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DOE OSTI2025

Preparing MPICH for exascale

The advent of exascale supercomputers heralds a new era of scientific discovery, yet it introduces significant architectural challenges that must be overcome for MPI applications to fully exploit its potential. Among these challenges is the adoption of heterogeneous architectures, particularly the integration of GPUs to accelerate computation. Additionally, the complexity of multithreaded programming models has also become a critical factor in achieving performance at scale. The efficient utilization of hardware acceleration for communication, provided by modern NICs, is also essential for achieving low latency and high throughput communication in such complex systems. In response to these challenges, the MPICH library, a high-performance and widely used Message Passing Interface (MPI) implementation, has undergone significant enhancements. Here, this paper presents four major contributions that prepare MPICH for the exascale transition. First, we describe a lightweight communication stack that leverages the advanced features of modern NICs to maximize hardware acceleration. Second, our work showcases a highly scalable multithreaded communication model that addresses the complexities of concurrent environments. Third, we introduce GPU-aware communication capabilities that optimize data movement in GPU-integrated systems. Finally, we present a new datatype engine aimed at accelerating the use of MPI derived datatypes on GPUs. These improvements in the MPICH library not only address the immediate needs of exascale computing architectures but also set a foundation for exploiting future innovations in high-performance computing. By embracing these new designs and approaches, MPICH-derived libraries from HPE Cray and Intel were able to achieve real exascale performance on OLCF Frontier and ALCF Aurora respectively.

Guo, Yanfei [Argonne National Laboratory (ANL), Ar↗
DOE OSTI2018

Scalable deep text comprehension for Cancer surveillance on high-performance computing

Background: Deep Learning (DL) has advanced the state-of-the-art capabilities in bioinformatics applications which has resulted in trends of increasingly sophisticated and computationally demanding models trained by larger and larger data sets. This vastly increased computational demand challenges the feasibility of conducting cutting-edge research. One solution is to distribute the vast computational workload across multiple computing cluster nodes with data parallelism algorithms. In this study, we used a High-Performance Computing environment and implemented the Downpour Stochastic Gradient Descent algorithm for data parallelism to train a Convolutional Neural Network (CNN) for the natural language processing task of information extraction from a massive dataset of cancer pathology reports. We evaluated the scalability improvements using data parallelism training and the Titan supercomputer at Oak Ridge Leadership Computing Facility. To evaluate scalability, we used different numbers of worker nodes and performed a set of experiments comparing the effects of different training batch sizes and optimizer functions.Results: We found that Adadelta would consistently converge at a lower validation loss, though requiring over twice as many training epochs as the fastest converging optimizer, RMSProp. The Adam optimizer consistently achieved a close 2nd place minimum validation loss significantly faster; using a batch size of 16 and 32 allowed the network to converge in only 4.5 training epochs.Conclusions: We demonstrated that the networked training process is scalable across multiple compute nodes communicating with message passing interface while achieving higher classification accuracy compared to a traditional machine learning algorithm.

Qiu, John X.↗
DOE OSTI2011

Creating a Parallel Version of VisIt for Microsoft Windows

VisIt is a popular, free interactive parallel visualization and analysis tool for scientific data. Users can quickly generate visualizations from their data, animate them through time, manipulate them, and save the resulting images or movies for presentations. VisIt was designed from the ground up to work on many scales of computers from modest desktops up to massively parallel clusters. VisIt is comprised of a set of cooperating programs. All programs can be run locally or in client/server mode in which some run locally and some run remotely on compute clusters. The VisIt program most able to harness today's computing power is the VisIt compute engine. The compute engine is responsible for reading simulation data from disk, processing it, and sending results or images back to the VisIt viewer program. In a parallel environment, the compute engine runs several processes, coordinating using the Message Passing Interface (MPI) library. Each MPI process reads some subset of the scientific data and filters the data in various ways to create useful visualizations. By using MPI, VisIt has been able to scale well into the thousands of processors on large computers such as dawn and graph at LLNL. The advent of multicore CPU's has made parallelism the 'new' way to achieve increasing performance. With today's computers having at least 2 cores and in many cases up to 8 and beyond, it is more important than ever to deploy parallel software that can use that computing power not only on clusters but also on the desktop. We have created a parallel version of VisIt for Windows that uses Microsoft's MPI implementation (MSMPI) to process data in parallel on the Windows desktop as well as on a Windows HPC cluster running Microsoft Windows Server 2008. Initial desktop parallel support for Windows was deployed in VisIt 2.4.0. Windows HPC cluster support has been completed and will appear in the VisIt 2.5.0 release. We plan to continue supporting parallel VisIt on Windows so our users will be able to take full advantage of their multicore resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Performance Analysis of and Tool Support for Transactional Memory on BG/Q

Martin Schindewolf worked during his internship at the Lawrence Livermore National Laboratory (LLNL) under the guidance of Martin Schulz at the Computer Science Group of the Center for Applied Scientific Computing. We studied the performance of the TM subsystem of BG/Q as well as researched the possibilities for tool support for TM. To study the performance, we run CLOMP-TM. CLOMP-TM is a benchmark designed for the purpose to quantify the overhead of OpenMP and compare different synchronization primitives. To advance CLOMP-TM, we added Message Passing Interface (MPI) routines for a hybrid parallelization. This enables to run multiple MPI tasks, each running OpenMP, on one node. With these enhancements, a beneficial MPI task to OpenMP thread ratio is determined. Further, the synchronization primitives are ranked as a function of the application characteristics. To demonstrate the usefulness of these results, we investigate a real Monte Carlo simulation called Monte Carlo Benchmark (MCB). Applying the lessons learned yields the best task to thread ratio. Further, we were able to tune the synchronization by transactifying the MCB. Further, we develop tools that capture the performance of the TM run time system and present it to the application's developer. The performance of the TM run time system relies on the built-in statistics. These tools use the Blue Gene Performance Monitoring (BGPM) interface to correlate the statistics from the TM run time system with performance counter values. This combination provides detailed insights in the run time behavior of the application and enables to track down the cause of degraded performance. Further, one tool has been implemented that separates the performance counters in three categories: Successful Speculation, Unsuccessful Speculation and No Speculation . All of the tools are crafted around IBM's xlc compiler for C and C++ and have been run and tested on a Q32 early access system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

MPI-Bash: Parallel scripting right from the Bourne-Again Shell (Bash)

Parallel, scientific applications running on massively parallel supercomputers commonly produce large numbers of large data files. While parallel filesystems improve the performance of file generation, postprocessing activities such as archiving and compressing the data are often run sequentially (and therefore slowly), squandering the supercomputer's vast performance. Consequently, data that take hours to generate may take days to postprocess. Writing and debugging parallel postprocessing programs is often too time-consuming to be worthwhile, especially for one-off postprocessing operations. What the community needs is a simple mechanism for cobbling together scripts that distribute postprocessing work across a supercomputer's many nodes. MPI-Bash attempts to supply that mechanism by augmenting the GNU project's command shell, Bash, with support for the Message Passing Interface (MPI) and, if available, the Libcircle library for work distribution and load balancing. MPI-Bash makes it easy to parallelize bash scripts that run a set of Linux commands independently over a large number of input files. Because MPI-Bash includes various MPI functions for data transfer and synchronization, it is not limited to embarrassingly parallel workloads but can incorporate phased operations (i.e., all workers must finish operation X before any worker is allowed to begin operation Y).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Design and simulation of the nuSTORM facility

This thesis provides a full design and simulation of the nuSTORM beam line facility including the pion beam line , the pion and muon orbit combination section, and the muon decay ring. The study also includes the development of numerical optimization methods and their application to the design. At nuSTORM, one proton batch from the Main Injector (MI) is extracted and transported to the target station to bombard a solid target. A magnetic horn is used as the collection device for the secondary particles such as pions and kaons. A pion beam line is designed to transport the secondary particles downstream to the muon decay ring. In order to avoid the use of a fast kicker with a large aperture for the injection, an Orbit Combination Section (OCS) was designed to combine the reference pion orbit with the circulating muon orbit. This ful lls the stochastic injection scheme proposed in the 1980s. It is shown through the simulation that the pion beam line is able to deliver 0.011 muons per proton on target within the acceptance of the ring. In order to improve the circulating muon ux, an optimization of the magnetic collection horn was studied. A Multi-Objective Genetic Algorithm (MOGA), that was modi ed to integrate Message Passing Interface (MPI) in the process, is applied to the optimization. The number of muons within the ring acceptance from the pion decay is estimated by the pions after the collection horn. This new method was proposed to lower the requirement on the computing resources for the optimization. The study gives a new and better target and horn con guration after a full investigation of the system. The nuSTORM ring is examined with two optics designs. With di erent features, the two designs are optimized via correcting the nonlinearities with sextupolar eld distribution in the ring. A modi ed MPI-based Simulated Annealing (SA) algorithm was applied to obtain the optimal ring acceptance. The comparison with the GA shows good agreements on the achieved optimal ring acceptance. The pion and muon beams at nuSTORM were simulated and yield avor-pure and precisely measurable neutrino beams, which can be used to provide a de nite statement about the existence of a sterile neutrino.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Planning for Pre-Exascale Platform Environment (Fiscal Year 2015 Level 2 Milestone 5216)

This Plan for ASC Pre-Exascale Platform Environments document constitutes the deliverable for the fiscal year 2015 (FY15) Advanced Simulation and Computing (ASC) Program Level 2 milestone Planning for Pre-Exascale Platform Environment. It acknowledges and quantifies challenges and recognized gaps for moving the ASC Program towards effective use of exascale platforms and recommends strategies to address these gaps. This document also presents an update to the concerns, strategies, and plans presented in the FY08 predecessor document that dealt with the upcoming (at the time) petascale high performance computing (HPC) platforms. With the looming push towards exascale systems, a review of the earlier document was appropriate in light of the myriad architectural choices currently under consideration. The ASC Program believes the platforms to be fielded in the 2020s will be fundamentally different systems that stress ASC’s ability to modify codes to take full advantage of new or unique features. In addition, the scale of components will increase the difficulty of maintaining an errorfree system, thus driving new approaches to resilience and error detection/correction. The code revamps of the past, from serial- to vector-centric code to distributed memory to threaded implementations, will be revisited as codes adapt to a new message passing interface (MPI) plus “x” or more advanced and dynamic programming models based on architectural specifics. Development efforts are already underway in some cases, and more difficult or uncertain aspects of the new architectures will require research and analysis that may inform future directions for program choices. In addition, the potential diversity of system architectures may require parallel if not duplicative efforts to analyze and modify environments, codes, subsystems, libraries, debugging tools, and performance analysis techniques as well as exploring new monitoring methodologies. It is difficult if not impossible to selectively eliminate some of these activities until more information is available through simulations of potential architectures, analysis of systems designs, and informed study of commodity technologies that will be the constituent parts of future platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Programming Models in HPC

These are the slides for a presentation on programming models in HPC, at the Los Alamos National Laboratory's Parallel Computing Summer School. The following topics are covered: Flynn's Taxonomy of computer architectures; single instruction single data; single instruction multiple data; multiple instruction multiple data; address space organization; definition of Trinity (Intel Xeon-Phi is a MIMD architecture); single program multiple data; multiple program multiple data; ExMatEx workflow overview; definition of a programming model, programming languages, runtime systems; programming model and environments; MPI (Message Passing Interface); OpenMP; Kokkos (Performance Portable Thread-Parallel Programming Model); Kokkos abstractions, patterns, policies, and spaces; RAJA, a systematic approach to node-level portability and tuning; overview of the Legion Programming Model; mapping tasks and data to hardware resources; interoperability: supporting task-level models; Legion S3D execution and performance details; workflow, integration of external resources into the programming model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Specification of Fenix MPI Fault Tolerance library version 1.0.1

This document provides a specification of Fenix, a software library compatible with the Message Passing Interface (MPI) to support fault recovery without application shutdown. The library consists of two modules. The first, termed process recovery , restores an application to a consistent state after it has suffered a loss of one or more MPI processes (ranks). The second specifies functions the user can invoke to store application data in Fenix managed redundant storage, and to retrieve it from that storage after process recovery.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2017

HPC Institutional Computing Project: W15_lesreactiveflow KIVA-hpFE Development: A Robust and Accurate Engine Modeling Software

KIVA-hpFE is a high performance computer software for solving the physics of multi-species and multiphase turbulent reactive flow in complex geometries having immersed moving parts. The code is written in Fortran 90/95 and can be used on any computer platform with any popular complier. The code is in two versions, a serial version and a parallel version utilizing MPICH2 type Message Passing Interface (MPI or Intel MPI) for solving distributed domains. The parallel version is at least 30x faster than the serial version and much faster than our previous generation of parallel engine modeling software, by many factors. The 5th generation algorithm construction is a Galerkin type Finite Element Method (FEM) solving conservative momentum, species, and energy transport equations along with two-equation turbulent model k-ω Reynolds Averaged Navier-Stokes (RANS) model and a Vreman type dynamic Large Eddy Simulation (LES) method. The LES method is capable modeling transitional flow from laminar to fully turbulent; therefore, this LES method does not require special hybrid or blending to walls. The FEM projection method also uses a Petrov-Galerkin (P-G) stabilization along with pressure stabilization. We employ hierarchical basis sets, constructed on the fly with enrichment in areas associated with relatively larger error as determined by error estimation methods. In addition, when not using the hp-adaptive module, the code employs Lagrangian basis or shape functions. The shape functions are constructed for hexahedral, prismatic and tetrahedral elements. The software is designed to solve many types of reactive flow problems, from burners to internal combustion engines and turbines. In addition, the formulation allows for direct integration of solid bodies (conjugate heat transfer), as in heat transfer through housings, parts, cylinders. It can also easily be extended to stress modeling of solids, used in fluid structure interactions problems, solidification, porous media modeling and magneto hydrodynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

PLATO (Parallel Load Assignment Tool): A Parallel Workload Partitioner for Particle-based Methods

A key first step in many large-scale high-performance scientific computing applications is the distribution of the computational domain across all participating processors of a parallel computing platform such as a shared memory machine, cluster or a tightly-coupled supercomputer. The primary goal of such a partitioning step is to ensure that the computational work performed by each process remains load-balanced throughout the execution of the parallel application. In this project, a general-purpose parallel partitioning tool, code-named PLATO, was designed and implemented. PLATO assumes that the workload is a set of points in an n-dimensional metric space. These points are abstractions of computational units that may represent point-like computational units such as celestial objects in astrophysics, atoms in materials sciences and molecules in biology/chemistry, or may represent more abstract computational units such as grid points in finite difference methods, grid elements in finite element methods or n-dimensional points in the feature space of a host of other applications. PLATO supports several key and useful capabilities, namely: (a) multiple load partitioning strategies, (b) support for arbitrary user-defined processor topology, (c) pre-fetching data from neighboring processors based on user-defined parameters, and (d) support for arbitrary number and types of attributes associated with each point in the computational domain. PLATO is implemented in the C programming language using Message Passing Interface (MPI) for inter-processor communications and its parallel performance tested on up to 48 processors of a shared memory machine. This report includes a detailed performance study of PLATO using parallel speedup and iso-efficiency as the two main metrics of parallel performance. In addition, a user manual and DOXYGEN generated code documentation have been included for completeness.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

ASC ATDM Level 2 Milestone #5325: Asynchronous Many-Task Runtime System Analysis and Assessment for Next Generation Platforms

This report provides in-depth information and analysis to help create a technical road map for developing next-generation programming models and runtime systems that support Advanced Simulation and Computing (ASC) work- load requirements. The focus herein is on asynchronous many-task (AMT) model and runtime systems, which are of great interest in the context of "Oriascale7 computing, as they hold the promise to address key issues associated with future extreme-scale computer architectures. This report includes a thorough qualitative and quantitative examination of three best-of-class AIM] runtime systems – Charm-++, Legion, and Uintah, all of which are in use as part of the Centers. The studies focus on each of the runtimes' programmability , performance , and mutability . Through the experiments and analysis presented, several overarching Predictive Science Academic Alliance Program II (PSAAP-II) Asc findings emerge. From a performance perspective, AIV runtimes show tremendous potential for addressing extreme- scale challenges. Empirical studies show an AM runtime can mitigate performance heterogeneity inherent to the machine itself and that Message Passing Interface (MP1) and AM11runtimes perform comparably under balanced conditions. From a programmability and mutability perspective however, none of the runtimes in this study are currently ready for use in developing production-ready Sandia ASC applications. The report concludes by recommending a co- design path forward, wherein application, programming model, and runtime system developers work together to define requirements and solutions. Such a requirements-driven co-design approach benefits the community as a whole, with widespread community engagement mitigating risk for both application developers developers. and high-performance computing runtime systein

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Survey of MPI Usage in the U.S. Exascale Computing Project

The Exascale Computing Project (ECP) is currently the primary effort in the United States focused on developing “exascale” levels of computing capability, including hardware, software and applications. In order to obtain a more thorough understanding of how the software projects under the ECP are using, and planning to use the Message Passing Interface (MPI), and help guide the work of our own project within the ECP, we created a survey. Of the 97 ECP projects active at the time the survey was distributed, we received 77 responses, 56 of which reported that their projects were using MPI. This paper reports the results of that survey for the benefit of the broader community of MPI developers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Specification of Fenix MPI Fault Tolerance library (V.0.9)

Fenix is a software library compatible with the Message Passing Interface (MPI) to support fault recovery without application shutdown. This specification is derived from a current implementation of Fenix that employs the User Level Fault Mitigation (ULFM) MPI fault tolerance module proposal. We only present the C library interface for Fenix; the Fortran interface will be added once the C version is complete.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Specification of Fenix MPI Fault Tolerance library (V. 1.0)

This document provides a specification of Fenix, a software library compatible with the Message Passing Interface (MPI) to support fault recovery without application shutdown. The library consists of two modules. The first, termed process recovery, restores an application to a consistent state after it has suffered a loss of one or more MPI processes (ranks). The second specifies functions the user can invoke to store application data in Fenix managed redundant storage, and to retrieve it from that storage after process recovery.

97 MATHEMATICS AND COMPUTING↗