Stream processing

Stream processing is a computer programming paradigm, related to SIMD, that allows some applications to more easily exploit a limited form of parallel processing. Such applications can use multiple computational units, such as the floating point units on a GPU, without explicitly managing allocation, synchronization, or communication among those units.

The stream processing paradigm simplifies parallel software and hardware by restricting the parallel computation that can be performed. Given a set of data (a "stream"), a series of operations ("kernel functions") are applied to each element in the stream. "Uniform streaming", where one kernel function is applied to all elements in the stream, is typical. Kernel functions are usually pipelined, and local on-chip memory is reused to minimize external memory bandwidth. Since the kernel and stream abstractions expose data dependencies, compiler tools can fully automate and optimize on-chip management tasks. Stream processing hardware can use scoreboarding, for example, to launch DMAs at runtime, when dependencies become known. The elimination of manual DMA management reduces software complexity, and the elimination of hardware caches reduces the amount of die area not dedicated to computational units such as ALUs.

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 characteristicsFact|date=June 2008:

• 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.