Task mapping for non-contiguous allocations.

This paper examines task mapping algorithms for non-contiguously allocated parallel jobs. Several studies have shown that task placement affects job running time for both contiguously and non-contiguously allocated jobs. Traditionally, work on task mapping either uses a very general model where the job has an arbitrary communication pattern or assumes that jobs are allocated contiguously, making them completely isolated from each other. A middle ground between these two cases is the mapping problem for non-contiguous jobs having a specific communication pattern. We propose several task mapping algorithms for jobs with a stencil communication pattern and evaluate them using experiments and simulations. Our strategies improve the running time of a MiniApp by as much as 30% over a baseline strategy. Furthermore, this improvement increases markedly with the job size, demonstrating the importance of task mapping as systems grow toward exascale

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

Modeling and optimization of high-performance many-core systems for energy-efficient and reliable computing

Thesis (Ph.D.)--Boston UniversityMany-core systems, ranging from small-scale many-core processors to large-scale high performance computing (HPC) data centers, have become the main trend in computing system design owing to their potential to deliver higher throughput per watt. However, power densities and temperatures increase following the growth in the performance capacity, and bring major challenges in energy efficiency, cooling costs, and reliability. These challenges require a joint assessment of performance, power, and temperature tradeoffs as well as the design of runtime optimization techniques that monitor and manage the interplay among them. This thesis proposes novel modeling and runtime management techniques that evaluate and optimize the performance, energy, and reliability of many-core systems. We first address the energy and thermal challenges in 3D-stacked many-core processors. 3D processors with stacked DRAM have the potential to dramatically improve performance owing to lower memory access latency and higher bandwidth. However, the performance increase may cause 3D systems to exceed the power budgets or create thermal hot spots. In order to provide an accurate analysis and enable the design of efficient management policies, this thesis introduces a simulation framework to jointly analyze performance, power, and temperature for 3D systems. We then propose a runtime optimization policy that maximizes the system performance by characterizing the application behavior and predicting the operating points that satisfy the power and thermal constraints. Our policy reduces the energy-delay product (EDP) by up to 61.9% compared to existing strategies. Performance, cooling energy, and reliability are also critical aspects in HPC data centers. In addition to causing reliability degradation, high temperatures increase the required cooling energy. Communication cost, on the other hand, has a significant impact on system performance in HPC data centers. This thesis proposes a topology-aware technique that maximizes system reliability by selecting between workload clustering and balancing. Our policy improves the system reliability by up to 123.3% compared to existing temperature balancing approaches. We also introduce a job allocation methodology to simultaneously optimize the communication cost and the cooling energy in a data center. Our policy reduces the cooling cost by 40% compared to cooling-aware and performance-aware policies, while achieving comparable performance to performance-aware policy

Improving efficiency and resilience in large-scale computing systems through analytics and data-driven management

Applications running in large-scale computing systems such as high performance computing (HPC) or cloud data centers are essential to many aspects of modern society, from weather forecasting to financial services. As the number and size of data centers increase with the growing computing demand, scalable and efficient management becomes crucial. However, data center management is a challenging task due to the complex interactions between applications, middleware, and hardware layers such as processors, network, and cooling units. This thesis claims that to improve robustness and efficiency of large-scale computing systems, significantly higher levels of automated support than what is available in today's systems are needed, and this automation should leverage the data continuously collected from various system layers. Towards this claim, we propose novel methodologies to automatically diagnose the root causes of performance and configuration problems and to improve efficiency through data-driven system management. We first propose a framework to diagnose software and hardware anomalies that cause undesired performance variations in large-scale computing systems. We show that by training machine learning models on resource usage and performance data collected from servers, our approach successfully diagnoses 98% of the injected anomalies at runtime in real-world HPC clusters with negligible computational overhead. We then introduce an analytics framework to address another major source of performance anomalies in cloud data centers: software misconfigurations. Our framework discovers and extracts configuration information from cloud instances such as containers or virtual machines. This is the first framework to provide comprehensive visibility into software configurations in multi-tenant cloud platforms, enabling systematic analysis for validating the correctness of software configurations. This thesis also contributes to the design of robust and efficient system management methods that leverage continuously monitored resource usage data. To improve performance under power constraints, we propose a workload- and cooling-aware power budgeting algorithm that distributes the available power among servers and cooling units in a data center, achieving up to 21% improvement in throughput per Watt compared to the state-of-the-art. Additionally, we design a network- and communication-aware HPC workload placement policy that reduces communication overhead by up to 30% in terms of hop-bytes compared to existing policies.2019-07-02T00:00:00

Algorithmic support for commodity-based parallel computing systems.

The Computational Plant or Cplant is a commodity-based distributed-memory supercomputer under development at Sandia National Laboratories. Distributed-memory supercomputers run many parallel programs simultaneously. Users submit their programs to a job queue. When a job is scheduled to run, it is assigned to a set of available processors. Job runtime depends not only on the number of processors but also on the particular set of processors assigned to it. Jobs should be allocated to localized clusters of processors to minimize communication costs and to avoid bandwidth contention caused by overlapping jobs. This report introduces new allocation strategies and performance metrics based on space-filling curves and one dimensional allocation strategies. These algorithms are general and simple. Preliminary simulations and Cplant experiments indicate that both space-filling curves and one-dimensional packing improve processor locality compared to the sorted free list strategy previously used on Cplant. These new allocation strategies are implemented in Release 2.0 of the Cplant System Software that was phased into the Cplant systems at Sandia by May 2002. Experimental results then demonstrated that the average number of communication hops between the processors allocated to a job strongly correlates with the job's completion time. This report also gives processor-allocation algorithms for minimizing the average number of communication hops between the assigned processors for grid architectures. The associated clustering problem is as follows: Given n points in {Re}d, find k points that minimize their average pairwise L{sub 1} distance. Exact and approximate algorithms are given for these optimization problems. One of these algorithms has been implemented on Cplant and will be included in Cplant System Software, Version 2.1, to be released. In more preliminary work, we suggest improvements to the scheduler separate from the allocator

Scalable Task Schedulers for Many-Core Architectures

This thesis develops schedulers for many-cores with different optimization objectives. The proposed schedulers are designed to be scale up as the number of cores in many-cores increase while continuing to provide guarantees on the quality of the schedule

Topology Agnostic Methods for Routing, Reconfiguration and Virtualization of Interconnection Networks

Modern computing systems, such as supercomputers, data centers and multicore chips, generally require efficient communication between their different system units; tolerance towards component faults; flexibility to expand or merge; and a high utilization of their resources. Interconnection networks are used in a variety of such computing systems in order to enable communication between their diverse system units. Investigation and proposal of new or improved solutions to topology agnostic routing and reconfiguration of interconnection networks are main objectives of this thesis. In addition, topology agnostic routing and reconfiguration algorithms are utilized in the development of new and flexible approaches to processor allocation. The thesis aims to present versatile solutions that can be used for the interconnection networks of a number of different computing systems. No particular routing algorithm was specified for an interconnection network technology which is now incorporated in Dolphin Express. The thesis states a set of criteria for a suitable routing algorithm, evaluates a number of existing routing algorithms, and recommend that one of the algorithms – which fulfils all of the criteria – is used. Further investigations demonstrate how this routing algorithm inherently supports fault-tolerance, and how it can be optimized for some network topologies. These considerations are also relevant for the InfiniBand interconnection network technology. Reconfiguration of interconnection networks (change of routing function) is a deadlock prone process. Some existing reconfiguration strategies include deadlock avoidance mechanisms that significantly reduce the network service offered to running applications. The thesis expands the area of application for one of the most versatile and efficient reconfiguration algorithms available in the literature, and proposes an optimization of this algorithm that improves the network service offered to running applications. Moreover, a new reconfiguration algorithm is presented that supports a replacement of the routing function without causing performance penalties. Processor allocation strategies that guarantee traffic-containment commonly pose strict requirements on the shape of partitions, and thus achieve only a limited utilization of a system’s computing resources. The thesis introduces two new approaches that are more flexible. Both approaches utilize the properties of a topology agnostic routing algorithm in order to enforce traffic-containment within arbitrarily shaped partitions. Consequently, a high resource utilization as well as isolation of traffic between different partitions is achieved