Data as a service yearly). The drawbacks of data as a service are generally similar to those associated with any type of cloud computing, such as the reliance of the customer on the service provider's ability to avoid server downtime. Specific to the DaaS model, a common criticism is that when compared to traditional data delivery, the consumer is really just "renting" the data, using it to produce a graph, chart or map, or possibly perform analysis, but for data as a service, generally the data is not available for download. "Service automation units" (code that expresses the service interface) may contain methods for
General-purpose computing on graphics processing units type of algorithm, not a piece of equipment. Specialized equipment designs may, however, even further enhance the efficiency of GPGPU pipelines, which traditionally perform relatively few algorithms on very large amounts of data. Massively parallelized, gigantic-data-level tasks thus may be parallelized even further via specialized setups such as rack computing (many similar, highly tailored machines built into a "rack"), which adds a third layer many computing units each using many CPUs to correspond to many GPUs. Some Bitcoin "miners" used such setups for high-quantity processing. Historically, CPUs have used hardware-managed caches but the earlier GPUs only provided software-managed local memories.