Publications

Younge, Andrew J.; Grant, Ryan E.; Laros, James H.; Levenhagen, Michael J.; Olivier, Stephen L.; Pedretti, Kevin P.; Ward, Harry L.

Abstract not provided.

Pedretti, Kevin P.; Grant, Ryan E.; Laros, James H.; Levenhagen, Michael J.; Olivier, Stephen L.; Ward, Harry L.; Younge, Andrew J.

Abstract not provided.

2017 8th International Green and Sustainable Computing Conference, IGSC 2017

Grant, Ryan E.; Laros, James H.; Levenhagen, Michael J.; Olivier, Stephen L.; Pedretti, Kevin P.; Ward, Harry L.; Younge, Andrew J.

Advanced power measurement capabilities are becoming available on large scale High Performance Computing (HPC) deployments. There exist several approaches to providing power measurements today, primarily through in-band (e.g. RAPL) and out-of-band measurements (e.g. power meters). Both types of measurement can be augmented with application-level profiling, however it can be difficult to assess the type and detail of measurement needed to obtain insight from the application power profile. This paper presents a taxonomy for classifying power profiling techniques on modern HPC platforms. Three HPC mini-applications are analyzed across three production HPC systems to examine the level of detail, scope, and complexity of these power profiles. We demonstrate that a combination of out-of-band measurement with in-band application region profiling can provide an accurate, detailed view of power usage without introducing overhead. This work also provides a set of recommendations for how to best profile HPC workloads.

Younge, Andrew J.; Pedretti, Kevin P.; Grant, Ryan E.; Brightwell, Ronald B.

Abstract not provided.

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

Schonbein, William W.; Dosanjh, Matthew D.; Grant, Ryan E.; Bridges, Patrick G.

MPI usage patterns are changing as applications move towards fully-multithreaded runtimes. However, the impact of these patterns on MPI message matching is not well-studied. In particular, MPI’s mechanic for receiver-side data placement, message matching, can be impacted by increased message volume and nondeterminism incurred by multithreading. While there has been significant developer interest and work to provide an efficient MPI interface for multithreaded access, there has not been a study showing how these patterns affect messaging patterns and matching behavior. In this paper, we present a framework for studying the effects of multithreading on MPI message matching. This framework allows us to explore the implications of different common communication patterns and thread-level decompositions. We present a study of these impacts on the architecture of two of the Top 10 supercomputers (NERSC’s Cori and LANL’s Trinity). This data provides a baseline to evaluate reasonable matching engine queue lengths, search depths, and queue drain times under the multithreaded model. Furthermore, the study highlights surprising results on the challenge posed by message matching for multithreaded application performance.

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017

Hoefer, Torsten; Di Girolamo, Salvatore; Taranov, Konstantin; Grant, Ryan E.; Brightwell, Ronald B.

Optimizing communication performance is imperative for large-scale computing because communication overheads limit the strong scalability of parallel applications. Today's network cards contain rather powerful processors optimized for data movement. However, these devices are limited to fixed functions, such as remote direct memory access. We develop sPIN, a portable programming model to offload simple packet processing functions to the network card. To demonstrate the potential of the model, we design a cycle-accurate simulation environment by combining the network simulator Log-GOPSim and the CPU simulator gem5. We implement offloaded message matching, datatype processing, and collective communications and demonstrate transparent full-application speedups. Furthermore, we show how sPIN can be used to accelerate redundant in-memory filesystems and several other use cases. Our work investigates a portable packet-processing network acceleration model similar to compute acceleration with CUDA or OpenCL. We show how such network acceleration enables an eco-system that can significantly speed up applications and system services.

Younge, Andrew J.; Pedretti, Kevin P.; Grant, Ryan E.; Brightwell, Ronald B.

Abstract not provided.

Grant, Ryan E.

Abstract not provided.

Grant, Ryan E.

Abstract not provided.

Ferreira, Kurt B.; Grant, Ryan E.; Levenhagen, Michael J.; Levy, Scott L.; Groves, Taylor G.

Abstract not provided.

Dosanjh, Matthew D.; Grant, Ryan E.; Schonbein, William W.; Bridges, Patrick B.

Abstract not provided.

ACM International Conference Proceeding Series

Ferreira, Kurt B.; Levy, Scott; Pedretti, Kevin P.; Grant, Ryan E.

With the increased scale expected on future leadership-class systems, detailed information about the resource usage and performance of MPI message matching provides important insights into how to maintain application performance on next-generation systems. However, obtaining MPI message matching performance data is often not possible without significant effort. A common approach is to instrument an MPI implementation to collect relevant statistics. While this approach can provide important data, collecting matching data at runtime perturbs the application’s execution, including its matching performance, and is highly dependent on the MPI library’s matchlist implementation. In this paper, we introduce a trace-based simulation approach to obtain detailed MPI message matching performance data for MPI applications without perturbing their execution. Using a number of key parallel workloads, we demonstrate that this simulator approach can rapidly and accurately characterize matching behavior. Specifically, we use our simulator to collect several important statistics about the operation of the MPI posted and unexpected queues. For example, we present data about search lengths and the duration that messages spend in the queues waiting to be matched. Data gathered using this simulation-based approach have significant potential to aid hardware designers in determining resource allocation for MPI matching functions and provide application and middleware developers with insight into the scalability issues associated with MPI message matching.

Proceedings - IEEE International Conference on Cluster Computing, ICCC

Younge, Andrew J.; Pedretti, Kevin P.; Grant, Ryan E.; Gaines, Brian G.; Brightwell, Ronald B.

While large-scale simulations have been the hallmark of the High Performance Computing (HPC) community for decades, Large Scale Data Analytics (LSDA) workloads are gaining attention within the scientific community not only as a processing component to large HPC simulations, but also as standalone scientific tools for knowledge discovery. With the path towards Exascale, new HPC runtime systems are also emerging in a way that differs from classical distributed computing models. However, system software for such capabilities on the latest extreme-scale DOE supercomputing needs to be enhanced to more appropriately support these types of emerging software ecosystems.In this paper, we propose the use of Virtual Clusters on advanced supercomputing resources to enable systems to support not only HPC workloads, but also emerging big data stacks. Specifically, we have deployed the KVM hypervisor within Cray's Compute Node Linux on a XC-series supercomputer testbed. We also use libvirt and QEMU to manage and provision VMs directly on compute nodes, leveraging Ethernet-over-Aries network emulation. To our knowledge, this is the first known use of KVM on a true MPP supercomputer. We investigate the overhead our solution using HPC benchmarks, both evaluating single-node performance as well as weak scaling of a 32-node virtual cluster. Overall, we find single node performance of our solution using KVM on a Cray is very efficient with near-native performance. However overhead increases by up to 20% as virtual cluster size increases, due to limitations of the Ethernet-over-Aries bridged network. Furthermore, we deploy Apache Spark with large data analysis workloads in a Virtual Cluster, effectively demonstrating how diverse software ecosystems can be supported by High Performance Virtual Clusters.

Pedretti, Kevin P.; Laros, James H.; Grant, Ryan E.; Hammond, Simon D.; Hemmert, Karl S.; Martinez, David J.; Noe, John P.; Patterson, Amy M.; Ward, Harry L.

Abstract not provided.

Younge, Andrew J.; Pedretti, Kevin P.; Grant, Ryan E.; Gaines, Brian G.; Brightwell, Ronald B.

Abstract not provided.

Grant, Ryan E.; Dosanjh, Matthew D.; Brightwell, Ronald B.

Abstract not provided.

Grant, Ryan E.

Abstract not provided.

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

This report presents a specification for the Portals 4 networ k programming interface. Portals 4 is intended to allow scalable, high-performance network communication betwee n nodes of a parallel computing system. Portals 4 is well suited to massively parallel processing and embedded syste ms. Portals 4 represents an adaption of the data movement layer developed for massively parallel processing platfor ms, 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 tar geted to the next generation of machines employing advanced network interface architectures that support enh anced offload capabilities.

Laros, James H.; Grant, Ryan E.; Levenhagen, Michael J.; Olivier, Stephen L.; Pedretti, Kevin P.; Ward, Harry L.; Younge, Andrew J.

Measuring and controlling the power and energy consumption of high performance computing systems by various components in the software stack is an active research area. Implementations in lower level software layers are beginning to emerge in some production systems, which is very welcome. To be most effective, a portable interface to measurement and control features would significantly facilitate participation by all levels of the software stack. We present a proposal for a standard power Application Programming Interface (API) that endeavors to cover the entire software space, from generic hardware interfaces to the input from the computer facility manager.

Grant, Ryan E.; Groves, Taylor G.; Pedretti, Kevin P.; Gentile, Ann C.; Arnold, Dorian A.

Abstract not provided.

Proceedings - IEEE International Conference on Cluster Computing, ICCC

Groves, Taylor G.; Grant, Ryan E.; Hemmert, Karl S.; Hammond, Simon D.; Levenhagen, Michael J.; Arnold, Dorian C.

Exascale networks are expected to comprise a significant part of the total monetary cost and 10-20% of the power budget allocated to exascale systems. Yet, our understanding of current and emerging workloads on these networks is limited. Left ignored, this knowledge gap likely will translate into missed opportunities for (1) improved application performance and (2) decreased power and monetary costs in next generation systems. This work targets a detailed understanding and analysis of the performance and utilization of the dragonfly network topology. Using the Structural Simulation Toolkit (SST) and a range of relevant workloads on a dragonfly topology of 110,592 nodes, we examine network design tradeoffs amongst execution time, power, bandwidth, and the number of global links. Our simulations report stalled, active and idle time on a per-port level of the fabric, in order to provide a detailed picture of future networks. The results of this work show potential savings of 3-10% of the exascale power budget and provide valuable insights to researchers looking for new opportunities to improve performance and increase power efficiency of next generation HPC systems.

Farmer, Shane F.; Skjellum, Anthony S.; Bridges, Patrick G.; Dosanjh, Matthew D.; Grant, Ryan E.; Brightwell, Ronald B.

Abstract not provided.

Grant, Ryan E.

Abstract not provided.

Computer

Grant, Ryan E.; Levenhagen, Michael J.; Olivier, Stephen L.; DeBonis, David D.; Pedretti, Kevin P.; Laros, James H.

Power API - the result of collaboration among national laboratories, universities, and major vendors - provides a range of standardized power management functions, from application-level control and measurement to facility-level accounting, including real-time and historical statistics gathering. Support is already available for Intel and AMD CPUs and standalone measurement devices.

Laros, James H.; DeBonis, David D.; Grant, Ryan E.; Kelly, Suzanne M.; Levenhagen, Michael J.; Olivier, Stephen L.; Pedretti, Kevin P.

Measuring and controlling the power and energy consumption of high performance computing systems by various components in the software stack is an active research area [13, 3, 5, 10, 4, 21, 19, 16, 7, 17, 20, 18, 11, 1, 6, 14, 12]. Implementations in lower level software layers are beginning to emerge in some production systems, which is very welcome. To be most effective, a portable interface to measurement and control features would significantly facilitate participation by all levels of the software stack. We present a proposal for a standard power Application Programming Interface (API) that endeavors to cover the entire software space, from generic hardware interfaces to the input from the computer facility manager.