OpenACC 2.5 Validation Testsuite Targeting Multiple Architectures

Friedline, Kyle; Chandrasekaran, Sunita; Lopez, M. Graham; Hernández, Óscar

doi:10.1007/978-3-319-67630-2_39

Cited by 13 publications

(7 citation statements)

References 10 publications

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“…Despite the importance of OpenACC, the only non-academic open-source OpenACC compiler cited by the OpenACC website is GCC [74]. However, GCC has lagged behind commercial compilers, such as NVIDIA's, in providing production-quality support for the latest OpenACC specifications [75]. Moreover, GCC is known within the compiler community to be challenging to extend and, especially within the DOE, is losing favor to Clang and LLVM for new compiler research and development efforts.…”

Section: Wbs 23210 Proteas-tune -Clacc: Openacc In Clang and Llvmmentioning

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: Wbs 23210 Proteas-tune -Clacc: Openacc In Clang and Llvmmentioning

confidence: 99%

ECP Software Technology Capability Assessment Report V3.0

Heroux

McInnes

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Throughout Clacc development, we are continuously integrating the latest upstream Clang and LLVM changes, and we are running and extending the Clang and LLVM test suites to detect regressions and incompatibilities. We are also investigating OpenACC benchmarks [71] and validation test suites [70] to ensure correct OpenACC behavior and good performance. Overview We are developing tools and an application development workflow that separates a high-level C/C++/FORTRAN implementation from an architecture-specific implementation (OpenMP, CUDA, etc.…”

Section: Key Challengesmentioning

confidence: 99%

ECP Software Technology Capability Assessment Report

Heroux

McInnes

Thakur

et al. 2020

View full text Add to dashboard Cite

The Exascale Computing Project (ECP) Software Technology (ST) Focus Area is responsible for developing critical software capabilities that will enable successful execution of ECP applications, and for 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 (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 six to 12 months). Highlights from this version of the report are presented here.What is new in CAR V2.0: CAR V2.0 contains the following updates relative to CAR V1.5.• We introduce the FY20-23 project structure. ECP ST now consists of 6 (up from 5) level-3 (L3) technical areas, introducing the NNSA ST L3 area, which brings into one L3 the ECP open source development efforts at NNSA labs that are of particular importance to the rest of ECP. The number of ECP ST level-4 (L4) subprojects has been reduced from 55 to 33. The strategic aggregation of projects into fewer and larger units enables us to better manage L4 subprojects consistently as a portfolio. See Section 1.2.• We describe new and enhanced project management processes and resources including our iterative planning process, new KPP-3 capability integration process, a product dictionary and dependency management database. These new project features and related dashboards enable more insight and better information to effectively manage efforts across ECP. See Section 2.• The two-page summaries of each ECP L4 projects have been updated to reflect recent progress and next steps. See Section 4.• The Extreme-scale Scientific Software Stack (E4S) is further described. The third release, which is also the first major public release Version 1.0, was November 18, 2019. E4S is the primary integration and delivery vehicle for ECP ST capabilities. See Section 2.1.1.• The ECP ST SDK effort has further refined its groupings. See Section 2.1.2.The Exascale Computing Project Software Technology (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 70 software products (Section 2.1.3) in six technical areas (Table 1). Since the publishing of CAR V1.5, we have introduced a 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, we are working on performance portability layers (Kokkos and RAJA) and participating in OpenMP and OpenACC so...

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“…Another study has been published in [29], also for evaluating CAPS, PGI, and CRAY compilers. Finally, OpenACC 2.5 was evaluated in [30] for validating and verifying the new feature of OpenACC compilers' implementation Despite efforts made to create and propose software testing tools for parallel application, there is still a lot to be done primarily for GPU-related programming models and for dualand tri-level programming models for heterogeneous systems. Heterogeneous systems can be hybrid CPUs/GPUs architectures or different architectures of GPUs.…”

Section: Related Workmentioning

confidence: 99%

A Parallel Hybrid-Testing Tool Architecture for a Dual-Programming Model

Alghamdi¹,

Elbouraey²

2019

IJACSA

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High-Performance Computing (HPC) recently has become important in several sectors, including the scientific and manufacturing fields. The continuous growth in building more powerful super machines has become noticeable, and the Exascale supercomputer will be feasible in the next few years. As a result, building massively parallel systems becomes even more important to keep up with the upcoming Exascale-related technologies. For building such systems, a combination of programming models is needed to increase the system's parallelism, especially dual and tri-level programming models to increase parallelism in heterogeneous systems that include CPUs and GPUs. There are several combinations of the dualprogramming model; one of them is MPI+ OpenACC. This combination has several features that increase the application's parallelism concerning heterogeneous architecture and support different platforms with more performance, productivity, and programmability. However, building systems with different programming models are error-prone and difficult and are also hard to test. Also, testing parallel applications is already a difficult task because parallel errors are hard to detect due to the non-determined behavior of the parallel application. Integrating more than one programming model inside the same application makes even it more difficult to test because this integration could come with a new type of errors. Our main contribution is to identify and categorize OpenACC run-time errors and determine their causes with a brief explanation for the first time in research. Also, we proposed a solution for detecting run-time errors in application implemented in the dual-programming model. Our solution based on using hybrid testing techniques to discover real and potential run-time errors. Finally, to the best of our knowledge, there is no parallel testing tool built to test applications programmed by using the dual-programming model MPI + OpenACC or any tri-level programming model or even the OpenACC programming model to detect their run-time errors. Also, OpenACC errors have not been classified or identified before.

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OpenACC 2.5 Validation Testsuite Targeting Multiple Architectures

Cited by 13 publications

References 10 publications

ECP Software Technology Capability Assessment Report V3.0

ECP Software Technology Capability Assessment Report V3.0

ECP Software Technology Capability Assessment Report

A Parallel Hybrid-Testing Tool Architecture for a Dual-Programming Model

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