Publications

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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. This paper introduces a novel technique to identify applications running on supercomputers. Our technique, Taxonomist, is based on the empirical evidence that applications have different and characteristic resource utilization patterns. Taxonomist uses machine learning to classify known applications and also detect unknown applications. We test our technique with a variety of benchmarks and cryptocurrency miners, and also with applications that users of a production supercomputer ran during a 6 month period. We show that our technique achieves nearly perfect classification for this challenging data set.

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The cost of data movement has always been an important concern in high performance computing (HPC) systems. It has now become the dominant factor in terms of both energy consumption and performance. Support for expression of data locality has been explored in the past, but those efforts have had only modest success in being adopted in HPC applications for various reasons. them However, with the increasing complexity of the memory hierarchy and higher parallelism in emerging HPC systems, locality management has acquired a new urgency. Developers can no longer limit themselves to low-level solutions and ignore the potential for productivity and performance portability obtained by using locality abstractions. Fortunately, the trend emerging in recent literature on the topic alleviates many of the concerns that got in the way of their adoption by application developers. Data locality abstractions are available in the forms of libraries, data structures, languages and runtime systems; a common theme is increasing productivity without sacrificing performance. This paper examines these trends and identifies commonalities that can combine various locality concepts to develop a comprehensive approach to expressing and managing data locality on future large-scale high-performance computing systems.

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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%.

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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. In this paper, we present a novel framework that uses machine learning to automatically diagnose previously encountered performance anomalies in HPC systems. Our framework leverages resource usage and performance counter data collected during application runs. We first convert the collected time series data into statistical features that retain application characteristics to significantly reduce the computational overhead of our technique. We then use machine learning algorithms to learn anomaly characteristics from this historical data and to identify the types of anomalies observed while running applications. We evaluate our framework both on an HPC cluster and on a public cloud, and demonstrate that our approach outperforms current state-of-the-art techniques in detecting anomalies, reaching an F-score over 0.97.

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We present a local search strategy to improve the coordinate-based mapping of a parallel job's tasks to the MPI ranks of its parallel allocation in order to reduce network congestion and the job's communication time. The goal is to reduce the number of network hops between communicating pairs of ranks. Our target is applications with a nearest-neighbor stencil communication pattern running on mesh systems with non-contiguous processor allocation, such as Cray XE and XK Systems. Using the miniGhost mini-app, which models the shock physics application CTH, we demonstrate that our strategy reduces application running time while also reducing the runtime variability. We further show that mapping quality can vary based on the selected allocation algorithm, even between allocation algorithms of similar apparent quality.

High-performance computing systems are shifting away from traditional interconnect topologies to exploit new technologies and to reduce interconnect power consumption. The Dragonfly topology is one promising candidate for new systems, with several variations already in production. It is hierarchical, with local links forming groups and global links joining the groups. At each level, the interconnect is a clique, with a link between each pair of switches in a group and a link between each pair of groups. This paper shows that the intergroup links can be made in meaningfully different ways. We evaluate three previously-proposed approaches for link organization (called global link arrangements) in two ways. First, we use bisection bandwidth, an important and commonly-used measure of the potential for communication bottlenecks. We show that the global link arrangements often give bisection bandwidths differing by 10s of percent, with the specific separation varying based on the relative bandwidths of local and global links. For the link bandwidths used in a current Dragonfly implementation, it is 33%. Second, we show that the choice of global link arrangement can greatly impact the regularity of task mappings for nearest neighbor stencil communication patterns, an important pattern in scientific applications.

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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 application's execution time. This overhead is determined by the application's 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 graphbased 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.

Performance and energy are critical aspects in high performance computing (HPC) data centers. Highly parallel HPC applications that require multiple nodes usually run for long durations in the range of minutes, hours or days. As the threads of parallel applications communicate with each other intensively, the communication cost of these applications has a significant impact on data center performance. Energy consumption has also become a first-order constraint of HPC data centers. Nearly half of the energy in the computing clusters today is consumed by the cooling infrastructure. Existing job allocation policies either target improving the system performance or reducing the cooling energy cost of the server nodes. How to optimize the system performance while minimizing the cooling energy consumption is still an open question. This paper proposes a job allocation methodology aimed at jointly reducing the communication cost and the cooling energy of HPC data centers. In order to evaluate and validate our optimization algorithm, we implement our joint job allocation methodology in the structural simulation toolkit (SST) - a simulation framework for large-scale data centers. We evaluate our joint optimization algorithm using traces extracted from real-world workloads. Experimental results show that, in comparison to performance-aware job allocation algorithms, our algorithm achieves comparable running times and reduces the cooling power by up to 42.21% across all the jobs.

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