Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

Design and Optimization of Networks-on-Chip for Future Heterogeneous Systems-on-Chip

Due to the tight power budget and reduced time-to-market, Systems-on-Chip (SoC) have emerged as a power-efficient solution that provides the functionality required by target applications in embedded systems. To support a diverse set of applications such as real-time video/audio processing and sensor signal processing, SoCs consist of multiple heterogeneous components, such as software processors, digital signal processors, and application-specific hardware accelerators. These components offer different flexibility, power, and performance values so that SoCs can be designed by mix-and-matching them. With the increased amount of heterogeneous cores, however, the traditional interconnects in an SoC exhibit excessive power dissipation and poor performance scalability. As an alternative, Networks-on-Chip (NoC) have been proposed. NoCs provide modularity at design-time because communications among the cores are isolated from their computations via standard interfaces. NoCs also exploit communication parallelism at run-time because multiple data can be transferred simultaneously. In order to construct an efficient NoC, the communication behaviors of various heterogeneous components in an SoC must be considered with the large amount of NoC design parameters. Therefore, providing an efficient NoC design and optimization framework is critical to reduce the design cycle and address the complexity of future heterogeneous SoCs. This is the thesis of my dissertation. Some existing design automation tools for NoCs support very limited degrees of automation that cannot satisfy the requirements of future heterogeneous SoCs. First, these tools only support a limited number of NoC design parameters. Second, they do not provide an integrated environment for software-hardware co-development. Thus, I propose FINDNOC, an integrated framework for the generation, optimization, and validation of NoCs for future heterogeneous SoCs. The proposed framework supports software-hardware co-development, incremental NoC design-decision model, SystemC-based NoC customization and generation, and fast system protyping with FPGA emulations. Virtual channels (VC) and multiple physical (MP) networks are the two main alternative methods to provide better performance, support quality-of-service, and avoid protocol deadlocks in packet-switched NoC design. To examine the effect of using VCs and MPs with other NoC architectural parameters, I completed a comprehensive comparative analysis that combines an analytical model, synthesis-based designs for both FPGAs and standard-cell libraries, and system-level simulations. Based on the results of this analysis, I developed VENTTI, a design and simulation environment that combines a virtual platform (VP), a NoC synthesis tool, and four NoC models characterized at different abstraction levels. VENTTI facilitates an incremental decision-making process with four NoC abstraction models associated with different NoC parameters. The selected NoC parameters can be validated by running simulations with the corresponding model instantiated in the VP. I augmented this framework to complete FINDNOC by implementing ICON, a NoC generation and customization tool that dynamically combines and customizes synthesizable SystemC components from a predesigned library. Thanks to its flexibility and automatic network interface generation capabilities, ICON can generate a rich variety of NoCs that can be then integrated into any Embedded Scalable Platform (ESP) architectures for fast prototying with FPGA emulations. I designed FINDNOC in a modular way that makes it easy to augmenting it with new capabilities. This, combined with the continuous progress of the ESP design methodology, will provide a seamless SoC integration framework, where the hardware accelerators, software applications, and NoCs can be designed, validated, and integrated simultaneously, in order to reduce the design cycle of future SoC platforms

Driving the Network-on-Chip Revolution to Remove the Interconnect Bottleneck in Nanoscale Multi-Processor Systems-on-Chip

The sustained demand for faster, more powerful chips has been met by the availability of chip manufacturing processes allowing for the integration of increasing numbers of computation units onto a single die. The resulting outcome, especially in the embedded domain, has often been called SYSTEM-ON-CHIP (SoC) or MULTI-PROCESSOR SYSTEM-ON-CHIP (MP-SoC). MPSoC design brings to the foreground a large number of challenges, one of the most prominent of which is the design of the chip interconnection. With a number of on-chip blocks presently ranging in the tens, and quickly approaching the hundreds, the novel issue of how to best provide on-chip communication resources is clearly felt. NETWORKS-ON-CHIPS (NoCs) are the most comprehensive and scalable answer to this design concern. By bringing large-scale networking concepts to the on-chip domain, they guarantee a structured answer to present and future communication requirements. The point-to-point connection and packet switching paradigms they involve are also of great help in minimizing wiring overhead and physical routing issues. However, as with any technology of recent inception, NoC design is still an evolving discipline. Several main areas of interest require deep investigation for NoCs to become viable solutions: • The design of the NoC architecture needs to strike the best tradeoff among performance, features and the tight area and power constraints of the onchip domain. • Simulation and verification infrastructure must be put in place to explore, validate and optimize the NoC performance. • NoCs offer a huge design space, thanks to their extreme customizability in terms of topology and architectural parameters. Design tools are needed to prune this space and pick the best solutions. • Even more so given their global, distributed nature, it is essential to evaluate the physical implementation of NoCs to evaluate their suitability for next-generation designs and their area and power costs. This dissertation performs a design space exploration of network-on-chip architectures, in order to point-out the trade-offs associated with the design of each individual network building blocks and with the design of network topology overall. The design space exploration is preceded by a comparative analysis of state-of-the-art interconnect fabrics with themselves and with early networkon- chip prototypes. The ultimate objective is to point out the key advantages that NoC realizations provide with respect to state-of-the-art communication infrastructures and to point out the challenges that lie ahead in order to make this new interconnect technology come true. Among these latter, technologyrelated challenges are emerging that call for dedicated design techniques at all levels of the design hierarchy. In particular, leakage power dissipation, containment of process variations and of their effects. The achievement of the above objectives was enabled by means of a NoC simulation environment for cycleaccurate modelling and simulation and by means of a back-end facility for the study of NoC physical implementation effects. Overall, all the results provided by this work have been validated on actual silicon layout

Reuse Distance Analysis for Large-Scale Chip Multiprocessors

Multicore Reuse Distance (RD) analysis is a powerful tool that can potentially provide a parallel program's detailed memory behavior. Concurrent Reuse Distance (CRD) and Private-stack Reuse Distance (PRD) measure RD across thread-interleaved memory reference streams, addressing shared and private caches. Sensitivity to memory interleaving makes CRD and PRD profiles architecture dependent, preventing them from analyzing different processor configurations. However such instability is minimal when all threads exhibit similar data-locality patterns. For loop-based parallel programs, interleaving threads are symmetric. CRD and PRD profiles are stable across cache size scaling, and exhibit predictable coherent movement across core count scaling. Hence, multicore RD analysis can provide accurate analysis for different processor configurations. Due to the prevalence of parallel loops, RD analysis will be valuable to multicore designers. This dissertation uses RD analysis to analyze multicore cache performance for loop-based parallel programs. First, we study the impacts of core count scaling and problem size scaling on CRD and PRD profiles. Two application parameters with architectural implications are identified: Ccore and Cshare. Core count scaling only impacts cache performance significantly below Ccore in shared caches, and Cshare is the capacity at which shared caches begin to outperform private caches in terms of data locality. Then, we develop techniques, in particular employing reference groups, to predict the coherent movement of CRD and PRD profiles due to scaling, and achieve accuracy of 80%-96%. After comparing our prediction techniques against profile sampling, we find that the prediction achieves higher speedup and accuracy, especially when the design space is large. Moreover, we evaluate the accuracy of using CRD and PRD profile predictions to estimate multicore cache performance, especially MPKI. When combined with the existing problem scaling prediction, our techniques can predict shared LLC (private L2 cache) MPKI to within 12% (14%) of simulation across 1,728 (1,440) configurations using only 36 measured CRD (PRD) profiles. Lastly, we propose a new framework based on RD analysis to optimize multicore cache hierarchies. Our study not only reveals several new insights, but it also demonstrates that RD analysis can help computer architects improve multicore designs

Parallel Architectures for Many-Core Systems-On-Chip in Deep Sub-Micron Technology

Despite the several issues faced in the past, the evolutionary trend of silicon has kept its constant pace. Today an ever increasing number of cores is integrated onto the same die. Unfortunately, the extraordinary performance achievable by the many-core paradigm is limited by several factors. Memory bandwidth limitation, combined with inefficient synchronization mechanisms, can severely overcome the potential computation capabilities. Moreover, the huge HW/SW design space requires accurate and flexible tools to perform architectural explorations and validation of design choices. In this thesis we focus on the aforementioned aspects: a flexible and accurate Virtual Platform has been developed, targeting a reference many-core architecture. Such tool has been used to perform architectural explorations, focusing on instruction caching architecture and hybrid HW/SW synchronization mechanism. Beside architectural implications, another issue of embedded systems is considered: energy efficiency. Near Threshold Computing is a key research area in the Ultra-Low-Power domain, as it promises a tenfold improvement in energy efficiency compared to super-threshold operation and it mitigates thermal bottlenecks. The physical implications of modern deep sub-micron technology are severely limiting performance and reliability of modern designs. Reliability becomes a major obstacle when operating in NTC, especially memory operation becomes unreliable and can compromise system correctness. In the present work a novel hybrid memory architecture is devised to overcome reliability issues and at the same time improve energy efficiency by means of aggressive voltage scaling when allowed by workload requirements. Variability is another great drawback of near-threshold operation. The greatly increased sensitivity to threshold voltage variations in today a major concern for electronic devices. We introduce a variation-tolerant extension of the baseline many-core architecture. By means of micro-architectural knobs and a lightweight runtime control unit, the baseline architecture becomes dynamically tolerant to variations

Heterogeneous Chip Multiprocessor: Data Representation, Mixed-Signal Processing Tiles, and System Design

With the emergence of big data, the need for more computationally intensive processors that can handle the increased processing demand has risen. Conventional computing paradigms based on the Von Neumann model that separates computational and memory structures have become outdated and less efficient for this increased demand. As the speed and memory density of processors have increased significantly over the years, these models of computing, which rely on a constant stream of data between the processor and memory, see less gains due to finite bandwidth and latency. Moreover, in the presence of extreme scaling, these conventional systems, implemented in submicron integrated circuits, have become even more susceptible to process variability, static leakage current, and more. In this work, alternative paradigms, predicated on distributive processing with robust data representation and mixed-signal processing tiles, are explored for constructing more efficient and scalable computing systems in application specific integrated circuits (ASICs). The focus of this dissertation work has been on heterogeneous chip multi-processor (CMP) design and optimization across different levels of abstraction. On the level of data representation, a different modality of representation based on random pulse density modulation (RPDM) coding is explored for more efficient processing using stochastic computation. On the level of circuit description, mixed-signal integrated circuits that exploit charge-based computing for energy efficient fixed point arithmetic are designed. Consequently, 8 different chips that test and showcase these circuits were fabricated in submicron CMOS processes. Finally, on the architectural level of description, a compact instruction-set processor and controller that facilitates distributive computing on System-On-Chips (SoCs) is designed. In addition to this, a robust bufferless network architecture is designed with a network simulator, and I/O cells are designed for SoCs. The culmination of this thesis work has led to the design and fabrication of a heterogeneous chip multi- processor prototype comprised of over 12,000 VVM cores, warp/dewarp processors, cache, and additional processors, which can be applied towards energy efficient large-scale data processing