Evaluation and enhancement of memory efficiency targeting general-purpose computations on scalable data-parallel GPU architectures

• Online Access: http://hdl.handle.net/2047/d20002062

This thesis addresses the memory efficiency of general-purpose applications running on massively multi-threaded, data-parallel GPU architectures. Although scalable, data-parallel GPU architectures and their associated general-purpose programming models offer impressive computational capability and attractive power budgets, the pace of migrating general-purpose applications to this emerging class of architectures is significantly hindered by the efficiency of memory subsystem present on these platforms. Programmers are forced to optimize the memory behavior of their code if they are interested in reaping the full benefits of these high performance, data-parallel architectures.