Neural Simulations on Multi-Core Architectures

Neuroscience is witnessing increasing knowledge about the anatomy and electrophysiological properties of neurons and their connectivity, leading to an ever increasing computational complexity of neural simulations. At the same time, a rather radical change in personal computer technology emerges with the establishment of multi-cores: high-density, explicitly parallel processor architectures for both high performance as well as standard desktop computers. This work introduces strategies for the parallelization of biophysically realistic neural simulations based on the compartmental modeling technique and results of such an implementation, with a strong focus on multi-core architectures and automation, i.e. user-transparent load balancing

High Performance Computing using Infiniband-based clusters

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Efficient Lookahead Routing and Header Compression For Multicasting in Networks-On-Chip

With advancing technology, Chip Multi-processor (CMP) architectures have emerged as a viable solution for designing processors. Networks-On-Chip (NOCs) provide a scalable communication method for CMP architectures with increasing numbers of cores. Although there has been significant research on NOC designs for unicast traffic, the research on the multicast router design is still in its infant stage. Considering that one-to-many (multicast) and one-to-all (broadcast) traffic are more common in CMP applications, it is important to design a router providing efficient multicasting. In this thesis, a lookahead multicast routing algorithm with limited area overhead is proposed. This lookahead algorithm reduces network latency by removing the need for a separate routing computation (RC) stage. An efficient area optimization technique is put forward to achieve minimal area overhead for the lookahead RC stage. Also, a novel compression scheme is proposed for multicast packet headers to alleviate their big overhead in large networks. Comprehensive simulation results show that with the new route computation logic design and area overhead optimization, providing lookahead routing in the multicast router only costs less than 20 percent area overhead and this percentage keeps decreasing with larger network sizes. Compared with the basic lookahead routing design, our design can save area by over 50 percent. With header compression and lookahead multicast routing, the network performance can be improved on an average by 22 percent for a (16 x 16) network