Available at: http://digitalcommons.calpoly.edu/theses/794
Date of Award
MS in Computer Science
Massively parallel programming is an increasingly growing field with the recent introduction of general purpose GPU computing. Modern graphics processors from NVIDIA and AMD have massively parallel architectures that can be used for such applications as 3D rendering, financial analysis, physics simulations, and biomedical analysis. These massively parallel systems are exposed to programmers through in- terfaces such as NVIDIAs CUDA, OpenCL, and Microsofts C++ AMP. These frame- works expose functionality using primarily either C or C++. In order to use these massively parallel frameworks, programs being implemented must be run on machines equipped with massively parallel hardware. These requirements limit the flexibility of new massively parallel systems. This paper explores the possibility that massively parallel systems can be exposed through web services in order to facilitate using these architectures from remote systems written in other languages. To explore this possi- bility, an architecture is put forth with requirements and high level design for building a web service that can overcome limitations of existing tools and frameworks. The CUDA Web API is built using Python, PyCUDA, NumPy, JSON, and Django to meet the requirements set forth. Additionaly, a client application, CUDA Cloud, is built and serves as an example web service client. The CUDA Web API’s performance and its functionality is validated using a common matrix multiplication algorithm implemented using different languages and tools. Performance tests show runtime improvements for larger datasets using the CUDA Web API for remote CUDA kernel execution over serial implementations. This paper concludes that existing limitations associated with GPGPU usage can be overcome with the specified architecture.