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This paper presents design, implementation and performance results of a new modular, parallel, agent-based and large scale crowd simulation environment. A parallel application, implemented with C and MPI, was implemented and run in this parallel environment for simulation and visualization of an evacuation scenario at Gdansk University of Technology, Poland and further in the area of districts of Gdansk. The application uses a parallel MPI I/O run on two different clusters and a two or three node Parallel File System (PFS) to store a current state in a file. In order to make this implementation efficient, we used our previously developed and tuned Byte-addressable Non-volatile RAM Distributed Cache - a solution that allows to access small data chunks from spread locations within a file efficiently. We have presented application execution times versus the number of agents (up to 100000), versus the number of simulation iterations (up to 25000), versus map size (up to 6km 2 ) and versus the number of processes (up to more than 650) showing high speed-ups. [ABSTRACT FROM AUTHOR]

The increasing size of computational clusters results in an increasing probability of failures, which in turn requires application checkpointing in order to survive those failures. Traditional checkpointing requires data to be copied from application memory into persistent storage medium, which increases application execution time as it is usually done in a separate step. In this paper we propose to use emerging byte-addressable non-volatile RAM (NVRAM) as a persistent storage medium and we analyze various methods of making consistent checkpoints with support of MPI one-sided API in order to minimize checkpointing overhead. We test our solution on two applications: HPCCG benchmark and PageRank algorithm. Our experiments showed that NVRAM based checkpointing performs much better than traditional disk based approach. We also simulated different possible latencies and bandwidth of future NVRAM and our experiments showed that only bandwidth had visible impact onto application execution time. [ABSTRACT FROM AUTHOR]