Data-parallel co-processors have the potential to improve performance
in highly parallel regions of code when coupled to a generalpurpose CPU. However, applications often have to be modified in non-intuitive and complicated ways to mitigate the cost of data marshalling between the CPU and the co-processor. In some applications the overheads cannot be amortized and co-processors are unable to provide benefit. The additional effort and complexity of incorporating co-processors makes it difficult, if not impossible, to effectively utilize co-processors in large applications.
This paper presents CUBA, an architecture model where coprocessors
encapsulated as function calls can efficiently access their input and output data structures through pointer parameters. The key idea is to map the data structures required by the co-processor to the co-processor local memory as opposed to the CPUs main memory. The mapping in CUBA preserves the original layout of the shared data structures hosted in the co-processor local memory. The mapping renders the data marshalling process unnecessary and
reduces the need for code changes in order to use the co-processors. CUBA allows the CPU to cache hosted data structures with a selective write-through cache policy, allowing the CPU to access hosted data structures while supporting efficient communication with the co-processors. Benchmark simulation results show that a CUBAbased system can approach optimal transfer rates while requiring few changes to the code that executes on the CPU.