2,928 research outputs found

    Cycle-accurate evaluation of reconfigurable photonic networks-on-chip

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    There is little doubt that the most important limiting factors of the performance of next-generation Chip Multiprocessors (CMPs) will be the power efficiency and the available communication speed between cores. Photonic Networks-on-Chip (NoCs) have been suggested as a viable route to relieve the off- and on-chip interconnection bottleneck. Low-loss integrated optical waveguides can transport very high-speed data signals over longer distances as compared to on-chip electrical signaling. In addition, with the development of silicon microrings, photonic switches can be integrated to route signals in a data-transparent way. Although several photonic NoC proposals exist, their use is often limited to the communication of large data messages due to a relatively long set-up time of the photonic channels. In this work, we evaluate a reconfigurable photonic NoC in which the topology is adapted automatically (on a microsecond scale) to the evolving traffic situation by use of silicon microrings. To evaluate this system's performance, the proposed architecture has been implemented in a detailed full-system cycle-accurate simulator which is capable of generating realistic workloads and traffic patterns. In addition, a model was developed to estimate the power consumption of the full interconnection network which was compared with other photonic and electrical NoC solutions. We find that our proposed network architecture significantly lowers the average memory access latency (35% reduction) while only generating a modest increase in power consumption (20%), compared to a conventional concentrated mesh electrical signaling approach. When comparing our solution to high-speed circuit-switched photonic NoCs, long photonic channel set-up times can be tolerated which makes our approach directly applicable to current shared-memory CMPs

    Simulation of 1+1 dimensional surface growth and lattices gases using GPUs

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    Restricted solid on solid surface growth models can be mapped onto binary lattice gases. We show that efficient simulation algorithms can be realized on GPUs either by CUDA or by OpenCL programming. We consider a deposition/evaporation model following Kardar-Parisi-Zhang growth in 1+1 dimensions related to the Asymmetric Simple Exclusion Process and show that for sizes, that fit into the shared memory of GPUs one can achieve the maximum parallelization speedup ~ x100 for a Quadro FX 5800 graphics card with respect to a single CPU of 2.67 GHz). This permits us to study the effect of quenched columnar disorder, requiring extremely long simulation times. We compare the CUDA realization with an OpenCL implementation designed for processor clusters via MPI. A two-lane traffic model with randomized turning points is also realized and the dynamical behavior has been investigated.Comment: 20 pages 12 figures, 1 table, to appear in Comp. Phys. Com

    Queueing analysis of a canonical model of real-time multiprocessors

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    A logical classification of multiprocessor structures from the point of view of control applications is presented. A computation of the response time distribution for a canonical model of a real time multiprocessor is presented. The multiprocessor is approximated by a blocking model. Two separate models are derived: one created from the system's point of view, and the other from the point of view of an incoming task
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