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GPU-STREAM v2.0: Benchmarking the Achievable Memory Bandwidth of Many-Core Processors Across Diverse Parallel Programming Models.

, , , and . ISC Workshops, volume 9945 of Lecture Notes in Computer Science, page 489-507. (2016)

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Many-Core Acceleration of a Discrete Ordinates Transport Mini-App at Extreme Scale., , and . ISC, volume 9697 of Lecture Notes in Computer Science, page 429-448. Springer, (2016)Exploiting Hardware-Accelerated Ray Tracing for Monte Carlo Particle Transport with OpenMC., and . PMBS@SC, page 19-29. IEEE, (2019)On the Performance Portability of Structured Grid Codes on Many-Core Computer Architectures., , , and . ISC, volume 8488 of Lecture Notes in Computer Science, page 53-75. Springer, (2014)Benchmarking the NVIDIA V100 GPU and Tensor Cores., , and . Euro-Par Workshops, volume 11339 of Lecture Notes in Computer Science, page 444-455. Springer, (2018)An Initial Evaluation of Arm's Scalable Matrix Extension., and . PMBS@SC, page 135-140. IEEE, (2022)Benchmarking and Extending SYCL Hierarchical Parallelism., , , and . HiPar@SC, page 10-19. IEEE, (2021)Improving Auto-Tuning Convergence Times with Dynamically Generated Predictive Performance Models., and . MCSoC, page 211-218. IEEE Computer Society, (2015)Multi-precision convolutional neural networks on heterogeneous hardware., , , and . DATE, page 419-424. IEEE, (2018)Analyzing and improving performance portability of OpenCL applications via auto-tuning., and . IWOCL, page 14:1-14:4. ACM, (2017)Evaluating the performance of HPC-style SYCL applications., and . IWOCL, page 12:1-12:11. ACM, (2020)