Kokkos 3: Programming Model Extensions for the Exascale Era
Not Available
97 MATHEMATICS AND COMPUTING↗
SEARCH · Search NASA
Results for “Exascale programming”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Building a Transdisciplinary, Exascale-Capable Workforce for Geospace Science
Key Points: ●First-principles, self-consistent geospace modeling will require at least exascale-level computing capabilities; however, the technical skills necessary to develop such simulation codes are not taught as part of Heliophysics training/PhD programs. ●Developing exascale Heliophysics codes will require transdisciplinary collaborations between physicists, computer scientists, software engineers, data scientists, and applied mathematics. Such teams must be persistent and formed around specific skills, not specific problems. ●We must have stable, positive long-term career outcomes for Heliophysics scientific developers in order to retain knowledge of and promote future innovation in scientific algorithm development and advanced computing techniques. One such way to do this is by funding long-term scientific programming groups, akin to Heliophysics DRIVE Centers. ●We must have a balance between promoting innovation via funding short-term closed-source development and allowing the open-source community to benefit from and build on the newest scientific modeling techniques.
C Bard↗
Program Correctness, Verification and Testing for Exascale (Corvette)
The goal of this project is to provide tools to assess the correctness of parallel programs written using hybrid parallelism. There is a dire lack of both theoretical and engineering know-how in the area of finding bugs in hybrid or large scale parallel programs, which our research aims to change. In the project we have demonstrated novel approaches in several areas: 1. Low overhead automated and precise detection of concurrency bugs at scale. 2. Using low overhead bug detection tools to guide speculative program transformations for performance. 3. Techniques to reduce the concurrency required to reproduce a bug using partial program restart/replay. 4. Techniques to provide reproducible execution of floating point programs. 5. Techniques for tuning the floating point precision used in codes.
97 MATHEMATICS AND COMPUTING↗
A GPU-based compressible combustion solver for applications exhibiting disparate space and time scales
High-speed chemically active flows pose significant computational challenges due to their disparate space and time scales, with stiff chemistry often dominating simulation time. While modern scientific computing programs achieve exascale performance by leveraging graphics processing units (GPUs), existing GPU-based compressible combustion solvers face critical limitations in memory management, load balancing, and handling the highly localized nature of chemical reactions. To this end, we present a high-performance compressible reacting flow solver built on the AMReX framework and optimized for multi-GPU settings. Here, our approach addresses three GPU performance bottlenecks: memory access patterns through column-major storage optimization, computational workload variability via a bulk-sparse integration strategy for chemical kinetics, and multi-GPU load distribution for adaptive mesh refinement applications. The solver adapts existing matrix-based chemical kinetics formulations to multi-grid contexts. Using representative combustion applications, including 2D and 3D detonations and a 3D jet-in-crossflow configuration, we demonstrate 1.4–5× performance improvements over initial implementations on an in-house cluster of NVIDIA H100 GPUs, and near-ideal weak scaling on the Frontier supercomputer (Oak Ridge Leadership Computing Facility) with up to 1024 AMD Instinct MI250X GPUs. Roofline analysis reveals substantial improvements in arithmetic intensity for both convection (∼ 10 ×) and chemistry (∼ 4 ×) routines, confirming efficient utilization of GPU memory bandwidth and computational resources.
42 ENGINEERING↗
Batched Sparse Linear Algebra Phase 2 (Final Report)
The purpose of the work was to help LLNL develop a broadly applicable set of capabilities that focus on batched sparse linear functionality and its software implementation. The enablement occurred in a broader scope of the Exascale Computing Program (ECP) with an eye on the xSDK collection of applications and libraries. These served as the main dissemination targets and in the end benefitted from this project’s outcomes.
97 MATHEMATICS AND COMPUTING↗
Collected Posters for Sandia Intern Program from Sustainable Research Pathways / Exascale Computing
Explore the source record for details and available documents.
Mullen, Coreen Michelle↗
Batched Sparse Linear Algebra Phase 2
The purpose of the work was to help LLNL develop a broadly applicable set of capabilities that focus on batched sparse linear functionality and its software implementation. The enablement occurred in a broader scope of the Exascale Computing Program (ECP) with an eye on the xSDK collection of applications and libraries. These served as the main dissemination targets and in the end benefitted from this project’s outcomes.
97 MATHEMATICS AND COMPUTING↗
Exascale Node-Level Parallel Programming Environments: Overview and Deciding What's Right for You.
Abstract not provided.
Rajamanickam, Sivasankaran↗
Evaluating performance and portability of high-level programming models: Julia, Python/Numba, and Kokkos on exascale nodes
We explore the performance and portability of the high-level programming models: the LLVM-based Julia and Python/Numba, and Kokkos on high-performance computing (HPC) nodes: AMD Epyc CPUs and MI250X graphical processing units (GPUs) on Frontier’s test bed Crusher system and Ampere’s Arm-based CPUs and NVIDIA’s A100 GPUs on the Wombat system at the Oak Ridge Leadership Computing Facilities. We compare the default performance of a hand-rolled dense matrix multiplication algorithm on CPUs against vendor-compiled C/OpenMP implementations, and on each GPU against CUDA and HIP. Rather than focusing on the kernel optimization per-se, we select this naive approach to resemble exploratory work in science and as a lower-bound for performance to isolate the effect of each programming model. Julia and Kokkos perform comparably with C/OpenMP on CPUs, while Julia implementations are competitive with CUDA and HIP on GPUs. Performance gaps are identified on NVIDIA A100 GPUs for Julia’s single precision and Kokkos, and for Python/Numba in all scenarios. We also comment on half-precision support, productivity, performance portability metrics, and platform readiness. We expect to contribute to the understanding and direction for high-level, high-productivity languages in HPC as the first-generation exascale systems are deployed.
Godoy, William↗
Domain Specific Language Support for Exascale
Domain-Specific Languages (DSLs) offer an attractive path to Exascale software since they provide expressive power through appropriate abstractions and enable domain-specific optimizations. But the advantages of a DSL compete with the difficulties of implementing a DSL, even for a narrowly defined domain. The DTEC project addresses how a variety of DSLs can be easily implemented to leverage existing compiler analysis and transformation capabilities within the ROSE open source compiler as part of a research program focusing on Exascale challenges. The OSU contributions to the DTEC project are in the area of code generation from high-level DSL descriptions, as well as verification of the automatically-generated code.
97 MATHEMATICS AND COMPUTING↗
Leveraging Compiler-Based Translation to Evaluate a Diversity of Exascale Platforms
Accelerator-based heterogeneous computing is the de facto standard in current and upcoming exascale machines. These heterogeneous resources empower computational scientists to select a machine or platform well-suited to their domain or applications. However, this diversity of machines also poses challenges related to programming model selection: inconsistent availability of programming models across different exascale systems, lack of performance portability for those programming models that do span several systems, and inconsistent performance between different models on a single platform. We explore these challenges on exascale-similar hardware, including AMD MI100 and NVIDIA A100 GPUs. By extending the sourceto-source compiler OpenARC, we demonstrate the power of automated translation of applications written in a single frontend programming model (OpenACC) into a variety of backend models (OpenMP, OpenCL, CUDA, HIP) that span the upcoming exascale environments. This translation enables us to compare performance within and across devices and to analyze programming model behavior with profiling tools.
Lambert, Jacob↗
Preparing NERSC users for Cori, a Cray XC40 system with Intel many integrated cores
The newest NERSC supercomputer Cori is a Cray XC40 system consisting of 2,388 Intel Xeon Haswell nodes and 9,688 Intel Xeon-Phi “Knights Landing” (KNL) nodes. Compared to the Xeon-based clusters NERSC users are familiar with, optimal performance on Cori requires consideration of KNL mode settings; process, thread, and memory affinity; fine-grain parallelization; vectorization; and use of the high-bandwidth MCDRAM memory. This paper describes our efforts preparing NERSC users for KNL through the NERSC Exascale Science Application Program, Web documentation, and user training. We discuss how we configured the Cori system for usability and productivity, addressing programming concerns, batch system configurations, and default KNL cluster and memory modes. Here, system usage data, job completion analysis, programming and running jobs issues, and a few successful user stories on KNL are presented.
97 MATHEMATICS AND COMPUTING↗
Optimizing fusion PIC code performance at scale on Cori Phase 2
In this paper we present the results of optimizing the performance of the gyrokinetic full-f fusion PIC code XGC1 on the Cori Phase Two Knights Landing system. The code has undergone substantial development to enable the use of vector instructions in its most expensive kernels within the NERSC Exascale Science Applications Program. We study the single-node performance of the code on an absolute scale using the roofline methodology to guide optimization efforts. We have obtained 2x speedups in single node performance due to enabling vectorization and performing memory layout optimizations. On multiple nodes, the code is shown to scale well up to 4000 nodes, near half the size of the machine. We discuss some communication bottlenecks that were identified and resolved during the work.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
XACC - eXtreme-scale Accelerator Programming Framework
Hybrid programming models for beyond-CMOS technologies will prove critical for integrating new computing technologies alongside our existing infrastructure. Unfortunately the software infrastructure required to enable this is lacking or not available. XACC is a programming framework for extreme-scale, post-exascale accelerator architectures that integrates alongside existing conventional applications. It is a pluggable framework for programming languages developed for next-gen computing hardware architectures like quantum and neuromorphic computing. It lets computational scientists efficiently off-load classically intractable work to attached accelerators through user-friendly Kernel definitions. XACC makes post-exascale hybrid programming approachable for domain computational scientists.
McCaskey, AlexanderJ.↗
XACC - eXtreme-scale Accelerator Programming Framework
Hybrid programming models for beyond-CMOS technologies will prove critical for integrating new computing technologies alongside our existing infrastructure. Unfortunately the software infrastructure required to enable this is lacking or not available. XACC is a programming framework for extreme-scale, post-exascale accelerator architectures that integrates alongside existing conventional applications. It is a pluggable framework for programming languages developed for next-gen computing hardware architectures like quantum and neuromorphic computing. It lets computational scientists efficiently off-load classically intractable work to attached accelerators through user-friendly Kernel definitions. XACC makes post-exascale hybrid programming approachable for domain computational scientists.
McCaskey, Alexander J.↗
Hardware Evaluation Outreach: Application development Challenges Now and for the Exascale Era
The intent of this document is to assist the programmer in understanding details of contemporary and Exascale hardware system design and how these designs provide opportunities and place constraints on next-generation simulation software design. We attempt to clarify hardware organization and component details for our most current and Exascale systems to help program developers understand how software needs to change in order to take best advantage of the performance available. Exascale success is specifically defined for ECP as a 50x improvement over baseline in the aggregate "capability volume" on several KPP axes, of which raw floating point performance is only one, but also includes characteristics such as problem size, system memory size, node memory size, power, and efficiency. This multi-axis approach is particularly important to understand in the context of delivered improvements in real applications, since, for instance, the floating point computation may comprise less than 10% of the actual computational work required. Given the Exascale requirements and the constraints these requirements put on the performance expectations of fundamental system components, the programmer will be forced to re-think several application implementation details in order to achieve exaflop performance on these systems. The remainder of this document aims to present more detail on Exascale era system hardware and the specific areas that the programmer should address to extract performance from these systems. We attempt to give the programmer guidance at both a high- and low-level, providing some abstract suggestions on how to refactor codes given the expected system architectures and some low-level recommendations on how to implement these modifications. We also include a section on training resources that are helpful for both programmers that are just beginning to understand code modifications for contemporary and Exascale systems and for those that have done some refactoring and are now trying to extract maximal application performance from these systems.
97 MATHEMATICS AND COMPUTING↗
S&TR Preview: Exascale Computing
Projects funded by Lawrence Livermore’s Laboratory Directed Research and Development Program help build a foundation for exascale computing and potential subsequent scientific breakthroughs.
97 MATHEMATICS AND COMPUTING↗
Large-Scale Computational Fluid Dynamics Simulations of Aerospace Configurations on the Frontier Exascale System
Over the past fifteen years, the high performance computing landscape has undergone a seismic shift in both hardware and software paradigms, which has been necessary to realize a 1000× leap in computational performance while meeting stringent constraints on power consumption. A historical overview of a long-term research effort aimed at addressing these challenges within the context of a commonly-used aerospace computational fluid dynamics (CFD) application is presented. Details of the current implementation as they relate to the new era of exascale-relevant hardware architectures and programming models are described. Two large-scale simulations of aerospace configurations are performed using the entire Frontier exascale system, currently ranked as the most powerful supercomputing system in the world. The effort serves to address a 2024 milestone posed a decade ago by the seminal CFD Vision 2030 Study.
Eric J Nielsen↗