Browsing by Subject "computer architecture"
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Item Architectural Support for High-Performance, Power-Efficient and Secure Multiprocessor Systems(2012-10-19) An, Baik SongHigh performance systems have been widely adopted in many fields and the demand for better performance is constantly increasing. And the need of powerful yet flexible systems is also increasing to meet varying application requirements from diverse domains. Also, power efficiency in high performance computing has been one of the major issues to be resolved. The power density of core components becomes significantly higher, and the fraction of power supply in total management cost is dominant. Providing dependability is also a main concern in large-scale systems since more hardware resources can be abused by attackers. Therefore, designing high-performance, power-efficient and secure systems is crucial to provide adequate performance as well as reliability to users. Adhering to using traditional design methodologies for large-scale computing systems has a limit to meet the demand under restricted resource budgets. Interconnecting a large number of uniprocessor chips to build parallel processing systems is not an efficient solution in terms of performance and power. Chip multiprocessor (CMP) integrates multiple processing cores and caches on a chip and is thought of as a good alternative to previous design trends. In this dissertation, we deal with various design issues of high performance multiprocessor systems based on CMP to achieve both performance and power efficiency while maintaining security. First, we propose a fast and secure off-chip interconnects through minimizing network overheads and providing an efficient security mechanism. Second, we propose architectural support for fast and efficient memory protection in CMP systems, making the best use of the characteristics in CMP environments and multi-threaded workloads. Third, we propose a new router design for network-on-chip (NoC) based on a new memory technique. We introduce hybrid input buffers that use both SRAM and STT-MRAM for better performance as well as power efficiency. Simulation results show that the proposed schemes improve the performance of off-chip networks through reducing the message size by 54% on average. Also, the schemes diminish the overheads of bounds checking operations, thus enhancing the overall performance by 11% on average. Adopting hybrid buffers in NoC routers contributes to increasing the network throughput up to 21%.Item GCA: Global Congestion Awareness for Load Balance in Networks-on-Chip(2012-10-19) Ramakrishna, MukundAs modern CMPs scale to ever increasing core counts, Networks-on-Chip (NoCs) are emerging as an interconnection fabric, enabling communication between components. While NoCs are easy to implement and provide high and scalable bandwidth, current routing algorithms, such as dimension-ordered routing, suffer from poor load balance, leading to reduced throughput and high latencies. Improving load balance, hence, is critical in future CMP designs where increased latency leads to wasted power and energy waiting for outstanding requests to resolve. Adaptive routing is a known technique to improve load balance; however, prior adaptive routing techniques either use local, myopic information or misinformed, regionally-aggregated information to form their routing decisions. This thesis proposes a new, light-weight, adaptive routing algorithm for on-chip routers based on global link state and congestion information, Global Congestion Awareness (GCA). GCA leverages unused bits in existing packet header flits to "piggyback" congestion state information around the network and uses a simple, low-complexity route calculation unit, to calculate optimal packet paths to their destination without the myopia of local decisions, nor the aggregation of unrelated status information, found in prior designs. In particular GCA outperforms local adaptive routing by up to 82%, Regional Congestion Awareness (RCA) by up to 51%, and a recent competing adaptive routing algorithm, DAR, by 8% on average on realistic workloads.