Modern computational workloads require abundant thread level parallelism (TLP), necessitating highly-parallel, manycore accelerators such as General Purpose Graphics Processing Units (GPGPUs). GPGPUs place a heavy demand on the on-chip interconnect between the many cores and a few memory controllers (MCs). Thus, traffic is highly asymmetric, impacting on-chip resource utilization and system performance. Here, we analyze the communication demands of typical GPGPU applications, and propose efficient Network-on-Chip (NoC) designs to meet those demands. We show that the proposed schemes improve performance by up to 64.7%. Compared to the best of class prior work, our VC monopolizing and partitioning schemes improve performance by 25%.

The popularity of multimedia streaming services via wireless networks presents major challenges in the management of network bandwidth. One challenge is to quickly and precisely estimate the available bandwidth for the decision of streaming rates of layered and scalable multimedia services. Previous studies based on wired networks are too burdensome to be applied to multimedia applications in wireless networks. In this paper, a new method, IdleGap, is suggested to estimate the available bandwidth of a wireless LAN based on the information from a low layer in the protocol stack. We use a network simulation tool, NS-2, to evaluate our new method with various ranges of cross-traffic and observation times. Our simulation results show that IdleGap accurately estimates the available bandwidth for all ranges of cross-traffic (100 Kbps ∼ 1 Mbps) with a very short observation time of 10 seconds.

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