Abstract. Loop scheduling scheme plays a critical role in the efficient execution of programs, especially loop dominated applications. This paper presents KASS, a knowledge-based adaptive loop scheduling scheme. KASS consists of two phases: static partitioning and dynamic scheduling. To balance the workload, the knowledge of loop features and the capabilities of processors are both taken into account using a heuristic approach in static partitioning phase. In dynamic scheduling phase, an adaptive self-scheduling algorithm is applied, in which two tuning parameters are set to control chunk sizes, aiming at load balancing and minimizing synchronization overhead. In addition, we extend KASS to apply on loop nests and adjust the chunk sizes at runtime. The experimental results show that KASS performs 4.8% to 16.9% better than the existing self-scheduling schemes, and up to 21% better than the affinity scheduling scheme.

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The crosstalk delay associated with global on-chip interconnects becomes more severe in deep submicron technology, and hence can greatly affect the overall system performance. Based on a delay model proposed by Sotiriadis et al., transition patterns over a bus can be classified according to their delays. Using this classification, crosstalk avoidance codes (CACs) have been proposed to alleviate the crosstalk delays by restricting the transition patterns on a bus. In this paper, we first propose a new classification of transition patterns, and then devise a new family of CACs based on this classification. In comparison to the previous classification, our classification has more classes and the delays of its classes do not overlap, both leading to more accurate control of delays. Our new family of CACs includes some previously proposed codes as well as new codes with reduced delays and improved throughput. Thus, this new family of crosstalk avoidance codes provides a wider variety of tradeoffs between bus delay and efficiency. Finally, since our analytical approach to the classification and CACs treats the technologydependent parameters as variables, our approach can be easily adapted to a wide variety of technology.

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