Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Annual Energy Outlook

Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.

Data Center Energy Consumption Modeling: A Survey

To alleviate the complex communication problems that arise as the number of on-chip components increases, network-on-chip (NoC) architectures have been recently proposed to replace global interconnects. In this paper, we first provide a general description of NoC architectures and applications. Then, we enumerate several related research problems organized under five main categories: Application characterization, communication paradigm, communication infrastructure, analysis, and solution evaluation. Motivation, problem description, proposed approaches, and open issues are discussed for each problem from system, microarchitecture, and circuit perspectives. Finally, we address the interactions among these research problems and put the NoC design process into perspective.

Outstanding Research Problems in NoC Design: System, Microarchitecture, and Circuit Perspectives

To accommodate the increasing presence of intermittent renewable energy sources in power generation, electricity providers offer incentives for demand response so as to stabilize the grid load. This paper argues that data centers have the ability to provide large capacity reserves to emerging smart grid programs, and thus, provide opportunities for sustainable data center growth and cost savings as well as more flexibility for the grid. The paper first reviews the emerging smart grid opportunities, and then proposes policies to deliver data center demand response for peak shaving, regulation services, and frequency control prorgrams. Experimental results indicate substantial cost saving potential compared to solely applying energy management strategies at the data centers.

Reducing the data center electricity costs through participation in smart grid programs

With the integration of high-performance multicore processors and multiple accelerators into modern mobile system-on-chips (SoCs), power densities have grown substantially. As a result, thermal management policies, which ensure operation at thermally safe conditions, became essential components of state-of-the-art mobile systems. Traditional thermal throttling approaches aim at maximum utilization of the available thermal headroom to minimize the performance loss and maximize user performance. This paper demonstrates that, in a mobile platform, such greedy techniques can lead to significant degradation in the quality-of-service (QoS) levels as the duration of device activity increases, leading to inconsistent user experience over time. We demonstrate that incorporating user/application QoS requirements into mobile power management to provide "just enough" performance (instead of always maximizing performance) allows for more efficient usage of the thermal headroom, which translates to substantially extended durations of sustainable performance. We propose a closed-loop QoS control policy, including an efficient dynamic voltage and frequency scaling (DVFS) state scheduling technique, to minimize the thermal impact for extending the sustainability of desired QoS levels. Experiments on a modern smartphone show that the proposed technique provides up to 74% longer sustainable performance while meeting the target QoS demands for a variety of real-life applications.

Just Enough is More: Achieving Sustainable Performance in Mobile Devices under Thermal Limitations

3D stacked circuits reduce communication delay in multicore system-on-chips (SoCs) and enable heterogeneous integration of cores, memories, sensors, and RF devices. However, vertical integration of layers exacerbates the reliability and thermal problems, and cooling is a limiting factor in multitier systems. Liquid cooling is a highly efficient solution to overcome the accelerated thermal problems in 3D architectures; however, liquid cooling brings new challenges in modeling and runtime management. This paper proposes a novel controller for improving energy efficiency and reliability in 3D systems through liquid cooling management and dynamic voltage frequency scaling (DVFS). The proposed fuzzy controller adjusts the liquid flow rate at runtime to match the cooling demand for preventing energy wastage of over-cooling and for maintaining a stable thermal profile. The DVFS decisions provide chip-level energy savings and help balancing the temperature across the system. Experimental results on 8- and 16-core multicore SoCs show that the controller prevents the system to exceed the given threshold temperature while reducing cooling energy by up to 50% and system-level energy by up to 21% in comparison to using a static worst-case flow rate setting.

Fuzzy control for enforcing energy efficiency in high-performance 3D systems

Nearly half of the energy in the computing clusters today is consumed by the cooling infrastructure. It is possible to reduce the cooling cost by allowing the data center temperatures to rise; however, component reliability constraints impose thermal thresholds as failure rates are exponentially dependent on the processor temperatures. Existing thermally-aware job allocation policies optimize the cooling costs by minimizing the peak inlet temperatures of the server nodes. An important constraint in high performance computing (HPC) data centers, however, is performance. Specifically, HPC data centers run multi-threaded applications with significant communication among the threads. Thus, performance of such applications is strongly affected by the job allocation decisions. This paper proposes a novel job allocation methodology to jointly minimize communication cost of an HPC application while also reducing the cooling energy. The proposed method also considers temperature-dependent hardware reliability as part of the optimization.

Optimizing communication and cooling costs in HPC data centers via intelligent job allocation

Some embodiments of the present invention provide a system that controls temperature variations in a computer system. During operation, a telemetry variable of the computer system is monitored. Next, a future temperature of the computer system is predicted based on the telemetry variable. A signal is then generated in response to the future temperature. Then, the signal is sent to a cooling device in the computer system to control temperature variations of the computer system.

Ayse K. Coskun

Papers

Reducing the data center electricity costs through participation in smart grid programs

Just Enough is More: Achieving Sustainable Performance in Mobile Devices under Thermal Limitations

Fuzzy control for enforcing energy efficiency in high-performance 3D systems

Optimizing communication and cooling costs in HPC data centers via intelligent job allocation

Method and apparatus for controlling temperature variations in a computer system