as a question of how to deal with the integration of new and diverse computational resources in a massive distributed grid computing environment that conversly requires stability. The thesis shows that a conceptual approach can be used to provide a level of abstraction that assists with the process of integrating new computational resources that adhere to existing grid standards while requiring only a small number of unobtrusive changes to the grid middleware. This has never been explored before. The result is a flexible, dynamic approach to the integration of new (and yet to be conceived) resources into existing grid infrastructures. This effectiveness of this approach is demonstrated by extending the Grid to handle both GPGPU and virtual GPGPU resources.