Publications

Zhang, Yijia Z.; Aksar, Burak A.; Aaziz, Omar R.; Schwaller, Benjamin S.; Brandt, James M.; Leung, Vitus J.; Egele, Manuel E.; Coskun, Ayse K.

Abstract not provided.

Aksar, Burak A.; Zhang, Yijia Z.; Ates, Emre A.; Aaziz, Omar R.; Schwaller, Benjamin S.; Brandt, James M.; Leung, Vitus J.; Egele, Manuel E.; Coskun, Ayse K.

Abstract not provided.

Hart, William E.; Leung, Vitus J.

Abstract not provided.

Aksar, Burak A.; Zhang, Yijia Z.; Ates, Emre A.; Aaziz, Omar R.; Schwaller, Benjamin S.; Brandt, James M.; Leung, Vitus J.; Egele, Manuel E.; Coskun, Ayse K.

Abstract not provided.

2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021

Ates, Emre; Aksar, Burak; Leung, Vitus J.; Coskun, Ayse K.

Multivariate time series are used in many science and engineering domains, including health-care, astronomy, and high-performance computing. A recent trend is to use machine learning (ML) to process this complex data and these ML-based frameworks are starting to play a critical role for a variety of applications. However, barriers such as user distrust or difficulty of debugging need to be overcome to enable widespread adoption of such frameworks in production systems. To address this challenge, we propose a novel explainability technique, CoMTE, that provides counterfactual explanations for supervised machine learning frameworks on multivariate time series data. Using various machine learning frameworks and data sets, we compare CoMTE with several state-of-the-art explainability methods and show that we outperform existing methods in comprehensibility and robustness. We also show how CoMTE can be used to debug machine learning frameworks and gain a better understanding of the underlying multivariate time series data.

Ates, Emre A.; Aksar, Burak A.; Leung, Vitus J.; Coskun, Ayse K.

Abstract not provided.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Aksar, Burak; Zhang, Yijia; Ates, Emre; Schwaller, Benjamin S.; Aaziz, Omar R.; Leung, Vitus J.; Brandt, James M.; Egele, Manuel; Coskun, Ayse K.

Performance variation diagnosis in High-Performance Computing (HPC) systems is a challenging problem due to the size and complexity of the systems. Application performance variation leads to premature termination of jobs, decreased energy efficiency, or wasted computing resources. Manual root-cause analysis of performance variation based on system telemetry has become an increasingly time-intensive process as it relies on human experts and the size of telemetry data has grown. Recent methods use supervised machine learning models to automatically diagnose previously encountered performance anomalies in compute nodes. However, supervised machine learning models require large labeled data sets for training. This labeled data requirement is restrictive for many real-world application domains, including HPC systems, because collecting labeled data is challenging and time-consuming, especially considering anomalies that sparsely occur. This paper proposes a novel semi-supervised framework that diagnoses previously encountered performance anomalies in HPC systems using a limited number of labeled data points, which is more suitable for production system deployment. Our framework first learns performance anomalies’ characteristics by using historical telemetry data in an unsupervised fashion. In the following process, we leverage supervised classifiers to identify anomaly types. While most semi-supervised approaches do not typically use anomalous samples, our framework takes advantage of a few labeled anomalous samples to classify anomaly types. We evaluate our framework on a production HPC system and on a testbed HPC cluster. We show that our proposed framework achieves 60% F1-score on average, outperforming state-of-the-art supervised methods by 11%, and maintains an average 0.06% anomaly miss rate.

2021 IEEE High Performance Extreme Computing Conference, HPEC 2021

Zhang, Yijia; Aksar, Burak; Aaziz, Omar R.; Schwaller, Benjamin S.; Brandt, James M.; Leung, Vitus J.; Egele, Manuel; Coskun, Ayse K.

On high-performance computing (HPC) systems, job allocation strategies control the placement of a job among available nodes. As the placement changes a job's communication performance, allocation can significantly affects execution times of many HPC applications. Existing allocation strategies typically make decisions based on resource limit, network topology, communication patterns, etc. However, system network performance at runtime is seldom consulted in allocation, even though it significantly affects job execution times.In this work, we demonstrate using monitoring data to improve HPC systems' performance by proposing a NetworkData-Driven (NeDD) job allocation framework, which monitors the network performance of an HPC system at runtime and allocates resources based on both network performance and job characteristics. NeDD characterizes system network performance by collecting the network traffic statistics on each router link, and it characterizes a job's sensitivity to network congestion by collecting Message Passing Interface (MPI) statistics. During allocation, NeDD pairs network-sensitive (network-insensitive) jobs with nodes whose parent routers have low (high) network traffic. Through experiments on a large HPC system, we demonstrate that NeDD reduces the execution time of parallel applications by 11% on average and up to 34%.

Ates, Emre A.; Aksar, Burak A.; Leung, Vitus J.; Coskun, Ayse K.

Abstract not provided.

Ates, Emre A.; Aksar, Burak A.; Leung, Vitus J.; Coskun, Ayse K.

Abstract not provided.

Schwaller, Benjamin S.; Aksar, Burak A.; Aaziz, Omar R.; Ates, Emre A.; Brandt, James M.; Coskun, Ayse K.; Egele, Manuel E.; Leung, Vitus J.

Abstract not provided.

Aksar, Burak A.; Schwaller, Benjamin S.; Aaziz, Omar R.; Ates, Emre A.; Brandt, James M.; Coskun, Ayse K.; Egele, Manuel E.; Leung, Vitus J.

Abstract not provided.

Vineyard, Craig M.; Green, Sam G.; Pedretti, Kevin P.; Younge, Andrew J.; Leung, Vitus J.

Abstract not provided.

Zhang, Yijia Z.; Tuncer, Ozan T.; Kaplan, Fulya K.; Olcoz, Katzalin O.; Leung, Vitus J.; Coskun, Ayse K.

Abstract not provided.

IEEE Transactions on Parallel and Distributed Systems

Tuncer, Ozan; Ates, Emre; Zhang, Yijia; Turk, Ata; Brandt, James M.; Leung, Vitus J.; Egele, Manuel; Coskun, Ayse K.

As the size and complexity of high performance computing (HPC) systems grow in line with advancements in hardware and software technology, HPC systems increasingly suffer from performance variations due to shared resource contention as well as software-and hardware-related problems. Such performance variations can lead to failures and inefficiencies, which impact the cost and resilience of HPC systems. To minimize the impact of performance variations, one must quickly and accurately detect and diagnose the anomalies that cause the variations and take mitigating actions. However, it is difficult to identify anomalies based on the voluminous, high-dimensional, and noisy data collected by system monitoring infrastructures. This paper presents a novel machine learning based framework to automatically diagnose performance anomalies at runtime. Our framework leverages historical resource usage data to extract signatures of previously-observed anomalies. We first convert collected time series data into easy-to-compute statistical features. We then identify the features that are required to detect anomalies, and extract the signatures of these anomalies. At runtime, we use these signatures to diagnose anomalies with negligible overhead. We evaluate our framework using experiments on a real-world HPC supercomputer and demonstrate that our approach successfully identifies 98 percent of injected anomalies and consistently outperforms existing anomaly diagnosis techniques.

Communications in Computer and Information Science

Link, Hamilton E.; Richter, Samuel N.; Leung, Vitus J.; Brost, Randolph B.; Phillips, Cynthia A.; Staid, Andrea S.

We use Bayesian data analysis to predict dengue fever outbreaks and quantify the link between outbreaks and meteorological precursors tied to the breeding conditions of vector mosquitos. We use Hamiltonian Monte Carlo sampling to estimate a seasonal Gaussian process modeling infection rate, and aperiodic basis coefficients for the rate of an “outbreak level” of infection beyond seasonal trends across two separate regions. We use this outbreak level to estimate an autoregressive moving average (ARMA) model from which we extrapolate a forecast. We show that the resulting model has useful forecasting power in the 6–8 week range. The forecasts are not significantly more accurate with the inclusion of meteorological covariates than with infection trends alone.

Brost, Randolph B.; Carrier, Erin E.; Carroll, Michelle C.; Groth, Katrina M.; Kegelmeyer, William P.; Leung, Vitus J.; Link, Hamilton E.; Patterson, Andrew J.; Phillips, Cynthia A.; Richter, Samuel N.; Robinson, David G.; Staid, Andrea S.; Woodbridge, Diane M.-K.

This report summarizes the work performed under the Sandia LDRD project "Adverse Event Prediction Using Graph-Augmented Temporal Analysis." The goal of the project was to de- velop a method for analyzing multiple time-series data streams to identify precursors provid- ing advance warning of the potential occurrence of events of interest. The proposed approach combined temporal analysis of each data stream with reasoning about relationships between data streams using a geospatial-temporal semantic graph. This class of problems is relevant to several important topics of national interest. In the course of this work we developed new temporal analysis techniques, including temporal analysis using Markov Chain Monte Carlo techniques, temporal shift algorithms to refine forecasts, and a version of Ripley's K-function extended to support temporal precursor identification. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication sub- missions and reports that were prepared as part of this work. We then describe work in progress that is not yet ready for publication.

Link, Hamilton E.; Richter, Samuel N.; Leung, Vitus J.; Brost, Randolph B.; Phillips, Cynthia A.; Staid, Andrea S.

Abstract not provided.

Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018

Zhang, Yijia; Tuncer, Ozan; Kaplan, Fulya; Olcoz, Katzalin; Leung, Vitus J.; Coskun, Ayse K.

The dragonfly network topology has attracted attention in recent years owing to its high radix and constant diameter. However, the influence of job allocation on communication time in dragonfly networks is not fully understood. Recent studies have shown that random allocation is better at balancing the network traffic, while compact allocation is better at harnessing the locality in dragonfly groups. Based on these observations, this paper introduces a novel allocation policy called Level-Spread for dragonfly networks. This policy spreads jobs within the smallest network level that a given job can fit in at the time of its allocation. In this way, it simultaneously harnesses node adjacency and balances link congestion. To evaluate the performance of Level-Spread, we run packet-level network simulations using a diverse set of application communication patterns, job sizes, and communication intensities. We also explore the impact of network properties such as the number of groups, number of routers per group, machine utilization level, and global link bandwidth. Level-Spread reduces the communication overhead by 16% on average (and up to 71%) compared to the state-of-The-Art allocation policies.

Ates, Emre; Tuncer, Ozan; Turk, Ata T.; Leung, Vitus J.; Brandt, James M.; Egele, Manuel E.; Coskun, Ayse K.

Abstract not provided.

Tuncer, Ozan; Ates, Emre; Zhang, Yijia Z.; Turk, Ata T.; Brandt, James M.; Leung, Vitus J.; Egele, Manuel E.; Coskun, Ayse K.

Abstract not provided.

Brost, Randolph B.; Leung, Vitus J.; Link, Hamilton E.; Phillips, Cynthia A.; Staid, Andrea S.

Abstract not provided.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Ates, Emre; Tuncer, Ozan; Turk, Ata; Leung, Vitus J.; Brandt, James M.; Egele, Manuel; Coskun, Ayse K.

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.

IEEE Transactions on Parallel and Distributed Systems

Unat, Didem; Dubey, Anshu; Hoefler, Torsten; Shalf, John B.; Abraham, Mark; Bianco, Mauro; Chamberlain, Bradford L.; Cledat, Romain; Edwards, Harold C.; Finkel, Hal; Fuerlinger, Karl; Hannig, Frank; Jeannot, Emmanuel; Kamil, Amir; Keasler, Jeff; Kelly, Paul H.J.; Leung, Vitus J.; Ltaief, Hatem; Maruyama, Naoya; Newburn, Chris J.; Pericas, Miquel

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.

Tuncer, Ozan T.; Zhang, Yijia Z.; Leung, Vitus J.; Coskun, Ayse K.

Abstract not provided.

Devine, Karen D.; Brandt, James M.; Deveci, Mehmet D.; Gentile, Ann C.; Leung, Vitus J.; Olivier, Stephen L.; Pedretti, Kevin P.; Rajamanickam, Sivasankaran R.; Taylor, Mark A.

Abstract not provided.

Brost, Randolph B.; Leung, Vitus J.; Link, Hamilton E.; Phillips, Cynthia A.; Staid, Andrea S.

Abstract not provided.

Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017

Kaplan, Fulya; Tuncer, Ozan; Leung, Vitus J.; Hemmert, Karl S.; Coskun, Ayse K.

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%.

Kaplan, Fulya K.; Tuncer, Ozan T.; Leung, Vitus J.; Hemmert, Karl S.; Coskun, Ayse K.

Abstract not provided.

Leung, Vitus J.

Abstract not provided.

Tuncer, Ozan T.; Ates, Emre A.; Zhang, Yijia Z.; Turk, Ata T.; Brandt, James M.; Leung, Vitus J.; Egele, Manuel E.; Coskun, Ayse K.

Abstract not provided.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Tuncer, Ozan; Ates, Emre; Zhang, Yijia; Turk, Ata; Brandt, James M.; Leung, Vitus J.; Egele, Manuel; Coskun, Ayse K.

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.

Ang, James A.; Barrett, Richard F.; Benner, R.E.; Burke, Daniel B.; Chan, Cy P.; Cook, Jeanine C.; Daley, Christopher D.; Donofrio, Dave D.; Hammond, Simon D.; Hemmert, Karl S.; Hoekstra, Robert J.; Ibrahim, Khaled I.; Kelly, Suzanne M.; Le, Hoang L.; Leung, Vitus J.; Michelogiannakis, George M.; Resnick, David R.; Rodrigues, Arun; Shalf, John S.; Stark, Dylan S.; Unat, D.U.; Wright, Nick W.; Voskuilen, Gwendolyn R.

Machine Models and Proxy Architectures for Exascale Computing Version 2.0 Prepared by Sandia National Laboratories Albuquerque, New Mexico 87185 and Livermore, California 94550 Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. Approved for public release; further dissemination unlimited. Issued by Sandia National Laboratories, operated for the United States Department of Energy by Sandia Corporation. NOTICE: This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government, nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors, or their employees, make any warranty, express or implied, or assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or rep- resent that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government, any agency thereof, or any of their contractors or subcontractors. The views and opinions expressed herein do not necessarily state or reflect those of the United States Government, any agency thereof, or any of their contractors. Printed in the United States of America. This report has been reproduced directly from the best available copy. Available to DOE and DOE contractors from U.S. Department of Energy Office of Scientific and Technical Information P.O. Box 62 Oak Ridge, TN 37831 Telephone: (865) 576-8401 Facsimile: (865) 576-5728 E-Mail: reports@adonis.osti.gov Online ordering: http://www.osti.gov/bridge Available to the public from U.S. Department of Commerce National Technical Information Service 5285 Port Royal Rd Springfield, VA 22161 Telephone: (800) 553-6847 Facsimile: (703) 605-6900 E-Mail: orders@ntis.fedworld.gov Online ordering: http://www.ntis.gov/help/ordermethods.asp?loc=7-4-0#online D E P A R T M E N T O F E N E R G Y * * U N I T E D S T A T E S O F A M E R I C A SAND2016-6049 Unlimited Release Printed Abstract Machine Models and Proxy Architectures for Exascale Computing Version 2.0 J.A. Ang 1 , R.F. Barrett 1 , R.E. Benner 1 , D. Burke 2 , C. Chan 2 , J. Cook 1 , C.S. Daley 2 , D. Donofrio 2 , S.D. Hammond 1 , K.S. Hemmert 1 , R.J. Hoekstra 1 , K. Ibrahim 2 , S.M. Kelly 1 , H. Le, V.J. Leung 1 , G. Michelogiannakis 2 , D.R. Resnick 1 , A.F. Rodrigues 1 , J. Shalf 2 , D. Stark, D. Unat, N.J. Wright 2 , G.R. Voskuilen 1 1 1 Sandia National Laboratories, P.O. Box 5800, Albuquerque, New Mexico 87185-MS 1319 2 Lawrence Berkeley National Laboratory, Berkeley, California Abstract To achieve exascale computing, fundamental hardware architectures must change. The most sig- nificant consequence of this assertion is the impact on the scientific and engineering applications that run on current high performance computing (HPC) systems, many of which codify years of scientific domain knowledge and refinements for contemporary computer systems. In order to adapt to exascale architectures, developers must be able to reason about new hardware and deter- mine what programming models and algorithms will provide the best blend of performance and energy efficiency into the future. While many details of the exascale architectures are undefined, an abstract machine model is designed to allow application developers to focus on the aspects of the machine that are important or relevant to performance and code structure. These models are intended as communication aids between application developers and hardware architects during the co-design process. We use the term proxy architecture to describe a parameterized version of an abstract machine model, with the parameters added to elucidate potential speeds and capacities of key hardware components. These more detailed architectural models are formulated to enable discussion between the developers of analytic models and simulators and computer hardware archi- tects. They allow for application performance analysis and hardware optimization opportunities. In this report our goal is to provide the application development community with a set of mod- els that can help software developers prepare for exascale. In addition, through the use of proxy architectures, we can enable a more concrete exploration of how well new and evolving applica- tion codes map onto future architectures. This second version of the document addresses system scale considerations and provides a system-level abstract machine model with proxy architecture information.

Deveci, Mehmet D.; Devine, Karen D.; Boman, Erik G.; Leung, Vitus J.; Rajamanickam, Sivasankaran R.; Taylor, Mark A.

Abstract not provided.

Hastings, Emily H.; Rincon-Cruz, David R.; Spehlmann, Marc S.; Meyers, Sofia M.; Xu, Anda X.; Bunde, David P.; Leung, Vitus J.

Abstract not provided.

Hastings, Emily H.; Rincon-Cruz, David R.; Spehlmann, Marc S.; Meyers, Sofia M.; Xu, Anda X.; Bunde, David P.; Leung, Vitus J.

Abstract not provided.

Hastings, Emily H.; Rincon-Cruz, David R.; Spehlmann, Marc S.; Meyers, Sofia M.; Xu, Anda X.; Bunde, David P.; Leung, Vitus J.

Abstract not provided.

Parallel Computing

Balzuweit, Evan; Bunde, David P.; Leung, Vitus J.; Finley, Austin; Lee, Alan C.S.

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.

Proceedings - IEEE International Conference on Cluster Computing, ICCC

Hastings, Emily; Rincon-Cruz, David; Spehlmann, Marc; Meyers, Sofia; Xu, Anda; Bunde, David P.; Leung, Vitus J.

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.

Hastings, Emily H.; Rincon-Cruz, David R.; Spehlmann, Marc S.; Meyers, Sofia M.; Xu, Anda X.; Bunde, David P.; Leung, Vitus J.

Abstract not provided.

Deveci, Mehmet D.; Devine, Karen D.; Leung, Vitus J.; Prokopenko, Andrey V.; Rajamanickam, Sivasankaran R.

Abstract not provided.

Leung, Vitus J.; Bunde, David P.; Hastings, Emily H.; Meyers, Sofia M.; Rincon-Cruz, David R.; Spehlmann, Marc S.; Xu, Anda X.

Abstract not provided.

Hastings, Emily H.; Rincon-Cruz, David R.; Spehlmann, Marc S.; Meyers, Sofia M.; Xu, Anda X.; Bunde, David P.; Leung, Vitus J.

Abstract not provided.

Proceedings of the International Conference on Supercomputing

Tuncer, Ozan; Leung, Vitus J.; Coskun, Ayse K.

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.

Sustainable Computing: Informatics and Systems

Meng, Jie; McCauley, Samuel; Kaplan, Fulya; Leung, Vitus J.; Coskun, Ayse K.

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.

Tuncer, Ozan T.; Leung, Vitus J.; Coskun, Ayse K.

Abstract not provided.

Balzuweit, Evan B.; Bunde, David P.; Leung, Vitus J.; Finley, Austin F.; Lee, Alan W.

Abstract not provided.

Devine, Karen D.; Boman, Erik G.; Rajamanickam, Sivasankaran R.; Leung, Vitus J.; Riesen, Lee A.; Deveci, Mehmet D.; Catalyurek, Umit V.

Abstract not provided.

Tate, Adrian T.; Kamil, Amir K.; Dubey, Anshu D.; Groblinger, Armin G.; Chamberlain, Brad C.; Goglin, Brice G.; Edwards, Harold C.; Newburn, Chris J.; Padua, David A.; Unat, Didem U.; Jeannot, Emmanuel J.; Hannig, Frank H.; Tobias, Gysi T.; Ltaief, Hatem L.; Sexton, James S.; Labarta, Jesus L.; Shalf, John S.; Fuerlinger, Karl F.; O'Brien, Kathryn O.; Linardakis, Leonidas L.; Besta, Maciej B.; Sawley, Marie-Christine S.; Abraham, Mark A.; Bianco, Mauro B.; Pericas, Miquel P.; Maruyama, Naoya M.; Kelly, Paul H.; Messmer, Peter M.; Ross, Robert B.; Ciedat, Romain C.; Matsuoka, Satoshi M.; Schulthess, Thomas S.; Hoefler, Torsten H.; Leung, Vitus J.

The goal of the workshop and this report is to identify common themes and standardize concepts for locality-preserving abstractions for exascale programming models.

Balzuweit, Evan B.; Bunde, David P.; Leung, Vitus J.; Finley, Austin F.; Lee, Alan W.

Abstract not provided.