Stream Processing - Applications

Applications

Stream processing is essentially a compromise, driven by a data-centric model that works very well for traditional DSP or GPU-type applications (such as image, video and digital signal processing) but less so for general purpose processing with more randomized data access (such as databases). By sacrificing some flexibility in the model, the implications allow easier, faster and more efficient execution. Depending on the context, processor design may be tuned for maximum efficiency or a trade-off for flexibility.

Stream processing is especially suitable for applications that exhibit three application characteristics:

• Compute Intensity, the number of arithmetic operations per I/O or global memory reference. In many signal processing applications today it is well over 50:1 and increasing with algorithmic complexity.
• Data Parallelism exists in a kernel if the same function is applied to all records of an input stream and a number of records can be processed simultaneously without waiting for results from previous records.
• Data Locality is a specific type of temporal locality common in signal and media processing applications where data is produced once, read once or twice later in the application, and never read again. Intermediate streams passed between kernels as well as intermediate data within kernel functions can capture this locality directly using the stream processing programming model.