Efficient processor management strategies for multicomputer systems

Multicomputers are cost-effective alternatives to the conventional supercomputers. Contemporary processor management schemes tend to underutilize the processors and leave many of the processors in the system idle while jobs are waiting for execution;Instead of designing faster processors or interconnection networks, a substantial performance improvement can be obtained by implementing better processor management strategies. This dissertation studies the performance issues related to the processor management schemes and proposes several ways to enhance the multicomputer systems by means of processor management. The proposed schemes incorporate the concepts of size-reduction, non-contiguous allocation, as well as job migration. Job scheduling using a bypass-queue is also studied. All the proposed schemes are proven effective in improving the system performance via extensive simulations. Each proposed scheme has different implementation cost and constraints. In order to take advantage of these schemes, judicious selection of system parameters is important and is discussed

A Survey and Comparative Study of Hard and Soft Real-time Dynamic Resource Allocation Strategies for Multi/Many-core Systems

Multi-/many-core systems are envisioned to satisfy the ever-increasing performance requirements of complex applications in various domains such as embedded and high-performance computing. Such systems need to cater to increasingly dynamic workloads, requiring efficient dynamic resource allocation strategies to satisfy hard or soft real-time constraints. This article provides an extensive survey of hard and soft real-time dynamic resource allocation strategies proposed since the mid-1990s and highlights the emerging trends for multi-/many-core systems. The survey covers a taxonomy of the resource allocation strategies and considers their various optimization objectives, which have been used to provide comprehensive comparison. The strategies employ various principles, such as market and biological concepts, to perform the optimizations. The trend followed by the resource allocation strategies, open research challenges, and likely emerging research directions have also been provided

Adaptive Knobs for Resource Efficient Computing

Performance demands of emerging domains such as artificial intelligence, machine learning and vision, Internet-of-things etc., continue to grow. Meeting such requirements on modern multi/many core systems with higher power densities, fixed power and energy budgets, and thermal constraints exacerbates the run-time management challenge. This leaves an open problem on extracting the required performance within the power and energy limits, while also ensuring thermal safety. Existing architectural solutions including asymmetric and heterogeneous cores and custom acceleration improve performance-per-watt in specific design time and static scenarios. However, satisfying applications’ performance requirements under dynamic and unknown workload scenarios subject to varying system dynamics of power, temperature and energy requires intelligent run-time management. Adaptive strategies are necessary for maximizing resource efficiency, considering i) diverse requirements and characteristics of concurrent applications, ii) dynamic workload variation, iii) core-level heterogeneity and iv) power, thermal and energy constraints. This dissertation proposes such adaptive techniques for efficient run-time resource management to maximize performance within fixed budgets under unknown and dynamic workload scenarios. Resource management strategies proposed in this dissertation comprehensively consider application and workload characteristics and variable effect of power actuation on performance for pro-active and appropriate allocation decisions. Specific contributions include i) run-time mapping approach to improve power budgets for higher throughput, ii) thermal aware performance boosting for efficient utilization of power budget and higher performance, iii) approximation as a run-time knob exploiting accuracy performance trade-offs for maximizing performance under power caps at minimal loss of accuracy and iv) co-ordinated approximation for heterogeneous systems through joint actuation of dynamic approximation and power knobs for performance guarantees with minimal power consumption. The approaches presented in this dissertation focus on adapting existing mapping techniques, performance boosting strategies, software and dynamic approximations to meet the performance requirements, simultaneously considering system constraints. The proposed strategies are compared against relevant state-of-the-art run-time management frameworks to qualitatively evaluate their efficacy

Allocation Strategies for Data-Oriented Architectures

Data orientation is a common design principle in distributed data management systems. In contrast to process-oriented or transaction-oriented system designs, data-oriented architectures are based on data locality and function shipping. The tight coupling of data and processing thereon is implemented in different systems in a variety of application scenarios such as data analysis, database-as-a-service, and data management on multiprocessor systems. Data-oriented systems, i.e., systems that implement a data-oriented architecture, bundle data and operations together in tasks which are processed locally on the nodes of the distributed system. Allocation strategies, i.e., methods that decide the mapping from tasks to nodes, are core components in data-oriented systems. Good allocation strategies can lead to balanced systems while bad allocation strategies cause skew in the load and therefore suboptimal application performance and infrastructure utilization. Optimal allocation strategies are hard to find given the complexity of the systems, the complicated interactions of tasks, and the huge solution space. To ensure the scalability of data-oriented systems and to keep them manageable with hundreds of thousands of tasks, thousands of nodes, and dynamic workloads, fast and reliable allocation strategies are mandatory. In this thesis, we develop novel allocation strategies for data-oriented systems based on graph partitioning algorithms. Therefore, we show that systems from different application scenarios with different abstraction levels can be generalized to generic infrastructure and workload descriptions. We use weighted graph representations to model infrastructures with bounded and unbounded, i.e., overcommited, resources and possibly non-linear performance characteristics. Based on our generalized infrastructure and workload model, we formalize the allocation problem, which seeks valid and balanced allocations that minimize communication. Our allocation strategies partition the workload graph using solution heuristics that work with single and multiple vertex weights. Novel extensions to these solution heuristics can be used to balance penalized and secondary graph partition weights. These extensions enable the allocation strategies to handle infrastructures with non-linear performance behavior. On top of the basic algorithms, we propose methods to incorporate heterogeneous infrastructures and to react to changing workloads and infrastructures by incrementally updating the partitioning. We evaluate all components of our allocation strategy algorithms and show their applicability and scalability with synthetic workload graphs. In end-to-end--performance experiments in two actual data-oriented systems, a database-as-a-service system and a database management system for multiprocessor systems, we prove that our allocation strategies outperform alternative state-of-the-art methods

Proceedings of the 5th International Workshop on Reconfigurable Communication-centric Systems on Chip 2010 - ReCoSoC\u2710 - May 17-19, 2010 Karlsruhe, Germany. (KIT Scientific Reports ; 7551)

ReCoSoC is intended to be a periodic annual meeting to expose and discuss gathered expertise as well as state of the art research around SoC related topics through plenary invited papers and posters. The workshop aims to provide a prospective view of tomorrow\u27s challenges in the multibillion transistor era, taking into account the emerging techniques and architectures exploring the synergy between flexible on-chip communication and system reconfigurability

Hardware/Software Co-design for Multicore Architectures

Siirretty Doriast

Run-time management for future MPSoC platforms

In recent years, we are witnessing the dawning of the Multi-Processor Systemon- Chip (MPSoC) era. In essence, this era is triggered by the need to handle more complex applications, while reducing overall cost of embedded (handheld) devices. This cost will mainly be determined by the cost of the hardware platform and the cost of designing applications for that platform. The cost of a hardware platform will partly depend on its production volume. In turn, this means that ??exible, (easily) programmable multi-purpose platforms will exhibit a lower cost. A multi-purpose platform not only requires ??exibility, but should also combine a high performance with a low power consumption. To this end, MPSoC devices integrate computer architectural properties of various computing domains. Just like large-scale parallel and distributed systems, they contain multiple heterogeneous processing elements interconnected by a scalable, network-like structure. This helps in achieving scalable high performance. As in most mobile or portable embedded systems, there is a need for low-power operation and real-time behavior. The cost of designing applications is equally important. Indeed, the actual value of future MPSoC devices is not contained within the embedded multiprocessor IC, but in their capability to provide the user of the device with an amount of services or experiences. So from an application viewpoint, MPSoCs are designed to ef??ciently process multimedia content in applications like video players, video conferencing, 3D gaming, augmented reality, etc. Such applications typically require a lot of processing power and a signi??cant amount of memory. To keep up with ever evolving user needs and with new application standards appearing at a fast pace, MPSoC platforms need to be be easily programmable. Application scalability, i.e. the ability to use just enough platform resources according to the user requirements and with respect to the device capabilities is also an important factor. Hence scalability, ??exibility, real-time behavior, a high performance, a low power consumption and, ??nally, programmability are key components in realizing the success of MPSoC platforms. The run-time manager is logically located between the application layer en the platform layer. It has a crucial role in realizing these MPSoC requirements. As it abstracts the platform hardware, it improves platform programmability. By deciding on resource assignment at run-time and based on the performance requirements of the user, the needs of the application and the capabilities of the platform, it contributes to ??exibility, scalability and to low power operation. As it has an arbiter function between different applications, it enables real-time behavior. This thesis details the key components of such an MPSoC run-time manager and provides a proof-of-concept implementation. These key components include application quality management algorithms linked to MPSoC resource management mechanisms and policies, adapted to the provided MPSoC platform services. First, we describe the role, the responsibilities and the boundary conditions of an MPSoC run-time manager in a generic way. This includes a de??nition of the multiprocessor run-time management design space, a description of the run-time manager design trade-offs and a brief discussion on how these trade-offs affect the key MPSoC requirements. This design space de??nition and the trade-offs are illustrated based on ongoing research and on existing commercial and academic multiprocessor run-time management solutions. Consequently, we introduce a fast and ef??cient resource allocation heuristic that considers FPGA fabric properties such as fragmentation. In addition, this thesis introduces a novel task assignment algorithm for handling soft IP cores denoted as hierarchical con??guration. Hierarchical con??guration managed by the run-time manager enables easier application design and increases the run-time spatial mapping freedom. In turn, this improves the performance of the resource assignment algorithm. Furthermore, we introduce run-time task migration components. We detail a new run-time task migration policy closely coupled to the run-time resource assignment algorithm. In addition to detailing a design-environment supported mechanism that enables moving tasks between an ISP and ??ne-grained recon??gurable hardware, we also propose two novel task migration mechanisms tailored to the Network-on-Chip environment. Finally, we propose a novel mechanism for task migration initiation, based on reusing debug registers in modern embedded microprocessors. We propose a reactive on-chip communication management mechanism. We show that by exploiting an injection rate control mechanism it is possible to provide a communication management system capable of providing a soft (reactive) QoS in a NoC. We introduce a novel, platform independent run-time algorithm to perform quality management, i.e. to select an application quality operating point at run-time based on the user requirements and the available platform resources, as reported by the resource manager. This contribution also proposes a novel way to manage the interaction between the quality manager and the resource manager. In order to have a the realistic, reproducible and ??exible run-time manager testbench with respect to applications with multiple quality levels and implementation tradev offs, we have created an input data generation tool denoted Pareto Surfaces For Free (PSFF). The the PSFF tool is, to the best of our knowledge, the ??rst tool that generates multiple realistic application operating points either based on pro??ling information of a real-life application or based on a designer-controlled random generator. Finally, we provide a proof-of-concept demonstrator that combines these concepts and shows how these mechanisms and policies can operate for real-life situations. In addition, we show that the proposed solutions can be integrated into existing platform operating systems

Physical parameter-aware Networks-on-Chip design

PhD ThesisNetworks-on-Chip (NoCs) have been proposed as a scalable, reliable and power-efficient communication fabric for chip multiprocessors (CMPs) and multiprocessor systems-on-chip (MPSoCs). NoCs determine both the performance and the reliability of such systems, with a significant power demand that is expected to increase due to developments in both technology and architecture. In terms of architecture, an important trend in many-core systems architecture is to increase the number of cores on a chip while reducing their individual complexity. This trend increases communication power relative to computation power. Moreover, technology-wise, power-hungry wires are dominating logic as power consumers as technology scales down. For these reasons, the design of future very large scale integration (VLSI) systems is moving from being computation-centric to communication-centric. On the other hand, chip’s physical parameters integrity, especially power and thermal integrity, is crucial for reliable VLSI systems. However, guaranteeing this integrity is becoming increasingly difficult with the higher scale of integration due to increased power density and operating frequencies that result in continuously increasing temperature and voltage drops in the chip. This is a challenge that may prevent further shrinking of devices. Thus, tackling the challenge of power and thermal integrity of future many-core systems at only one level of abstraction, the chip and package design for example, is no longer sufficient to ensure the integrity of physical parameters. New designtime and run-time strategies may need to work together at different levels of abstraction, such as package, application, network, to provide the required physical parameter integrity for these large systems. This necessitates strategies that work at the level of the on-chip network with its rising power budget. This thesis proposes models, techniques and architectures to improve power and thermal integrity of Network-on-Chip (NoC)-based many-core systems. The thesis is composed of two major parts: i) minimization and modelling of power supply variations to improve power integrity; and ii) dynamic thermal adaptation to improve thermal integrity. This thesis makes four major contributions. The first is a computational model of on-chip power supply variations in NoCs. The proposed model embeds a power delivery model, an NoC activity simulator and a power model. The model is verified with SPICE simulation and employed to analyse power supply variations in synthetic and real NoC workloads. Novel observations regarding power supply noise correlation with different traffic patterns and routing algorithms are found. The second is a new application mapping strategy aiming vii to minimize power supply noise in NoCs. This is achieved by defining a new metric, switching activity density, and employing a force-based objective function that results in minimizing switching density. Significant reductions in power supply noise (PSN) are achieved with a low energy penalty. This reduction in PSN also results in a better link timing accuracy. The third contribution is a new dynamic thermal-adaptive routing strategy to effectively diffuse heat from the NoC-based threedimensional (3D) CMPs, using a dynamic programming (DP)-based distributed control architecture. Moreover, a new approach for efficient extension of two-dimensional (2D) partially-adaptive routing algorithms to 3D is presented. This approach improves three-dimensional networkon- chip (3D NoC) routing adaptivity while ensuring deadlock-freeness. Finally, the proposed thermal-adaptive routing is implemented in field-programmable gate array (FPGA), and implementation challenges, for both thermal sensing and the dynamic control architecture are addressed. The proposed routing implementation is evaluated in terms of both functionality and performance. The methodologies and architectures proposed in this thesis open a new direction for improving the power and thermal integrity of future NoC-based 2D and 3D many-core architectures

3rd Many-core Applications Research Community (MARC) Symposium. (KIT Scientific Reports ; 7598)

This manuscript includes recent scientific work regarding the Intel Single Chip Cloud computer and describes approaches for novel approaches for programming and run-time organization