Provided by: University of Toledo
Date Added: Feb 2014
GPUs offer an order of magnitude higher compute power and memory bandwidth than CPUs. GPUs therefore might appear to be well suited to accelerate computations that operate on voluminous data sets in independent ways; e.g., for transformations, filtering, aggregation, partitioning or other \"Big data\" style processing. Yet experience indicates that it is difficult, and often error-prone, to write GPGPU programs which efficiently process data that does not fit in GPU memory, partly because of the intricacies of GPU hardware architecture and programming models, and partly because of the limited bandwidth available between GPUs and CPUs.