Based on the two-dimensional (2-D) weight distribution of tail-biting codes we give guidelines on how to choose tail biting component codes that are especially suited for parallel concatenated coding schemes. Employing these guidelines, we tabulate tail-biting codes of different rate, length, and complexity. The performance of parallel concatenated block codes (PCBCs) using iterative (turbo) decoding is evaluated by simulation and bounds are calculated in order to study their asymptotic performance.

Conventional implementations of iterative numerical algorithms, especially multigrid methods, merely reach a disappointing small percentage of the theoretically available CPU performance when applied to representative large problems.One of the most important reasons for this phenomenon is that the current DRAM technology cannot provide the data fast enough to keep the CPU busy.Although the fundamentals of cache optimizations are quite simple, current compilers cannot optimize even elementary iterative schemes.In this paper, we analyze the memory and cache behavior of iterative methods with extensive profiling and describe program transformation techniques to improve the cache performance of two-and three-dimensional multigrid algorithms.This project is partially funded by DFG Ru 422/7-1,2.1 All benchmarks in the article were compiled with native FORTRAN77 compilers and aggressive optimizations enabled.On the Intel platform we used egcs (V2.91.60).The platforms include an Intel PentiumII Xeon PC (450 MHz, 450 MFLOPS), a SUN Ultra 60 (296 MHz, 592 MFLOPS), a HP SPP2200 Convex Exemplar Node (200 MHz, 800 MFLOPS), a Compaq PWS 500au (500 MHz, 1 GFLOPS), and a Compaq XP1000 (500 MHz, 1 GFLOPS).1

The Golay convolutional code and related block codes are investigated from an application point of view. We calculate the distance spectrum of the Golay convolutional code and the weight distributions of the block codes using the transfer-matrix method. The performance of the considered codes is shown and discussed. Moreover, the codes are applied as component codes of parallel concatenated codes which are iteratively decoded.

Distributed shared objects are a well known approach to achieve independence of the memory model for parallel programming. The illusion of shared (global) objects is a convenient abstraction which leads to ease of programming on both kinds of parallel architectures, shared memory and distributed memory machines. We present several different implementation variants for distributed shared objects on distributed platforms. We have considered these variants while implementing a high level parallel programming model known as coordinators (J. Knopp, 1996). These are global objects coordinating accesses to the encapsulated data according to statically defined access patterns. Coordinators have been implemented on both shared memory multiprocessors and networks of workstations (NOWs). We describe their implementation as distributed shared objects and give basic performance results on a NOW.