Publications

Barrett, Richard F.; Aaziz, Omar R.; Cook, Jeanine C.; Lehoucq, Richard B.; Olivier, Stephen L.; Vaughan, Courtenay T.

Abstract not provided.

2020 IEEE High Performance Extreme Computing Conference, HPEC 2020

Teranishi, Keita T.; Dunlavy, Daniel D.; Myers, Jeremy M.; Barrett, Richard F.

Canonical Polyadic tensor decomposition using alternate Poisson regression (CP-APR) is an effective analysis tool for large sparse count datasets. One of the variants using projected damped Newton optimization for row subproblems (PDNR) offers quadratic convergence and is amenable to parallelization. Despite its potential effectiveness, PDNR performance on modern high performance computing (HPC) systems is not well understood. To remedy this, we have developed a parallel implementation of PDNR using Kokkos, a performance portable parallel programming framework supporting efficient runtime of a single code base on multiple HPC systems. We demonstrate that the performance of parallel PDNR can be poor if load imbalance associated with the irregular distribution of nonzero entries in the tensor data is not addressed. Preliminary results using tensors from the FROSTT data set indicate that using multiple kernels to address this imbalance when solving the PDNR row subproblems in parallel can improve performance, with up to 80% speedup on CPUs and 10-fold speedup on NVIDIA GPUs.

Teranishi, Keita T.; Dunlavy, Daniel D.; Myers, Jeremy M.; Barrett, Richard F.

Abstract not provided.

Barrett, Richard F.; Cook, Jeanine C.; Olivier, Stephen L.; Aaziz, Omar R.; Jenkins, Christipher D.; Vaughan, Courtenay T.

Abstract not provided.

Teranishi, Keita T.; Hollman, David S.; Barrett, Richard F.; Myers, Jeremy M.; Dunlavy, Daniel D.

Abstract not provided.

Teranishi, Keita T.; Hollman, David S.; Dunlavy, Daniel D.; Barrett, Richard F.

Abstract not provided.

Teranishi, Keita T.; Dunlavy, Daniel D.; Barrett, Richard F.; Kolda, Tamara G.; Forster, Christopher F.

Abstract not provided.

Teranishi, Keita T.; Barrett, Richard F.; Forster, Christopher F.; Dunlavy, Daniel D.; Kolda, Tamara G.

Abstract not provided.

Proceedings of the 7th International Workshop on Runtime and Operating Systems for Supercomputers, ROSS 2017 - In conjunction with HPDC

Evans, Noah; Olivier, Stephen L.; Barrett, Richard F.; Stelle, George

This paper describes improvements in task scheduling for the Chapel parallel programming language provided in its default on-node tasking runtime, the Qthreads library. We describe a new scheduler distrib which builds on the approaches of two previous Qthreads schedulers, Sherwood and Nemesis, and combines the best aspects of both-work stealing and load balancing from Sherwood and a lock free queue access from Nemesis- to make task queuing better suited for the use of Chapel in the manycore era. We demonstrate the efficacy of this new scheduler by showing improvements in various individual benchmarks of the Chapel test suite on the Intel Knights Landing architecture.

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.

Proceedings - IEEE International Conference on Cluster Computing, ICCC

Vaughan, Courtenay T.; Barrett, Richard F.

A broad range of physical phenomena in science and engineering can be explored using finite difference and volume based application codes. Incorporating Adaptive Mesh Refinement (AMR) into these codes focuses attention on the most critical parts of a simulation, enabling increased numerical accuracy of the solution while limiting memory consumption. However, adaptivity comes at the cost of increased runtime complexity, which is particularly challenging on emerging and expected future architectures. In order to explore the design space offered by new computing environments, we have developed a proxy application called miniAMR. MiniAMR exposes a range of the important issues that will significantly impact the performance potential of full application codes. In this paper, we describe miniAMR, demonstrate what is designed to represent in a full application code, and illustrate how it can be used to exploit future high performance computing architectures. To ensure an accurate understanding of what miniAMR is intended to represent, we compare it with CTH, a shock hydrodynamics code in heavy use throughout several computational science and engineering communities.

Concurrency and Computation. Practice and Experience

Lin, Paul L.; Heroux, Michael A.; Williams, Alan B.; Barrett, Richard F.

The performance of a large-scale, production-quality science and engineering application (‘app’) is often dominated by a small subset of the code. Even within that subset, computational and data access patterns are often repeated, so that an even smaller portion can represent the performance-impacting features. If application developers, parallel computing experts, and computer architects can together identify this representative subset and then develop a small mini-application (‘miniapp’) that can capture these primary performance characteristics, then this miniapp can be used to both improve the performance of the app as well as provide a tool for co-design for the high-performance computing community. However, a critical question is whether a miniapp can effectively capture key performance behavior of an app. This study provides a comparison of an implicit finite element semiconductor device modeling app on unstructured meshes with an implicit finite element miniapp on unstructured meshes. The goal is to assess whether the miniapp is predictive of the performance of the app. Finally, single compute node performance will be compared, as well as scaling up to 16,000 cores. Results indicate that the miniapp can be reasonably predictive of the performance characteristics of the app for a single iteration of the solver on a single compute node.

Vaughan, Courtenay T.; Barrett, Richard F.

Abstract not provided.

Lin, Paul L.; Barrett, Richard F.

Abstract not provided.

Proceedings of the 6th International Workshop on Programming Models and Applications for Multicores and Manycores, PMAM 2015

Barrett, Richard F.; Stark, Dylan S.; Vaughan, Courtenay T.; Grant, Ryan E.; Olivier, Stephen L.; Pedretti, Kevin P.

The Bulk Synchronous Parallel programming model is showing performance limitations at high processor counts. We propose over-decomposition of the domain, operated on as tasks, to smooth out utilization of the computing resource, in particular the node interconnect and processing cores, and hide intra- and inter-node data movement. Our approach maintains the existing coding style commonly employed in computational science and engineering applications. Although we show improved performance on existing computers, up to 131,072 processor cores, the effectiveness of this approach on expected future architectures will require the continued evolution of capabilities throughout the codesign stack. Success then will not only result in decreased time to solution, but would also make better use of the hardware capabilities and reduce power and energy requirements, while fundamentally maintaining the current code configuration strategy.

Rajan, Mahesh R.; Doerfler, Douglas W.; Hammond, Simon D.; Trott, Christian R.; Barrett, Richard F.

Abstract not provided.

Hoekstra, Robert J.; Barrett, Richard F.; howell, louis h.; daniel, david d.

This milestone was the 2nd in a series of Tri-Lab Co-Design L2 milestones supporting ‘Co-Design’ efforts in the ASC program. It is a crucial step towards evaluating the effectiveness of proxy applications in exploring code performance on next generation architectures. All three labs evaluated the performance of 2 proxy applications on modern architectures and/or testbeds for pre-production hardware. The results are captured in this document as well as annotated presentations from all 3 laboratories.

Barrett, Richard F.; Stark, Dylan S.; Vaughan, Courtenay T.; Grant, Ryan E.; Olivier, Stephen L.; Pedretti, Kevin P.

Abstract not provided.

Vaughan, Courtenay T.; Barrett, Richard F.; Roweth, Duncan R.

Abstract not provided.

Rajan, Mahesh R.; Doerfler, Douglas W.; Barrett, Richard F.; Stevenson, Joel O.; Agelastos, Anthony M.; Shaw, Ryan P.; Meyer, Harold E.

Abstract not provided.

Vaughan, Courtenay T.; Barrett, Richard F.; Jayaraj, Jagan J.

Abstract not provided.

Proceedings of Co-HPC 2014: 1st International Workshop on Hardware-Software Co-Design for High Performance Computing - Held in Conjunction with SC 2014: The International Conference for High Performance Computing, Networking, Storage and Analysis

Woodward, Paul R.; Jayaraj, Jagan J.; Barrett, Richard F.

The Piecewise Parabolic Method (PPM) was designed as a means of exploring compressible gas dynam-ics problems of interest in astrophysics, including super-sonic jets, compressible turbulence, stellar convection, and turbulent mixing and burning of gases in stellar interiors. Over time, the capabilities encapsulated in PPM have co-evolved with the availability of a series of high performance computing platforms. Implementation of the algorithm has adapted to and advanced with the architectural capabilities and characteristics of these machines. This adaptability of our PPM codes has enabled targeted astrophysical applica-tions of PPM to exploit these scarce resources to explore complex physical phenomena. Here we describe the means by which this was accomplished, and set a path forward, with a new miniapp, mPPM, for continuing this process in a diverse and dynamic architecture design environment. Adaptations in mPPM for the latest high performance machines are discussed that address the important issue of limited bandwidth from locally attached main memory to the microprocessor chip.

Proceedings of ExaMPI 2014: Exascale MPI 2014 - held in conjunction with SC 2014: The International Conference for High Performance Computing, Networking, Storage and Analysis

Stark, Dylan S.; Barrett, Richard F.; Grant, Ryan E.; Olivier, Stephen L.; Pedretti, Kevin P.; Vaughan, Courtenay T.

Advances in node-level architecture and interconnect technology needed to reach extreme scale necessitate a reevaluation of long-standing models of computation, in particular bulk synchronous processing. The end of Dennard-scaling and subsequent increases in CPU core counts each successive generation of general purpose processor has made the ability to leverage parallelism for communication an increasingly critical aspect for future extreme-scale application performance. But the use of massive multithreading in combination with MPI is an open research area, with many proposed approaches requiring code changes that can be unfeasible for important large legacy applications already written in MPI. This paper covers the design and initial evaluation of an extension of a massive multithreading runtime system supporting dynamic parallelism to interface with MPI to handle fine-grain parallel communication and communication-computation overlap. Our initial evaluation of the approach uses the ubiquitous stencil computation, in three dimensions, with the halo exchange as the driving example that has a demonstrated tie to real code bases. The preliminary results suggest that even for a very well-studied and balanced workload and message exchange pattern, co-scheduling work and communication tasks is effective at significant levels of decomposition using up to 131,072 cores. Furthermore, we demonstrate useful communication-computation overlap when handling blocking send and receive calls, and show evidence suggesting that we can decrease the burstiness of network traffic, with a corresponding decrease in the rate of stalls (congestion) seen on the host link and network.

Parallel Processing Letters

Barrett, Richard F.; Vaughan, Courtenay T.; Hammond, Simon D.

For over two decades the dominant means for enabling portable performance of computational science and engineering applications on parallel processing architectures has been the bulk-synchronous parallel programming (BSP) model. Code developers, motivated by performance considerations to minimize the number of messages transmitted, have typically pursued a strategy of aggregating message data into fewer, larger messages. Emerging and future high-performance architectures, especially those seen as targeting Exascale capabilities, provide motivation and capabilities for revisiting this approach. In this paper we explore alternative configurations within the context of a large-scale complex multi-physics application and a proxy that represents its behavior, presenting results that demonstrate some important advantages as the number of processors increases in scale.

Rider, William J.; Kelly, Suzanne M.; Barrett, Richard F.; Hammond, Simon D.

Abstract not provided.