Ozan Tuncer

Leakage-Aware Cooling Management for Improving Server Energy Efficiency

The computational and cooling power demands of enterprise servers are increasing at an unsustainable rate. Understanding the relationship between computational power, temperature, leakage, and cooling power is crucial to enable energy-efficient operation at the server and data center levels. This paper develops empirical models to estimate the contributions of static and dynamic power consumption in enterprise servers for a wide range of workloads, and analyzes the interactions between temperature, leakage, and cooling power for various workload allocation policies. We propose a cooling management policy that minimizes the server energy consumption by setting the optimum fan speed during runtime. Our experimental results on a presently shipping enterprise server demonstrate that including leakage awareness in workload and cooling management provides additional energy savings without any impact on performance.

PaCMap: Topology Mapping of Unstructured Communication Patterns onto Non-contiguous Allocations

In high performance computing (HPC), applications usually have many parallel tasks running on multiple machine nodes. As these tasks intensively communicate with each other, the communication overhead has a significant impact on an applications execution time. This overhead is determined by the applications communication pattern as well as the network distances between communicating tasks. By mapping the tasks to the available machine nodes in a communication-aware manner, the network distances and the execution times can be significantly reduced. Existing techniques first allocate available nodes to an application, and then map the tasks onto the allocated nodes. In this paper, we discuss the potential benefits of simultaneous allocation and mapping for applications with irregular communication patterns. We also propose a novel graph-based allocation and mapping technique to reduce the execution time in HPC machines that use non-contiguous allocation, such as Cray XK series. Simulations calibrated with real-life experiments show that our technique reduces hop-bytes up to 30% compared to the state-of-the-art.

Diagnosing Performance Variations in HPC Applications Using Machine Learning

With the growing complexity and scale of high performance computing (HPC) systems, application performance variation has become a significant challenge in efficient and resilient system management. Application performance variation can be caused by resource contention as well as software- and firmware-related problems, and can lead to premature job termination, reduced performance, and wasted compute platform resources. To effectively alleviate this problem, system administrators must detect and identify the anomalies that are responsible for performance variation and take preventive actions. However, diagnosing anomalies is often a difficult task given the vast amount of noisy and high-dimensional data being collected via a variety of system monitoring infrastructures.

CoolBudget: Data center power budgeting with workload and cooling asymmetry awareness

Power over-subscription challenges and emerging cost management strategies motivate designing efficient data center power capping techniques. During capping, provisioned power must be budgeted among the computational and cooling units. This work presents a data center power budgeting policy that simultaneously improves the quality-of-service (QoS) and power efficiency by considering the workload- and cooling-induced asymmetries among the servers. Proposed policy finds the most efficient data center temperature and the power distribution among servers while guaranteeing reliable temperature levels for the server internal components. Experiments based on real servers demonstrate 21% increase in throughput compared to existing techniques.

Taxonomist: Application Detection Through Rich Monitoring Data

Modern supercomputers are shared among thousands of users running a variety of applications. Knowing which applications are running in the system can bring substantial benefits: knowledge of applications that intensively use shared resources can aid scheduling; unwanted applications such as cryptocurrency mining or password cracking can be blocked; system architects can make design decisions based on system usage. However, identifying applications on supercomputers is challenging because applications are executed using esoteric scripts along with binaries that are compiled and named by users.

Unveiling the Interplay Between Global Link Arrangements and Network Management Algorithms on Dragonfly Networks

Network messaging delay historically constitutes a large portion of the wall-clock time for High Performance Computing (HPC) applications, as these applications run on many nodes and involve intensive communication among their tasks. Dragonfly network topology has emerged as a promising solution for building exascale HPC systems owing to its low network diameter and large bisection bandwidth. Dragonfly includes local links that form groups and global links that connect these groups via high bandwidth optical links. Many aspects of the dragonfly network design are yet to be explored, such as the performance impact of the connectivity of the global links, i.e., global link arrangements, the bandwidth of the local and global links, or the job allocation algorithm. This paper first introduces a packet-level simulation framework to model the performance of HPC applications in detail. The proposed framework is able to simulate known MPI (message passing interface) routines as well as applications with custom-defined communication patterns for a given job placement algorithm and network topology. Using this simulation framework, we investigate the coupling between global link bandwidth and arrangements, communication pattern and intensity, job allocation and task mapping algorithms, and routing mechanisms in dragonfly topologies. We demonstrate that by choosing the right combination of system settings and workload allocation algorithms, communication overhead can be decreased by up to 44%. We also show that circulant arrangement provides up to 15% higher bisection bandwidth compared to the other arrangements, but for realistic workloads, the performance impact of link arrangements is less than 3%.