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

Iso-energy-efficiency: An approach to power-constrained parallel computation

Future large scale high performance supercomputer systems require high energy efficiency to achieve exaflops computational power and beyond. Despite the need to understand energy efficiency in high-performance systems, there are few techniques to evaluate energy efficiency at scale. In this paper, we propose a system-level iso-energy-efficiency model to analyze, evaluate and predict energy-performance of data intensive parallel applications with various execution patterns running on large scale power-aware clusters. Our analytical model can help users explore the effects of machine and application dependent characteristics on system energy efficiency and isolate efficient ways to scale system parameters (e.g. processor count, CPU power/frequency, workload size and network bandwidth) to balance energy use and performance. We derive our iso-energy-efficiency model and apply it to the NAS Parallel Benchmarks on two power-aware clusters. Our results indicate that the model accurately predicts total system energy consumption within 5% error on average for parallel applications with various execution and communication patterns. We demonstrate effective use of the model for various application contexts and in scalability decision-making

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

Energy-Efficient, Thermal-Aware Modeling and Simulation of Datacenters: The CoolEmAll Approach and Evaluation Results

International audienceThis paper describes the CoolEmAll project and its approach for modeling and simulating energy-efficient and thermal-aware data centers. The aim of the project was to address energy-thermal efficiency of data centers by combining the optimization of IT, cooling and workload management. This paper provides a complete data center model considering the workload profiles, the applications profiling, the power model and a cooling model. Different energy efficiency metrics are proposed and various resource management and scheduling policies are presented. The proposed strategies are validated through simulation at different levels of a data cente

Reliability-oriented resource management for High-Performance Computing

Reliability is an increasingly pressing issue for High-Performance Computing systems, as failures are a threat to large-scale applications, for which an even single run may incur significant energy and billing costs. Currently, application developers need to address reliability explicitly, by integrating application-specific checkpoint/restore mechanisms. However, the application alone cannot exploit system knowledge, which is not the case for system-wide resource management systems. In this paper, we propose a reliability-oriented policy that can increase significantly component reliability by combining checkpoint/restore mechanisms exploitation and proactive resource management policies

Coordinated Autonomic Managers for Energy Efficient Date Centers

The complexity of today’s data centers has led researchers to investigate ways in which autonomic methods can be used for data center management. Autonomic managers try to monitor and manage resources to ensure that the components they manage are self-configuring, self-optimizing, self-healing and self-protecting (so called “self-*” properties). In this research, we consider autonomic management systems for data centers with a particular focus on making data centers more energy-aware. In particular, we consider a policy based, multi-manager autonomic management systems for energy aware data centers. Our focus is on defining the foundations – the core concepts, entities, relationships and algorithms - for autonomic management systems capable of supporting a range of management configurations. Central to our approach is the notion of a “topology” of autonomic managers that when instantiated can support a range of different configurations of autonomic managers and communication among them. The notion of “policy” is broadened to enable some autonomic managers to have more direct control over the behavior of other managers through changes in policies. The ultimate goal is to create a management framework that would allow the data center administrator to a) define managed objects, their corresponding managers, management system topology, and policies to meet their operation needs and b) rely on the management system to maintain itself automatically. A data center simulator that computes its energy consumption (computing and cooling) at any given time is implemented to evaluate the impact of different management scenarios. The management system is evaluated with different management scenarios in our simulated data center

Evaluation of DVFS techniques on modern HPC processors and accelerators for energy-aware applications

Energy efficiency is becoming increasingly important for computing systems, in particular for large scale HPC facilities. In this work we evaluate, from an user perspective, the use of Dynamic Voltage and Frequency Scaling (DVFS) techniques, assisted by the power and energy monitoring capabilities of modern processors in order to tune applications for energy efficiency. We run selected kernels and a full HPC application on two high-end processors widely used in the HPC context, namely an NVIDIA K80 GPU and an Intel Haswell CPU. We evaluate the available trade-offs between energy-to-solution and time-to-solution, attempting a function-by-function frequency tuning. We finally estimate the benefits obtainable running the full code on a HPC multi-GPU node, with respect to default clock frequency governors. We instrument our code to accurately monitor power consumption and execution time without the need of any additional hardware, and we enable it to change CPUs and GPUs clock frequencies while running. We analyze our results on the different architectures using a simple energy-performance model, and derive a number of energy saving strategies which can be easily adopted on recent high-end HPC systems for generic applications