Abstract-Graphics processors (GPU) offer the promise of more than an order of magnitude speedup over conventional processors for certain non-graphics computations. Because the GPU is often presented as a C-like abstraction (e.g., Nvidia's CUDA), little is known about the characteristics of the GPU's architecture beyond what the manufacturer has documented. This work develops a microbechmark suite and measures the CUDA-visible architectural characteristics of the Nvidia GT200 (GTX280) GPU. Various undisclosed characteristics of the processing elements and the memory hierarchies are measured. This analysis exposes undocumented features that impact program performance and correctness. These measurements can be useful for improving performance optimization, analysis, and modeling on this architecture and offer additional insight on the decisions made in developing this GPU.

This work introduces a new branch predictor design that increases the perceived predictor capacity without increasing its delay by using a large virtual second-level table allocated in the second-level caches. Virtualization is applied to a state-of-theart multi-table branch predictor. We evaluate the design using instruction count as proxy for timing on a set of commercial workloads. For a predictor whose size is determined by access delay constraints, accuracy can be improved by 8.7%. Alternatively, the design can be used to achieve the same accuracy as a non-virtualized design while using 25% less dedicated storage.