Minimizing development costs for efficient many-core visualization using MCD3

Moreland, Kenneth D.; Maynard, Robert; Pugmire, David; Yenpure, Abhishek; Vacanti, Allison; Larsen, Matthew C.; Childs, Hank

doi:10.1016/j.parco.2021.102834

Cited by 6 publications

(2 citation statements)

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“…The device adapter is constructed from a small but versatile set of data parallel primitives, which can be optimized for each platform [205]. It has been shown that this approach not only simplifies parallel implementations, but also allows them to work well across many platforms [206,207,208] and while still providing performance comparable to less portable solutions [209]. Within the device adapter we are leveraging Kokkos [210] to rapidly port to ECP hardware.…”

Section: Solution Strategymentioning

confidence: 99%

ECP Software Technology Capability Assessment Report V3.0

Heroux

McInnes

et al. 2022

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The Exascale Computing Project (ECP) Software Technology (ST) focus area is responsible for (1) developing critical software capabilities that will enable the successful execution of ECP applications and (2) providing key components of a productive and sustainable exascale computing ecosystem that will position the US Department of Energy (DOE) and the broader high-performance computing (HPC) community with a firm foundation for future extreme-scale computing capabilities.This ECP ST Capability Assessment Report (CAR) provides an overview and assessment of current ECP ST capabilities and activities, giving stakeholders and the broader HPC community information that can be used to assess ECP ST progress and plan their own efforts accordingly. ECP ST leaders commit to updating this document on regular basis (every 6-12 months). Highlights from this version of the report are presented here.This version of the CAR contains the following updates relative to the previous revision.• This report highlights the progress with the Extreme-scale Scientific Software Stack (E4S) efforts.In particular, this report discusses how E4S continues to gain traction as a first-class entity in the HPC ecosystem, enabling new conversations with users, facilities, vendors, other US agencies, and international partners.• The several-page summaries of each ECP Level 4 project were updated to reflect recent progress and next steps (Section 4). Of particular note are the experiences of our teams on early-access systems for Frontier.• The E4S is described further. E4S is now updated via quarterly releases. E4S is the primary integration and delivery vehicle for ECP ST capabilities (Section 2.1.1).• The ECP ST software development kit (SDK) effort further refined its groupings (Section 2.1.2).The ECP ST focus area represents the key bridge between exascale systems and the scientists developing applications that will run on those platforms. ECP ST efforts contribute to approximately 70 software products (Section 2.1.3) in six technical areas (Table 1). Since publishing the previous revision of the CAR, the team has continued to evolve the product dictionary of official product names, which enables more rigorous mapping of ECP ST deliverables to stakeholders (Section 2.1.4).Programming Models & Runtimes: In addition to developing key enhancements to MPI and OpenMP for scalable systems with accelerated node architectures, the team is working on performance portability layers (Kokkos and RAJA) and participating in OpenMP and OpenACC software design and development that will enable applications to write much of their source code without needing to provide vendor-specific implementations for each exascale system. One legacy of ECP ST efforts is anticipated to be a software stack that supports Intel and AMD accelerators in addition to NVIDIA's accelerators (Section 4.1).Development Tools: The team is enhancing existing widely used compilers (e.g., LLVM) and performance tools for next-generation platforms. Compilers are critical for heterogeneous archi...

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Section: Solution Strategymentioning

confidence: 99%

ECP Software Technology Capability Assessment Report V3.0

Heroux

McInnes

et al. 2022

View full text Add to dashboard Cite

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“…The introduction of general GPU processors into HPC systems was a wake up call to the scientific visualization community, which responded with several research projects to address visualization computations on these new accelerators [4]. Today, the VTK-m project [3] provides a software framework for developing accelerated visualization algorithms as well as a collection of written algorithms. With the basics of GPU computation well in hand, it is time to focus our new research activities to other processor hardware features.…”

Section: Timelinessmentioning

confidence: 99%