The Uintah Computational Framework is being prepared to make portable use of forthcoming exascale systems, initially the DOE Aurora system through the Aurora Early Science Program. This paper describes the evolution of Uintah to be ready for such architectures. A key part of this preparation has been the adoption of the Kokkos performance portability layer in Uintah. The sheer size of the Uintah codebase has made it imperative to have a representative benchmark. The design of this benchmark and the use of Kokkos within it is discussed. This paper complements recent work with additional details and new scaling studies run 24x further than earlier studies. Results are shown for two benchmarks executing workloads representative of typical Uintah applications. These results demonstrate single-source portability across the DOE Summit and NSF Frontera systems with good strong-scaling characteristics. The challenge of extending this approach to anticipated exascale systems is also considered.

Adaptive mesh refinement (AMR) is an important method that enables many mesh-based applications to run at effectively higher resolution within limited computing resources by allowing high resolution only where really needed. This advantage comes at a cost, however: greater complexity in the mesh management machinery and challenges with load distribution. With the current trend of increasing heterogeneity in hardware architecture, AMR presents an orthogonal axis of complexity. The usual techniques, such as asynchronous communication and hierarchy management for parallelism and memory that are necessary to obtain reasonable performance are very challenging to reason about with AMR. Different groups working with AMR are bringing different approaches to this challenge. Here, we examine the design choices of several AMR codes and also the degree to which demands placed on them by their users influence these choices.

The complexity of heterogeneous nodes near and at exascale has increased the need for “heroic” programming efforts. To accommodate this complexity, significant investment is required for codes not yet optimizing for low-level architecture features (e.g., wide vector units) and/or running at large-scale. This paper describes ongoing efforts to combine two codes, Hedgehog and Uintah, lying at both extremes to ease programming efforts. The end goals of this effort are (1) to combine the two codes to make an asynchronous many-task runtime system specializing in both node-level and large-scale performance and (2) to further improve the accessibility of both with portable abstractions. A prototype adopting Hedgehog in Uintah and a prototype extending Hedgehog to support MPI+X hybrid parallelism are discussed. Results achieving ∼60% of NVIDIA V100 GPU peak performance for a distributed DGEMM problem are shown for a naive MPI+Hedgehog implementation before any attempt to optimize for performance.

Authors note: This is a refereed but unpublished report that was
submitted to, reviewed for and accepted in revised form for a presentation of the same material at the Hipar Workshop at Supercomputing 21

Exascale supercomputers capable of computing 10¹⁸ double-precision floating point operations per second are expected to be operational around 2022/23. The complexity and diversity of the proposed exascale machines pose new challenges for the software applications, namely, 1) implementing efficient data management; 2) having programming systems to exploit locality and multimillion parallelism; 3) developing efficient algorithms to leverage new architectures; 4) ensuring resiliency; and 5) improving scientific productivity on diverse architectures. Due to data-driven scheduling and asynchronous execution, Asynchronous Many-Task (AMT) runtime systems show promise to handle these exascale challenges.

One such AMT, the Uintah Computational Framework, maintains two distinct layers for the application and underlying runtime infrastructure. This distinction allows Uintah users to concentrate on application and the Uintah infrastructure handles communication, data coherency, multithreading, and architecture-specific complexities.

This dissertation addresses some of the exascale challenges and also integrates the individual solutions under the single umbrella of Uintah. The resiliency approach handles node failure faster than the traditional checkpointing method and helps to address challenge (4). A potential solution for challenges (2) and (3) can be the new asynchronous scheduler designed for the Sunway Taihulight supercomputer that shows the benefits of asynchronous execution. The novel portable Single Instruction Multiple Data (SIMD) primitive provides a prospective approach to handle (2) and (5), which achieves near-ideal vectorization on Central Processing Units (CPUs) along with Graphics Processing Unit (GPU) portability provided by the CUDA back end. The newly developed threading model using MPI endpoints shows performance improvements over the MPI-everywhere version, which can be one of the solutions to tackle challenges (2) and (3). Finally, this work enhances the heterogeneous scheduler, contributes to the ongoing portability drive, and successfully runs a simulation using portable AMT tasks on thousands of CPUs and GPUs. These enhancements are important to answer challenges (2), (3), and (5). As a result, this research takes Uintah closer to exascale readiness. Using Uintah as an example, this work demonstrates how AMTs, third-party libraries, and applications can be enhanced to benefit from the next-generation architectures.

The paper gives an overview of Material Point Method and shows its evolution over the last 25 years. The Material Point Method developments followed a logical order. The article aims at identifying this order and show not only the current state of the art, but explain the drivers behind the developments and identify what is currently still missing.The paper explores modern implementations of both explicit and implicit Material Point Method. It concentrates mainly on uses of the method in engineering, but also gives a short overview of Material Point Method application in computer graphics and animation. Furthermore, the article gives overview of errors in the material point method algorithms, as well as identify gaps in knowledge, filling which would hopefully lead to a much more efficient and accurate Material Point Method. The paper also briefly discusses algorithms related to contact and boundaries, coupling the Material Point Method with other numerical methods and modeling of fractures. It also gives an overview of modeling of multi-phase continua with Material Point Method. The paper closes with numerical examples, aiming at showing the capabilities of Material Point Method in advanced simulations. Those include landslide modeling, multiphysics simulation of shaped charge explosion and simulations of granular material flow out of a silo undergoing changes from continuous to discontinuous and back to continuous behavior.The paper uniquely illustrates many of the developments not only with figures but also with videos, giving the whole extend of simulation instead of just a timestamped image

The paper gives an overview of Material Point Method and shows its evolution over the last 25 years. The Material Point Method developments followed a logical order. The article aims at identifying this order and show not only the current state of the art, but explain the drivers behind the developments and identify what is currently still missing.The paper explores modern implementations of both explicit and implicit Material Point Method. It concentrates mainly on uses of the method in engineering, but also gives a short overview of Material Point Method application in computer graphics and animation. Furthermore, the article gives overview of errors in the material point method algorithms, as well as identify gaps in knowledge, filling which would hopefully lead to a much more efficient and accurate Material Point Method. The paper also briefly discusses algorithms related to contact and boundaries, coupling the Material Point Method with other numerical methods and modeling of fractures. It also gives an overview of modeling of multi-phase continua with Material Point Method. The paper closes with numerical examples, aiming at showing the capabilities of Material Point Method in advanced simulations. Those include landslide modeling, multiphysics simulation of shaped charge explosion and simulations of granular material flow out of a silo undergoing changes from continuous to discontinuous and back to continuous behavior.The paper uniquely illustrates many of the developments not only with figures but also with videos, giving the whole extend of simulation instead of just a timestamped image

Supercomputing applications are increasingly adopting the MPI+threads programming model over the traditional “MPI everywhere” approach to better handle the disproportionate increase in the number of cores compared with other on-node resources. In practice, however, most applications observe a slower performance with MPI+threads primarily because of poor communication performance. Recent research efforts on MPI libraries address this bottleneck by mapping logically parallel communication, that is, operations that are not subject to MPI’s ordering constraints to the underlying network parallelism. Domain scientists, however, typically do not expose such communication independence information because the existing MPI-3.1 standard’s semantics can be limiting. Researchers had initially proposed user-visible endpoints to combat this issue, but such a solution requires intrusive changes to the standard (new APIs). The upcoming MPI-4.0 standard, on the other hand, allows applications to relax unneeded semantics and provides them with many opportunities to express logical communication parallelism. In this paper, we show how MPI+threads applications can achieve high performance with logically parallel communication. Through application case studies, we compare the capabilities of the new MPI-4.0 standard with those of the existing one and user-visible endpoints (upper bound). Logical communication parallelism can boost the overall performance of an application by over 2x.

The solution of large-scale combustion problems with codes such as Uintah on modern computer architectures requires the use of multithreading and GPUs to achieve performance. Uintah uses a low-Mach number approximation that requires iteratively solving a large system of linear equations. The Hypre iterative solver has solved such systems in a scalable way for Uintah, but the use of OpenMP with Hypre leads to at least 2x slowdown due to OpenMP overheads. The proposed solution uses the MPI Endpoints within Hypre, where each team of threads acts as a different MPI rank. This approach minimizes OpenMP synchronization overhead and performs as fast or (up to 1.44x) faster than Hypre’s MPI-only version, and allows the rest of Uintah to be optimized using OpenMP. The profiling of the GPU version of Hypre shows the bottleneck to be the launch overhead of thousands of micro-kernels. The GPU performance was improved by fusing these micro-kernels and was further optimized by using Cuda-aware MPI, resulting in an overall speedup of 1.16–1.44x compared to the baseline GPU implementation.

The above optimization strategies were published in the International Conference on Computational Science 2020. This work extends the previously published research by carrying out the second phase of communication-centered optimizations in Hypre to improve its scalability on large-scale supercomputers. This includes an efficient non-blocking inter-thread communication scheme, communication-reducing patch assignment, and expression of logical communication parallelism to a new version of the MPICH library that utilizes the underlying network parallelism. The above optimizations avoid communication bottlenecks previously observed during strong scaling and improve performance by up to 2x on 256 nodes of Intel Knight’s Landing processor.

Uncertainty and diversity in future HPC systems, including those for exascale, makes portable codebases desirable. To ease future ports, the Uintah Computational Framework has adopted the Kokkos C++ Performance Portability Library. This paper describes infrastructure advancements and performance improvements using partitioning functionality recently added to Kokkos within Uintah's MPI+Kokkos hybrid parallelism approach. Results are presented for two challenging calculations that have been refactored to support Kokkos::OpenMP and Kokkos::Cuda back-ends. These results demonstrate performance improvements up to (i) 2.66x when refactoring for portability, (ii) 81.59x when adding loop-level parallelism via Kokkos back-ends, and (iii) 2.63x when more eciently using a node. Good strong-scaling characteristics to 442,368 threads across 1728 Knights Landing processors are also shown. These improvements have been achieved with little added overhead (sub-millisecond, consuming up to 0.18% of per-timestep time). Kokkos adoption and refactoring lessons are also discussed.

Diversity among supported architectures in current and emerging high performance computing systems, including those for exascale, makes portable codebases desirable. Portability of a codebase can be improved using a performance portability layer to provide access to multiple underlying programming models through a single interface. Direct adoption of a performance portability layer, however, poses challenges for large pre-existing software frameworks that may need to preserve legacy code and/or adopt other programming models in the future. This paper describes an approach for indirect adoption that introduces a framework-specific portability layer between the application developer and the adopted performance portability layer to help improve legacy code support and long-term portability for future architectures and programming models. This intermediate layer uses loop-level, application-level, and build-level components to ease adoption of a performance portability layer in large legacy codebases. Results are shown for two challenging case studies using this approach to make portable use of OpenMP and CUDA via Kokkos in an asynchronous many-task runtime system, Uintah. These results show performance improvements up to 2.7x when refactoring for portability and 2.6x when more efficiently using a node. Good strong-scaling to 442,368 threads across 1,728 Knights Landing processors are also shown using MPI+Kokkos at scale.

The challenge of running complex physics code on the largest computers available has led to dataflow paradigms being explored. While such approaches are often applied at smaller scales, the challenge of extreme-scale data flow computing remains. The Uintah dataflow framework has consistently used dataflow computing at the largest scales on complex physics applications. At present Uintah contains two main dataflow models. Both are based upon asynchronous communication. One uses a static graph-based approach with asynchronous communication and the other uses a more dynamic approach that was introduced almost a decade ago. Subsequent changes within the Uintah runtime system combined with many more large scale experiments, has necessitated a reevaluation of these two approaches, comparing them in the context of large scale problems. While the static approach has worked well for some large-scale simulations, the dynamic approach is seen to offer performance improvements over the static case for a challenging fluid-structure interaction problem at large scale that involves fluid flow and a moving solid represented using particle method on an adaptive mesh.

Asynchronous many-task (AMT) runtimes are maturing as a model for computing simulations on a diverse range of architectures at large-scale. The Uintah AMT framework is driven by a philosophy of maintaining an application layer distinct from the underlying runtime while operating on an adaptive mesh grid. This model has enabled task devel-opers to focus on writing task code while minimizing their interaction with MPI transfers, halo processing, data stores, coherency of simulation variables, and proper ordering of task execution. Further, Uintah is implementing an architecture portable solution by utilizing the Kokkos programming portability layer so that application tasks can be written in one codebase and performantly executed on CPUs, GPUs, Intel Xeon Phis, and other future architectures.

Of these architectures, it is perhaps Nvidia GPUs that introduce the greatest usability and portability challenges for AMT runtimes. Specifically, Nvidia GPUs require code to adhere to a proprietary programming model, use separate high capacity memory, utilize asynchrony of data movement and execution, and partition execution units among many streaming multiprocessors. Numerous novel solutions to both Uintah and Kokkos are required to abstract these GPU features into an AMT runtime while preserving an appli-cation layer and enabling portability.

The focus of this AMT research is largely split into two main parts, performance and portability. Runtime performance comes from 1) minimizing runtime overhead when preparing simulation variables for tasks prior to execution, and 2) executing a hetero-geneous mixture of tasks to keep compute node processing units busy. Preparation of simulation variables, especially halo processing, receives significant emphasis as Uintah’s target problems heavily rely on local and global halos. In addition, this work covers automated data movement of simulation variables between host and GPU memory as well as distributing tasks throughout a GPU for execution.

Portability is a productivity necessity as application developers struggle to maintain three sets of code per task, namely code for single CPU core execution, CUDA code for GPU tasks, and a third set of code for Xeon Phi parallel execution. Programming portability layers, such as Kokkos, provide a framework for this portability, however, Kokkos itself requires modifications to support GPU execution of finer grained tasks typical of AMT runtimes like Uintah. Currently, Kokkos GPU parallel loop execution is bulk-synchronous. This research demonstrates a model for portable loops that is asynchronous, nonblocking, and performant. Additionally, integrating GPU portability into Uintah required additional modifications to aid the application developer in avoiding Kokkos specific details.

This research concludes by demonstrating a GPU-enabled AMT runtime that is both performant and portable. Further, application developers are not burdened with additional architecture specific requirements. Results are demonstrated using production task codebases written for CPUs, GPUs, and Kokkos portability and executed in GPU homogeneous and CPU/GPU heterogeneous environments.

The frequency of failures in upcoming exascale supercomputers may well be greater than at present due to many-core architectures if component failure rates remain unchanged. This potential increase in failure frequency coupled with I/O challenges at exascale may prove problematic for current resiliency approaches such as checkpoint restarting, although the use of fast intermediate memory may help. Algorithm-Based Fault Tolerance (ABFT) using Adaptive Mesh Refinement (AMR) is one resiliency approach used to address these challenges. For adaptive mesh codes, a coarse mesh version of the solution may be used to restore the fine mesh solution. This paper addresses the implementation of the ABFT approach within the Uintah software framework: both at a software level within Uintah and in the data reconstruction method used for the recovery of lost data. This method has two problems: inaccuracies introduced during the reconstruction propagate forward in time, and the physical consistency of variables such as positivity or boundedness may be violated during interpolation. These challenges can be addressed by the combination of two techniques: 1. a fault-tolerant MPI implementation to recover from runtime node failures, and 2. high-order interpolation schemes to preserve the physical solution and reconstruct lost data. The approach considered here uses a "Limited Essentially Non-Oscillatory" (LENO) scheme along with AMR to rebuild the lost data without checkpointing using Uintah. Experiments were carried out using a fault-tolerant MPI - ULFM to recover from runtime failure, and LENO to recover data on patches belonging to failed ranks, while the simulation was continued to the end. Results show that this ABFT approach is up to 10x faster than the traditional checkpointing method. The new interpolation approach is more accurate than linear interpolation and not subject to the overshoots found in other interpolation methods.

As application scientists develop and deploy simulation codes on to leadership-class computing resources, there is a need to instrument these codes to better understand performance to efficiently utilize these resources. This instrumentation may come from independent third-party tools that generate and store performance metrics or from custom instrumentation tools built directly into the application. The metrics collected are then available for visual analysis, typically in the domain in which there were collected. In this paper, we introduce an approach to visualize and analyze the performance metrics in situ in the context of the machine, application, and communication domains (MAC model) using a single visualization tool. This visualization model provides a holistic view of the application performance in the context of the resources where it is executing.

The need to scale next-generation industrial engineering problems to the largest computational platforms presents unique challenges. This paper focuses on data management related problems faced by the Uintah simulation framework at a production scale of 260K processes. Uintah provides a highly scalable asynchronous many-task runtime system, which in this work is used for the modeling of a 1000 megawatt electric (MWe) ultra-supercritical (USC) coal boiler. At 260K processes, we faced both parallel I/O and visualization related challenges, e.g., the default file-per-process I/O approach of Uintah did not scale on Mira. In this paper we present a simple to implement, restructuring based parallel I/O technique. We impose a restructuring step that alters the distribution of data among processes. The goal is to distribute the dataset such that each process holds a larger chunk of data, which is then written to a file independently. This approach finds a middle ground between two of the most common parallel I/O schemes--file per process I/O and shared file I/O--in terms of both the total number of generated files, and the extent of communication involved during the data aggregation phase. To address scalability issues when visualizing the simulation data, we developed a lightweight renderer using OSPRay, which allows scientists to visualize the data interactively at high quality and make production movies. Finally, this work presents a highly efficient and scalable radiation model based on the sweeping method, which significantly outperforms previous approaches in Uintah, like discrete ordinates. The integrated approach allowed the USC boiler problem to run on 260K CPU cores on Mira.

High performance computing frameworks utilizing CPUs, Nvidia GPUs, and/or Intel Xeon Phis necessitate portable and scalable solutions for application developers. Nvidia GPUs in particular present numerous portability challenges with a different programming model, additional memory hierarchies, and partitioned execution units among streaming multiprocessors. This work presents modifications to the Uintah asynchronous many-task runtime and the Kokkos portability library to enable one single codebase for complex multiphysics applications to run across different architectures. Scalability and performance results are shown on multiple architectures for a globally coupled radiation heat transfer simulation, ranging from a single node to 16384 Titan compute nodes.

The Uintah computational framework is used for the parallel solution of partial differential equations on adaptive mesh refinement grids using modern supercomputers. Uintah is structured with an application layer and a separate runtime system. Uintah is based on a distributed directed acyclic graph (DAG) of computational tasks, with a task scheduler that efficiently schedules and executes these tasks on both CPU cores and on-node accelerators. The runtime system identifies task dependencies, creates a task graph prior to the execution of these tasks, automatically generates MPI message tags, and automatically performs halo transfers for simulation variables. Automating halo transfers in a heterogeneous environment poses significant challenges when tasks compute within a few milliseconds, as runtime overhead affects wall time execution, or when simulation variables require large halos spanning most or all of the computational domain, as task dependencies become expensive to process. These challenges are magnified at production scale when application developers require each compute node perform thousands of different halo transfers among thousands simulation variables. The principal contribution of this work is to (1) identify and address inefficiencies that arise when mapping tasks onto the GPU in the presence of automated halo transfers, (2) implement new schemes to reduce runtime system overhead, (3) minimize application developer involvement with the runtime, and (4) show overhead reduction results from these improvements.

Data analysis and visualization are an essential part of the scientific discovery process. As HPC simulations have grown, I/O has become a bottleneck, which has required scientists to turn to in situ tools for simulation data exploration. Incorporating additional data, such as runtime performance data, into the analysis or I/O phases of a workflow is routinely avoided for fear of excaberting performance issues. The paper presents how the Uintah Framework, a suite of HPC libraries and applications for simulating complex chemical and physical reactions, was coupled with VisIt, an interactive analysis and visualization toolkit, to allow scientists to perform parallel in situ visualization of simulation and runtime performance data. An additional benefit of the coupling made it possible to create a "simulation dashboard" that allowed for in situ computational steering and visual debugging.

The Sunway TaihuLight is the world's fastest supercomputer at the present time with a low power consumption per flop and a unique set of architectural features. Applications performance depends heavily on being able to adapt codes to make best use of these features. Porting large codes to novel architectures such as Sunway is both time-consuming and expensive, as modifications throughout the code may be needed. One alternative to conventional porting is to consider an approach based upon Asynchronous Many Task (AMT) Runtimes such as the Uintah framework considered here. Uintah structures the problem as a series of tasks that are executed by the runtime via a task scheduler. The central challenge in porting a large AMT runtime like Uintah is thus to consider how to devise an appropriate scheduler and how to write tasks to take advantage of a particular architecture. It will be shown how an asynchronous Sunway-specific scheduler, based upon MPI and athreads, may be written and how individual taskcode for a typical but model structured-grid fluid-flow problem needs to be refactored. Preliminary experiments show that it is possible to obtain a strong-scaling efficiency ranging from 31.7% to 96.1% for different problem sizes with full optimizations. The asynchronous scheduler developed here improves the overall performance over a synchronous alternative by at most 22.8%, and the fluid-flow simulation reaches 1.17% the theoretical peak of the running nodes. Conclusions are drawn for the porting of full-scale Uintah applications.

The University of Utah's Carbon Capture Multidisciplinary Simulation Center (CCMSC) is using the Uintah Computational Framework to predict performance of a 1000 MWe ultra-supercritical clean coal boiler. The center aims to utilize the Intel Xeon Phi-based DOE systems, Theta and Aurora, through the Aurora Early Science Program by using the Kokkos C++ library to enable node-level performance portability. This paper describes infrastructure advancements and portability improvements made possible by our integration of Kokkos within Uintah. Scalability results are presented that compare serial and data parallel task execution models for a challenging radiative heat transfer calculation, central to the center's predictive boiler simulations. These results demonstrate both good strong-scaling characteristics to 256 Knights Landing (KNL) processors on the NSF Stampede system, and show the KNL-based calculation to compete with prior GPU-based results for the same calculation.