Real-time signal processing consumes the majority of the world's computing power. Increasingly, programmable parallel processors are used to address a wide variety of signal processing applications (e.g., scientific, video, wireless, medical, communication, encoding, radar, sonar, and imaging). In programmable systems, the major challenge is no longer hardware but software. Specifically, the key technical hurdle lies in allowing the user to write programs at high level, while still achieving performance and preserving the portability of the code across parallel computing hardware platforms. The Parallel Vector, Signal, and Image Processing Library (Parallel VSIPL++) addresses this hurdle by providing high-level C++ array constructs, a simple mechanism for mapping data and functions onto parallel hardware, and a community-defined portable interface. This paper presents an overview of the Parallel VSIPL++ standard as well as a deeper description of the technical foundations and expected performance of the library. Parallel VSIPL++ supports adaptive optimization at many levels. The C++ arrays are designed to support automatic hardware specialization by the compiler. The computation objects (e.g., fast Fourier transforms) are built with explicit setup and run stages to allow for runtime optimization. Parallel arrays and functions in Parallel VSIPL++ also support explicit setup and run stages, which are used to accelerate communication operations. The parallel mapping mechanism provides an external interface that allows optimal mappings to be generated offline and read into the system at runtime. Finally, the standard has been developed in collaboration with high performance embedded computing vendors and is compatible with their proprietary approaches to achieving performance.

VSIPL, the Vector, Signal, and Image Processing Library, is an open standard application programmer's interface (API) for signal and image processing. Defined by a consortium of industry, government, and academic representatives, VSIPL is gaining widespread acceptance as a de facto standard in the embedded signal processing world. The primary goal of the API is to increase the portability of vector signal processing, matrix signal processing, and image processing applications. We present an overview of the design, features, and availability of the VSIPL API.

Space-time adaptive processing (STAP) refers to a class of methods for detecting targets using an array of sensors. Various STAP methods use similar operations on different data or in different orders. We have developed a portable, parallel library of subroutines for prototyping STAP methods. The subroutines work on the IBM SP2 and the Intel Paragon under three different operating systems and three different communication libraries, and can also be configured for other systems. We provide execution-time models for predicting the performance of each subroutine. Using the library routines, we created a parallel version of element-space pre-Doppler processing, three parallel versions of higher-order post-Doppler processing, and two versions of PRI-staggered post-Doppler processing. We implemented a fourth version of higher-order post-Doppler processing, the hybrid method, which uses a combination of fine-grain and coarse-grain parallelism to reduce execution time. The hybrid method can be used to improve performance when a large number of processors is available. Our execution time models generally predict the best method and predict execution times to within 10 percent or better for large test cases.