Towards a methodology for creating time-critical, cloud-based CUDA applications

CUDA has been used in many different application domains, not all of which are specifically image processing related. There is the opportunity to use multiple and/or distributed CUDA resources in cloud facilities such as Amazon Web Services (AWS), in order to obtain enhanced processing power and to satisfy time-critical requirements which cannot be satisfied using a single CUDA resource. In particular, this would provide enhanced ability for processing Big Data, especially in conjunction with distributed file systems (for example). In this paper, we present a survey of time-critical CUDA applications, identifying requirements and concepts that they tend to have in common. In particular, we investigate the terminology used for Quality of Service metrics, and present a taxonomy which summarises the underlying concepts and maps these terms to the diverse terminology used. We also survey typical requirements for developing, deploying and managing such applications. Given these requirements, we consider how the SWITCH platform can in principle support the entire life-cycle of time-critical CUDA application development and cloud deployment, and identify specific extensions which would be needed in order fully to support this particular class of time-critical cloud applications.