Automatic synthesis and optimization of chip multiprocessors

The microprocessor technology has experienced an enormous growth during the last decades. Rapid downscale of the CMOS technology has led to higher operating frequencies and performance densities, facing the fundamental issue of power dissipation. Chip Multiprocessors (CMPs) have become the latest paradigm to improve the power-performance efficiency of computing systems by exploiting the parallelism inherent in applications. Industrial and prototype implementations have already demonstrated the benefits achieved by CMPs with hundreds of cores.CMP architects are challenged to take many complex design decisions. Only a few of them are:- What should be the ratio between the core and cache areas on a chip?- Which core architectures to select?- How many cache levels should the memory subsystem have?- Which interconnect topologies provide efficient on-chip communication?These and many other aspects create a complex multidimensional space for architectural exploration. Design Automation tools become essential to make the architectural exploration feasible under the hard time-to-market constraints. The exploration methods have to be efficient and scalable to handle future generation on-chip architectures with hundreds or thousands of cores.Furthermore, once a CMP has been fabricated, the need for efficient deployment of the many-core processor arises. Intelligent techniques for task mapping and scheduling onto CMPs are necessary to guarantee the full usage of the benefits brought by the many-core technology. These techniques have to consider the peculiarities of the modern architectures, such as availability of enhanced power saving techniques and presence of complex memory hierarchies.This thesis has several objectives. The first objective is to elaborate the methods for efficient analytical modeling and architectural design space exploration of CMPs. The efficiency is achieved by using analytical models instead of simulation, and replacing the exhaustive exploration with an intelligent search strategy. Additionally, these methods incorporate high-level models for physical planning. The related contributions are described in Chapters 3, 4 and 5 of the document.The second objective of this work is to propose a scalable task mapping algorithm onto general-purpose CMPs with power management techniques, for efficient deployment of many-core systems. This contribution is explained in Chapter 6 of this document.Finally, the third objective of this thesis is to address the issues of the on-chip interconnect design and exploration, by developing a model for simultaneous topology customization and deadlock-free routing in Networks-on-Chip. The developed methodology can be applied to various classes of the on-chip systems, ranging from general-purpose chip multiprocessors to application-specific solutions. Chapter 7 describes the proposed model.The presented methods have been thoroughly tested experimentally and the results are described in this dissertation. At the end of the document several possible directions for the future research are proposed

Evaluation of temperature-performance trade-offs in wireless network-on-chip architectures

Continued scaling of device geometries according to Moore\u27s Law is enabling complete end-user systems on a single chip. Massive multicore processors are enablers for many information and communication technology (ICT) innovations spanning various domains, including healthcare, defense, and entertainment. In the design of high-performance massive multicore chips, power and heat are dominant constraints. Temperature hotspots witnessed in multicore systems exacerbate the problem of reliability in deep submicron technologies. Hence, there is a great need to explore holistic power and thermal optimization and management strategies for the massive multicore chips. High power consumption not only raises chip temperature and cooling cost, but also decreases chip reliability and performance. Thus, addressing thermal concerns at different stages of the design and operation is critical to the success of future generation systems. The performance of a multicore chip is also influenced by its overall communication infrastructure, which is predominantly a Network-on-Chip (NoC). The existing method of implementing a NoC with planar metal interconnects is deficient due to high latency, significant power consumption, and temperature hotspots arising out of long, multi-hop wireline links used in data exchange. On-chip wireless networks are envisioned as an enabling technology to design low power and high bandwidth massive multicore architectures. However, optimizing wireless NoCs for best performance does not necessarily guarantee a thermally optimal interconnection architecture. The wireless links being highly efficient attract very high traffic densities which in turn results in temperature hotspots. Therefore, while the wireless links result in better performance and energy-efficiency, they can also cause temperature hotspots and undermine the reliability of the system. Consequently, the location and utilization of the wireless links is an important factor in thermal optimization of high performance wireless Networks-on-Chip. Architectural innovation in conjunction with suitable power and thermal management strategies is the key for designing high performance yet energy-efficient massive multicore chips. This work contributes to exploration of various the design methodologies for establishing wireless NoC architectures that achieve the best trade-offs between temperature, performance and energy-efficiency. It further demonstrates that incorporating Dynamic Thermal Management (DTM) on a multicore chip designed with such temperature and performance optimized Wireless Network-on-Chip architectures improves thermal profile while simultaneously providing lower latency and reduced network energy dissipation compared to its conventional counterparts

Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections

Cortical synapse organization supports a range of dynamic states on multiple spatial and temporal scales, from synchronous slow wave activity (SWA), characteristic of deep sleep or anesthesia, to fluctuating, asynchronous activity during wakefulness (AW). Such dynamic diversity poses a challenge for producing efficient large-scale simulations that embody realistic metaphors of short- and long-range synaptic connectivity. In fact, during SWA and AW different spatial extents of the cortical tissue are active in a given timespan and at different firing rates, which implies a wide variety of loads of local computation and communication. A balanced evaluation of simulation performance and robustness should therefore include tests of a variety of cortical dynamic states. Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which reflects the modular organization of the cortex. We explored networks up to 192x192 modules, each composed of 1250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying inter-modular synaptic connectivity with varying spatial decay constant. For the largest networks the total number of synapses was over 70 billion. The execution platform included up to 64 dual-socket nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz clock rates. Network initialization time, memory usage, and execution time showed good scaling performances from 1 to 1024 processes, implemented using the standard Message Passing Interface (MPI) protocol. We achieved simulation speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both cortical states in the explored range of inter-modular interconnections.Comment: 22 pages, 9 figures, 4 table