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Recent trends in processor and memory systems in large-scale computing systems reveal a new "memory wall" that prompts investigation on alternate main memory organization separating main memory from processors and arranging them in separate ensembles. In this paper, we study the feasibility of transferring data across processors by using the optical interconnection fabric that acts as a bridge between processor and memory ensembles. We propose a memory switching protocol that transfers data across processors without physically moving the data across electrical switches. Such a mechanism allows large-scale data communication across processors through transfer of a few tiny blocks of meta-data. We present detailed techniques for supporting two communication patterns prevalent in any large-scale scientific and data management applications. We present experimental results analyzing the feasibility of memory switching in a wide range of applications, and characterize applications based on the impact of the memory switching on their performance.

Abstract-The availability of large number of processing nodes in a parallel and distributed computing environment enables sophisticated real time processing over high speed data streams, as required by many emerging applications. Sliding window stream joins are among the most important operators in a stream processing system. In this paper, we consider the issue of parallelizing a sliding window stream join operator over a shared nothing cluster. We propose a framework, based on fixed or predefined communication pattern, to distribute the join processing loads over the shared-nothing cluster. We consider various overheads while scaling over a large number of nodes, and propose solution methodologies to cope with the issues. We implement the algorithm over a cluster using a message passing system, and present the experimental results showing the effectiveness of the join processing algorithm.