What is predictability for real-time systems?
Real-Time Systems
POSIX.4: programming for the real world
POSIX.4: programming for the real world
Worst-case execution time analysis on modern processors
LCTES '95 Proceedings of the ACM SIGPLAN 1995 workshop on Languages, compilers, & tools for real-time systems
Language constructs and transformation for hard real-time systems
LCTES '95 Proceedings of the ACM SIGPLAN 1995 workshop on Languages, compilers, & tools for real-time systems
High performance computing for vision on distributed-memory machines
High performance computing for vision on distributed-memory machines
Parallel algorithms for space-time adaptive processing
IPPS '95 Proceedings of the 9th International Symposium on Parallel Processing
The challenge of the increased use of COTS: a developer's perspective
WPDRTS '95 Proceedings of the 3rd Workshop on Parallel and Distributed Real-Time Systems
MPI: A Message-Passing Interface Standard
MPI: A Message-Passing Interface Standard
Performance Evaluation of a Parallel Pipeline Computational Model for Space-Time Adaptive Processing
The Journal of Supercomputing
Proceedings of the Third International Workshop on High-Performance Reconfigurable Computing Technology and Applications
SCF: A Framework for Task-Level Coordination in Reconfigurable, Heterogeneous Systems
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
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Embedded signal processing systems have traditionally been built using custom VLSI to meet real-time requirements. This leads to limited programmability and restricted flexibility. With recent technological advances in high performance computing, scalable systems based on heterogeneous "off the shelf" modules are attractive as computing platforms in real-time embedded environments, leading to an emerging class of Scalable Heterogeneous High Performance Embedded (SHHiPE) systems. These systems offer advantages of low-cost, scalability, easy programmability, software portability, and the ability to incorporate evolving hardware technology. In order to satisfy the timing and predictability requirements that arise in embedded environments, several issues must be considered. These issues arise at the hardware level-such as choice of processing element architecture, and also at the software level-issues related to operating system and communication libraries. We propose an integrated methodology to develop efficient parallel solutions for signal processing applications on the SHHiPE platforms. Our approach is to develop scalable portable algorithms based on accurate computational models of the hardware platforms. We present preliminary performance results of such an approach applied to a radar signal processing problem.