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Geometric mapping of tasks to processors on parallel computers with mesh or torus networks

IEEE Transactions on Parallel and Distributed Systems

Deveci, Mehmet; Devine, Karen D.; Pedretti, Kevin P.; Taylor, Mark A.; Rajamanickam, Sivasankaran R.; Çatalyurek, Umit V.

We present a new method for reducing parallel applications’ communication time by mapping their MPI tasks to processors in a way that lowers the distance messages travel and the amount of congestion in the network. Assuming geometric proximity among the tasks is a good approximation of their communication interdependence, we use a geometric partitioning algorithm to order both the tasks and the processors, assigning task parts to the corresponding processor parts. In this way, interdependent tasks are assigned to “nearby” cores in the network. We also present a number of algorithmic optimizations that exploit specific features of the network or application to further improve the quality of the mapping. We specifically address the case of sparse node allocation, where the nodes assigned to a job are not necessarily located in a contiguous block nor within close proximity to each other in the network. However, our methods generalize to contiguous allocations as well, and results are shown for both contiguous and non-contiguous allocations. We show that, for the structured finite difference mini-application MiniGhost, our mapping methods reduced communication time up to 75 percent relative to MiniGhost’s default mapping on 128K cores of a Cray XK7 with sparse allocation. For the atmospheric modeling code E3SM/HOMME, our methods reduced communication time up to 31% on 16K cores of an IBM BlueGene/Q with contiguous allocation.

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TYPE Journal Article YEAR 2019
ScopusOSTIDOI

Evaluating the Marvell ThunderX2 Server Processor for HPC Workloads

2019 International Conference on High Performance Computing and Simulation, HPCS 2019

Hammond, Simon D.; Hughes, Clayton H.; Levenhagen, Michael J.; Vaughan, Courtenay T.; Younge, Andrew J.; Schwaller, Benjamin S.; Aguilar, Michael J.; Pedretti, Kevin P.; Laros, James H.

The high performance computing industry is undergoing a period of substantial change. Not least because of fabrication and lithographic challenges in the manufacturing of next-generation processors. As such challenges mount, the industry is looking to generate higher performance from additional functionality in the micro-architecture space as well as a greater emphasis on efficiency in the design of networkon-chip resources and memory subsystems. Such variation in design opens opportunities for new entrants in the data center and server markets where varying compute-to-memory ratios can present end users with more efficient node designs for particular workloads. In this paper we compare the recently released Marvell ThunderX2 Arm processor - arguably the first high-performance computing capable Arm design available in the marketplace. We perform a set of micro-benchmarking and mini-application evaluation on the ThunderX2 comparing it with Intel's Haswell and Skylake Xeon server parts commonly used in contemporary HPC designs. Our findings show that no one processor performs the best across all benchmarks, but that the ThunderX2 excels in areas demanding high memory bandwidth due to the provisioning of more memory channels in its design. We conclude that the ThunderX2 is a serious contender in the HPC server segment and has the potential to offer supercomputing sites with a viable high-performance alternative to existing designs from established industry players.

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TYPE Conference Poster YEAR 2019
ScopusOSTI

The Portals 4.2 Network Programming Interface

Barrett, Brian W.; Brightwell, Ronald B.; Grant, Ryan E.; Hemmert, Karl S.; Pedretti, Kevin P.; Wheeler, Kyle W.; Riesen, Rolf R.; Hoefler, Torsten H.; Maccabe, Arthur B.; Hudson, Trammell H.

This report presents a specification for the Portals 4 network programming interface. Portals 4 is intended to allow scalable, high-performance network communication between nodes of a parallel computing system. Portals 4 is well suited to massively parallel processing and embedded systems. Portals 4 represents an adaption of the data movement layer developed for massively parallel processing platforms, such as the 4500-node Intel TeraFLOPS machine. Sandia's Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4 is targeted to the next generation of machines employing advanced network interface architectures that support enhanced offload capabilities.

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TYPE SAND Report YEAR 2018
OSTIDOI

Large-Scale System Monitoring Experiences and Recommendations

Proceedings - IEEE International Conference on Cluster Computing, ICCC

Ahlgren, Ville; Andersson, Stefan; Brandt, James M.; Cardo, Nicholas; Chunduri, Sudheer; Enos, Jeremy; Fields, Parks; Gentile, Ann C.; Gerber, Richard; Gienger, Michael; Greenseid, Joe; Greiner, Annette; Hadri, Bilel; He, Yun; Hoppe, Dennis; Kaila, Urpo; Kelly, Kaki; Klein, Mark; Kristiansen, Alex; Leak, Steve; Mason, Mike; Pedretti, Kevin P.; Piccinali, Jean G.; Repik, Jason; Rogers, Jim; Salminen, Susanna; Showerman, Mike; Whitney, Cary; Williams, Jim

Monitoring of High Performance Computing (HPC) platforms is critical to successful operations, can provide insights into performance-impacting conditions, and can inform methodologies for improving science throughput. However, monitoring systems are not generally considered core capabilities in system requirements specifications nor in vendor development strategies. In this paper we present work performed at a number of large-scale HPC sites towards developing monitoring capabilities that fill current gaps in ease of problem identification and root cause discovery. We also present our collective views, based on the experiences presented, on needs and requirements for enabling development by vendors or users of effective sharable end-to-end monitoring capabilities.

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TYPE Conference Poster YEAR 2018
ScopusOSTIDOI
Results 26–50 of 218
Results 26–50 of 218