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NAS Parallel Benchmarks with CUDA and beyond

https://doi.org/10.1002/spe.3056
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cpe.1860openalex-snapshot
Productivity of GPUs under different programming paradigms
inferredcites →https://doi.org/10.1002/cpe.3648openalex-snapshot
Compiler transformation of nested loops for general purpose GPUs
inferredcites →https://doi.org/10.1007/978-3-642-30961-8_12openalex-snapshot
Using Compiler Directives for Accelerating CFD Applications on GPUs
inferredcites →https://doi.org/10.1007/978-3-642-36036-7openalex-snapshot
Languages and compilers for parallel computing : 24th international workshop, LCPC 2011, Fort Collins, CO, USA, September 8-10, 2011 : revised selected papers
inferredcites →https://doi.org/10.1016/j.future.2021.07.021openalex-snapshot
The NAS Parallel Benchmarks for evaluating C++ parallel programming frameworks on shared-memory architectures
inferredcites →https://doi.org/10.1109/icetc.2010.5529656openalex-snapshot
Efficient Embarrassingly Parallel on Graphics Processor Unit
inferredcites →https://doi.org/10.1109/iiswc.2011.6114174openalex-snapshot
Performance characterization of the NAS Parallel Benchmarks in OpenCL
inferredcites →https://doi.org/10.1109/iiswc47752.2019.9041954openalex-snapshot
SNU-NPB 2019: Parallelizing and Optimizing NPB in OpenCL and CUDA for Modern GPUs
inferredcites →https://doi.org/10.1109/pdp50117.2020.00009openalex-snapshot
Efficient NAS Parallel Benchmark Kernels with CUDA
inferredcites →https://doi.org/10.1145/2808091.2808092openalex-snapshot
Meta-programming and auto-tuning in the search for high performance GPU code
inferredcites →https://doi.org/10.12694/scpe.v11i3.654openalex-snapshot
Programming Massively Parallel Processors. A Hands-on Approach

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Special issue on new trends in high‐performance computing: Software systems and applications
inferred← citeshttps://doi.org/10.1007/s10766-022-00746-1openalex-snapshot
Assessing Application Efficiency and Performance Portability in Single-Source Programming for Heterogeneous Parallel Systems
inferred← citeshttps://doi.org/10.1007/s11227-022-04932-3openalex-snapshot
NAS Parallel Benchmarks with Python: a performance and programming effort analysis focusing on GPUs
inferred← citeshttps://doi.org/10.1016/j.cma.2024.116974openalex-snapshot
Solving the discretised multiphase flow equations with interface capturing on structured grids using machine learning libraries
inferred← citeshttps://doi.org/10.1016/j.cola.2025.101369openalex-snapshot
A C++ annotation-based domain-specific language for expressing stream and data parallelism supporting CPU and GPU
inferred← citeshttps://doi.org/10.1016/j.csi.2024.103922openalex-snapshot
GSParLib: A multi-level programming interface unifying OpenCL and CUDA for expressing stream and data parallelism
inferred← citeshttps://doi.org/10.1016/j.enbuild.2023.113600openalex-snapshot
Acceleration of state–space method based on parallelization for enhancing building thermal process simulation efficiency
inferred← citeshttps://doi.org/10.1016/j.future.2024.07.003openalex-snapshot
Benchmarking parallel programming for single-board computers
inferred← citeshttps://doi.org/10.1016/j.jpdc.2023.04.009openalex-snapshot
Supporting efficient overlapping of host-device operations for heterogeneous programming with CtrlEvents
inferred← citeshttps://doi.org/10.1109/ccgrid57682.2023.00020openalex-snapshot
An experimental comparison of software-based power meters: focus on CPU and GPU
inferred← citeshttps://doi.org/10.1109/pdp55904.2022.00013openalex-snapshot
NAS Parallel Benchmark Kernels with Python: A performance and programming effort analysis focusing on GPUs
inferred← citeshttps://doi.org/10.1109/pdp66500.2025.00030openalex-snapshot
NPB-PSTL: C++ STL Algorithms with Parallel Execution Policies in NAS Parallel Benchmarks
inferred← citeshttps://doi.org/10.1109/pdp66500.2025.00033openalex-snapshot
Programming and Mapping for Mixed Heterogeneous Devices: The Case of Optical Flow
inferred← citeshttps://doi.org/10.1109/sbac-pad66369.2025.00027openalex-snapshot
Performance, Portability, and Productivity of HIP on GPUs with NAS Parallel Benchmarks
inferred← citeshttps://doi.org/10.1109/tpds.2025.3555718openalex-snapshot
Taming Offload Overheads in a Massively Parallel Open-Source RISC-V MPSoC: Analysis and Optimization
inferred← citeshttps://doi.org/10.1145/3665251openalex-snapshot
Indigo3: A Parallel Graph Analytics Benchmark Suite for Exploring Implementation Styles and Common Bugs
inferred← citeshttps://doi.org/10.1145/3720555.3721989openalex-snapshot
From OpenACC to OpenMP5 GPU Offloading: Performance Evaluation on NAS Parallel Benchmarks
inferred← citeshttps://doi.org/10.1145/3774748.3787654openalex-snapshot
Trustworthiness of Continuous Integration Practices
inferred← citeshttps://doi.org/10.1145/3797143openalex-snapshot
Understanding Performance Problems in CUDA Programs
inferred← citeshttps://doi.org/10.23919/date58400.2024.10546670openalex-snapshot
Optimizing Offload Performance in Heterogeneous MPSoCs
inferred← citeshttps://doi.org/10.3390/electronics12224555openalex-snapshot
An Evaluation of Directive-Based Parallelization on the GPU Using a Parboil Benchmark
inferred← citeshttps://doi.org/10.3390/math11041055openalex-snapshot
Programming for High-Performance Computing on Edge Accelerators
inferred← citeshttps://doi.org/10.3390/math13203305openalex-snapshot
Evaluation of GPU-Accelerated Edge Platforms for Stochastic Simulations: Performance and Energy Efficiency Analysis
inferred← citeshttps://doi.org/10.36930/40330213openalex-snapshot
Модель та метод синтезу нейронного елемента паралельно-потокового типу
inferred← citeshttps://doi.org/10.5753/eradrs.2022.19188openalex-snapshot
Provendo melhorias na GSParLib
inferred← citeshttps://doi.org/10.5753/eradrs.2022.19191openalex-snapshot
Encontrando a Configuração de Threads por Bloco para os Kernels NPB-CUDA com Q-Learning
inferred← citeshttps://doi.org/10.5753/sscad.2024.244712openalex-snapshot
Análise de Desempenho e Consumo Energético de Aplicações Recursivas em Ambientes OpenMP, CUDA e CUDA DP
inferred← citeshttps://doi.org/10.5753/sscad_estendido.2024.244350openalex-snapshot
Avaliação do Desempenho de Aplicações CUDA em um Ambiente com a Tecnologia Multi-Instance GPU
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