Publications

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Tensor decomposition models play an increasingly important role in modern data science applications. One problem of particular interest is fitting a low-rank Canonical Polyadic (CP) tensor decomposition model when the tensor has sparse structure and the tensor elements are nonnegative count data. SparTen is a high-performance C++ library which computes a low-rank decomposition using different solvers: a first-order quasi-Newton or a second-order damped Newton method, along with the appropriate choice of runtime parameters. Since default parameters in SparTen are tuned to experimental results in prior published work on a single real-world dataset conducted using MATLAB implementations of these methods, it remains unclear if the parameter defaults in SparTen are appropriate for general tensor data. Furthermore, it is unknown how sensitive algorithm convergence is to changes in the input parameter values. This report addresses these unresolved issues with large-scale experimentation on three benchmark tensor data sets. Experiments were conducted on several different CPU architectures and replicated with many initial states to establish generalized profiles of algorithm convergence behavior.

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Multi-dimensional arrays are ubiquitous in high-performance computing (HPC), but their absence from the C++ language standard is a long-standing and well-known limitation of their use for HPC. This paper describes the design and implementation of mdspan, a proposed C++ standard multidimensional array view (planned for inclusion in C++23). The proposal is largely inspired by work done in the Kokkos project - a C++ performance-portable programming model de- ployed by numerous HPC institutions to prepare their code base for exascale-class supercomputing systems. This paper describes the final design of mdspan af- ter a five-year process to achieve consensus in the C++ community. In particular, we will lay out how the design addresses some of the core challenges of performance-portable programming, and how its cus- tomization points allow a seamless extension into areas not currently addressed by the C++ Standard but which are of critical importance in the heterogeneous computing world of today's systems. Finally, we have provided a production-quality implementation of the proposal in its current form. This work includes several benchmarks of this implementation aimed at demon- strating the zero-overhead nature of the modern design.

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For at least the last 20 years, many have tried to create a general resource management system to support interoperability across various concurrent libraries. The previous strategies all suffered from additional toolchain requirements, and/or a usage of a shared programing model that assumed it owned/controlled access to all resources available to the program. None of these techniques have achieved wide spread adoption. The ubiquity of OpenMP coupled with C++ developing a standard way to describe many different concurrent paradigms (C++23 executors) would allow OpenMP to assume the role of a general resource manager without requiring user code written directly in OpenMP. With a few added features such as the ability to use otherwise idle threads to execute tasks and to specify a task “width”, many interesting concurrent frameworks could be developed in native OpenMP and achieve high performance. Further, one could create concrete C++ OpenMP executors that enable support for general C++ executor based codes, which would allow Fortran, C, and C++ codes to use the same underlying concurrent framework when expressed as native OpenMP or using language specific features. Effectively, OpenMP would become the de facto solution for a problem that has long plagued the HPC community.

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Task-based execution models have received considerable attention in recent years to meet the performance challenges facing high-performance computing (HPC). In this paper we introduce MetaPASS-Metaprogramming-enabled Para-llelism from Apparently Sequential Semantics-a proof-of-concept, non-intrusive header library that enables implicit task-based parallelism in a sequential C++ code. MetaPASS is a data-driven model, relying on dependency analysis of variable read-/write accesses to derive a directed acyclic graph (DAG) of the computation to be performed. MetaPASS enables embedding of runtime dependency analysis directly in C++ applications using only template metaprogramming. Rather than requiring verbose task-based code or source-to-source compilers, a native C++ code can be made task-based with minimal modifications. We present an overview of the programming model enabled by MetaPASS and the C++ runtime API required to support it. Details are provided regarding how standard template metaprogramming is used to capture task dependencies. We finally discuss how the programming model can be deployed in both an MPI+X and in a standalone distributed memory context.

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We explore the use of asynchronous many-task (AMT) programming models for the implementation of in situ analysis towards the goal of maximizing programmer productivity and overall performance on next generation platforms. We describe how a broad class of statistics algorithms can be transformed from a traditional single-programm multiple-data (SPMD) implementation to an AMT implementation, demonstrating with a concrete example: a measurement of descriptive statistics implemented in Legion. Our experiments to quantify the benefit and possible drawbacks of this approach are in progress, and we present some encouraging initial results on the (minimal) impact of the AMT-based approach on code complexity, task scheduling, and application scalability.

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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

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In this position paper, we argue for improved fault-tolerance of an MPI code by introducing lightweight virtualization into the MPI interface. In particular, we outline key-value store semantics for MPI send/recv calls, thereby creating a far more expressive programming model. The general message passing semantics and imperative style of MPI application codes would remain essentially unchanged. However, the additional expressiblity of the programming model 1) enables the underlying transport layer to handle faulttolerance more transparently to the application developer, and 2) provides an evolutionary code path towards more declarative asynchronous programming models. The core contribution of this paper is an initial implementation of the DHARMA transport layer that provides the new, required functionality to support the MPI key-value store model.

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