Publications

Marts, William P.; Dosanjh, Matthew D.; Grant, Ryan E.; Bridges, Patrick B.

Abstract not provided.

Parallel Computing

Ghazimirsaeed, S.M.; Grant, Ryan E.; Afsahi, Ahmad

The Message Passing Interface (MPI) libraries use message queues to guarantee correct message ordering between communicating processes. Message queues are in the critical path of MPI communications and thus, the performance of message queue operations can have significant impact on the performance of applications. Collective communications are widely used in MPI applications and they can have considerable impact on generating long message queues. In this paper, we propose a unified message matching mechanism that improves the message queue search time by distinguishing messages coming from point-to-point and collective communications and using a distinct message queue data structure for them. For collective operations, it dynamically profiles the impact of each collective call on message queues during the application runtime and uses this information to adapt the message queue data structure for each collective dynamically. Moreover, we use a partner/non-partner message queue data structure for the messages coming from point-to-point communications. The proposed approach can successfully reduce the queue search time while maintaining scalable memory consumption. The evaluation results show that we can obtain up to 5.5x runtime speedup for applications with long list traversals. Moreover, we can gain up to 15% and 94% queue search time improvement for all elements in applications with short and medium list traversals, respectively.

Schonbein, William W.; Grant, Ryan E.; Dosanjh, Matthew D.; Arnold, Dorian A.

Abstract not provided.

Beltre, Angel B.; Saha, Pankaj S.; Govindaraju, Madhusudhan G.; Grant, Ryan E.; Younge, Andrew J.

Abstract not provided.

ACM International Conference Proceeding Series

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

As we approach Exascale, message matching has increasingly become a significant factor in HPC application performance. To address this, network vendors have placed higher precedence on improving MPI message matching performance. ConnectX-5, Mellanox's new network interface card, has both hardware and software matching layers. The performance characteristics of these layers have yet to be studied under real world circumstances. In this work we offer an initial evaluation of ConnectX-5 message matching performance. To analyze this new hardware we executed a series of micro-benchmarks and applications on Astra, an ARM-based ConnectX-5 HPC system, while varying hardware and software matching parameters. The benchmark results show the ConnectX-5 is sensitive to queue depths, and that hardware message matching increases performance for applications that send messages between 1KiB and 16KiB. Furthermore, the hardware matching system was capable of matching wildcard receives without negatively impacting performance. Finally, for some applications, a significant improvement can be observed when leveraging the ConnectX-5's hardware matching.

Dosanjh, Matthew D.; Grant, Ryan E.; Hjelmn, Nathan H.; Levy, Scott L.; Schonbein, William W.

Abstract not provided.

Grant, Ryan E.

Abstract not provided.

Proceedings - 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019

Dosanjh, Matthew D.; Schonbein, William W.; Grant, Ryan E.; Bridges, Patrick G.; Gazimirsaeed, S.M.; Afsahi, Ahmad

Contemporary parallel scientific codes often rely on message passing for inter-process communication. However, inefficient coding practices or multithreading (e.g., via MPI-THREAD-MULTIPLE) can severely stress the underlying message processing infrastructure, resulting in potentially un-acceptable impacts on application performance. In this article, we propose and evaluate a novel method for addressing this issue: 'Fuzzy Matching'. This approach has two components. First, it exploits the fact most server-class CPUs include vector operations to parallelize message matching. Second, based on a survey of point-to-point communication patterns in representative scientific applications, the method further increases parallelization by allowing matches based on 'partial truth', i.e., by identifying probable rather than exact matches. We evaluate the impact of this approach on memory usage and performance on Knight's Landing and Skylake processors. At scale (262,144 Intel Xeon Phi cores), the method shows up to 1.13 GiB of memory savings per node in the MPI library, and improvement in matching time of 95.9%; smaller-scale runs show run-time improvements of up to 31.0% for full applications, and up to 6.1% for optimized proxy applications.

Parallel Computing

Levy, Scott; Ferreira, Kurt B.; Schonbein, Whit; Grant, Ryan E.; Dosanjh, Matthew D.

Attaining high performance with MPI applications requires efficient message matching to minimize message processing overheads and the latency these overheads introduce into application communication. In this paper, we use a validated simulation-based approach to examine the relationship between MPI message matching performance and application time-to-solution. Specifically, we examine how the performance of several important HPC workloads is affected by the time required for matching. Our analysis yields several important contributions: (i) the performance of current workloads is unlikely to be significantly affected by MPI matching unless match queue operations get much slower or match queues get much longer; (ii) match queue designs that provide sublinear performance as a function of queue length are unlikely to yield much benefit unless match queue lengths increase dramatically; and (iii) we provide guidance on how long the mean time per match attempt may be without significantly affecting application performance. The results and analysis in this paper provide valuable guidance on the design and development of MPI message match queues.

Grant, Ryan E.

Abstract not provided.

Grant, Ryan E.

Abstract not provided.

Dosanjh, Matthew D.; Schonbein, William W.; Grant, Ryan E.; Bridges, Patrick B.; Gazimirsaeed, Mahdieh G.; Asafi, Ahmad A.

Abstract not provided.

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

Grant, Ryan E.; Dosanjh, Matthew D.; Levenhagen, Michael J.; Brightwell, Ronald B.; Skjellum, Anthony

The MPI multithreading model has been historically difficult to optimize; the interface that it provides for threads was designed as a process-level interface. This model has led to implementations that treat function calls as critical regions and protect them with locks to avoid race conditions. We hypothesize that an interface designed specifically for threads can provide superior performance than current approaches and even outperform single-threaded MPI. In this paper, we describe a design for partitioned communication in MPI that we call finepoints. First, we assess the existing communication models for MPI two-sided communication and then introduce finepoints as a hybrid of MPI models that has the best features of each existing MPI communication model. In addition, “partitioned communication” created with finepoints leverages new network hardware features that cannot be exploited with current MPI point-to-point semantics, making this new approach both innovative and useful both now and in the future. To demonstrate the validity of our hypothesis, we implement a finepoints library and show improvements against a state-of-the-art multithreaded optimized Open MPI implementation on a Cray XC40 with an Aries network. Our experiments demonstrate up to a 12 × reduction in wait time for completion of send operations. This new model is shown working on a nuclear reactor physics neutron-transport proxy-application, providing up to 26.1% improvement in communication time and up to 4.8% improvement in runtime over the best performing MPI communication mode, single-threaded MPI.

Olivier, Stephen L.; Brightwell, Ronald B.; Pedretti, Kevin P.; Younge, Andrew J.; Evans, Noah; Levy, Scott L.; Ferreira, Kurt B.; Grant, Ryan E.

Abstract not provided.

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.

Laros, James H.; Pedretti, Kevin P.; Hammond, Simon D.; Aguilar, Michael J.; Curry, Matthew L.; Grant, Ryan E.; Hoekstra, Robert J.; Klundt, Ruth A.; Monk, Stephen T.; Ogden, Jeffry B.; Olivier, Stephen L.; Scott, Randall D.; Ward, Harry L.; Younge, Andrew J.

The Vanguard program informally began in January 2017 with the submission of a white pa- per entitled "Sandia's Vision for a 2019 Arm Testbed" to NNSA headquarters. The program proceeded in earnest in May 2017 with an announcement by Doug Wade (Director, Office of Advanced Simulation and Computing and Institutional R&D at NNSA) that Sandia Na- tional Laboratories (Sandia) would host the first Advanced Architecture Prototype platform based on the Arm architecture. In August 2017, Sandia formed a Tri-lab team chartered to develop a robust HPC software stack for Astra to support the Vanguard program goal of demonstrating the viability of Arm in supporting ASC production computing workloads. This document describes the high-level Vanguard program goals, the Vanguard-Astra project acquisition plan and procurement up to contract placement, the initial software stack environment planned for the Vanguard-Astra platform (Astra), a description of how the communities of users will utilize the platform during the transition from the open network to the classified network, and initial performance results.

Laros, James H.; Pedretti, Kevin P.; Hammond, Simon D.; Aguilar, Michael J.; Curry, Matthew L.; Grant, Ryan E.; Hoekstra, Robert J.; Klundt, Ruth A.; Monk, Stephen T.; Ogden, Jeffry B.; Olivier, Stephen L.; Scott, Randall D.; Ward, Harry L.; Younge, Andrew J.

The Vanguard program informally began in January 2017 with the submission of a white pa- per entitled "Sandia's Vision for a 2019 Arm Testbed" to NNSA headquarters. The program proceeded in earnest in May 2017 with an announcement by Doug Wade (Director, Office of Advanced Simulation and Computing and Institutional R&D at NNSA) that Sandia Na- tional Laboratories (Sandia) would host the first Advanced Architecture Prototype platform based on the Arm architecture. In August 2017, Sandia formed a Tri-lab team chartered to develop a robust HPC software stack for Astra to support the Vanguard program goal of demonstrating the viability of Arm in supporting ASC production computing workloads. This document describes the high-level Vanguard program goals, the Vanguard-Astra project acquisition plan and procurement up to contract placement, the initial software stack environment planned for the Vanguard-Astra platform (Astra), a description of how the communities of users will utilize the platform during the transition from the open network to the classified network, and initial performance results.

ACM International Conference Proceeding Series

Ghazimirsaeed, S.M.; Grant, Ryan E.; Afsahi, Ahmad

The Message Passing Interface (MPI) libraries use message queues to guarantee correct message ordering between communicating processes. Message queues are in the critical path of MPI communications and thus, the performance of message queue operations can have significant impact on the performance of applications. Collective communications are widely used in MPI applications and they can have considerable impact on generating long message queues. In this paper, we propose a message matching mechanism that improves the message queue search time by distinguishing messages coming from point-to-point and collective communications and allocating separate queues for them. Moreover, it dynamically profiles the impact of each collective call on message queues during the application runtime and uses this information to adapt the message queue data structure for each collective operation dynamically. The proposed approach can successfully reduce the queue search time while maintaining scalable memory consumption. The evaluation results show that we can obtain up to 5.5x runtime speedup for applications with long list traversals. Moreover, we can gain up to 15% and 45% queue search time improvement for applications with short and medium list traversals, respectively.

ACM International Conference Proceeding Series

Dosanjh, Matthew D.; Ghazimirsaeed, S.M.; Grant, Ryan E.; Schonbein, William W.; Levenhagen, Michael J.; Bridges, Patrick G.; Afsahi, Ahmad

The performance critical path for MPI implementations relies on fast receive side operation, which in turn requires fast list traversal. The performance of list traversal is dependent on data-locality; whether the data is currently contained in a close-to-core cache due to its temporal locality or if its spacial locality allows for predictable pre-fetching. In this paper, we explore the effects of data locality on the MPI matching problem by examining both forms of locality. First, we explore spacial locality, by combining multiple entries into a single linked list element, we can control and modify this form of locality. Secondly, we explore temporal locality by utilizing a new technique called “hot caching”, a process that creates a thread to periodically access certain data, increasing its temporal locality. In this paper, we show that by increasing data locality, we can improve MPI performance on a variety of architectures up to 4x for micro-benchmarks and up to 2x for an application.

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

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

Large-scale HPC systems increasingly incorporate sophisticated power management control mechanisms. While these mechanisms are potentially useful for performing energy and/or power-aware job scheduling and resource management (EPA JSRM), greater understanding of their operation and performance impact on real-world applications is required before they can be applied effectively in practice. In this paper, we compare static p-state control to static node-level power cap control on a Cray XC system. Empirical experiments are performed to evaluate node-to-node performance and power usage variability for the two mechanisms. We find that static p-state control produces more predictable and higher performance characteristics than static node-level power cap control at a given power level. However, this performance benefit is at the cost of less predictable power usage. Static node-level power cap control produces predictable power usage but with more variable performance characteristics. Our results are not intended to show that one mechanism is better than the other. Rather, our results demonstrate that the mechanisms are complementary to one another and highlight their potential for combined use in achieving effective EPA JSRM solutions.

Dosanjh, Matthew D.; Ghazimirsaeed, S.M.; Grant, Ryan E.; Schonbein, William W.; Levenhagen, Michael J.; Bridges, Patrick B.; Afsahi, Ahmad A.

Abstract not provided.

IEEE Transactions on Parallel and Distributed Systems

Groves, Taylor G.; Grant, Ryan E.; Gonzales, Aaron; Arnold, Dorian

Remote Direct Memory Access (RDMA) is expected to be an integral communication mechanism for future exascale systems - enabling asynchronous data transfers, so that applications may fully utilize CPU resources while simultaneously sharing data amongst remote nodes. In this work we examine Network-induced Memory Contention (NiMC) on Infiniband networks. We expose the interactions between RDMA, main-memory and cache, when applications and out-of-band services compete for memory resources. We then explore NiMC's resulting impact on application-level performance. For a range of hardware technologies and HPC workloads, we quantify NiMC and show that NiMC's impact grows with scale resulting in up to 3X performance degradation at scales as small as 8K processes even in applications that previously have been shown to be performance resilient in the presence of noise. Additionally, this work examines the problem of predicting NiMC's impact on applications by leveraging machine learning and easily accessible performance counters. This approach provides additional insights about the root cause of NiMC and facilitates dynamic selection of potential solutions. Lastly, we evaluated three potential techniques to reduce NiMC's impact, namely hardware offloading, core reservation and software-based network throttling.

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

Abstract not provided.

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

Abstract not provided.

Hjelm, Nathan T.; Dosanjh, Matthew D.; Groves, Taylor G.; Grant, Ryan E.; Brightwell, Ronald B.; Bridges, Patrick B.; Arnold, Dorian A.

Abstract not provided.