Optimizing Memory Bandwidth Efficiency with User-Preferred Kernel Merge

Jumah, Nabeeh; Kunkel, Julian

doi:10.1007/978-3-030-48340-1_6

Cited by 1 publication

(2 citation statements)

References 17 publications

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“…First, we generated code versions with and without blocking for the Broadwell processor and the P100 GPU. An excerpt of results presented for different size of grids is shown in [29] and in Fig. 5a.…”

Section: Evaluating Blockingmentioning

confidence: 99%

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AIMES: Advanced Computation and I/O Methods for Earth-System Simulations

Kunkel

Jumah

Novikova

et al. 2020

Lecture Notes in Computational Science and Engineering

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Dealing with extreme scale earth system models is challenging from the computer science perspective, as the required computing power and storage capacity are steadily increasing. Scientists perform runs with growing resolution or aggregate results from many similar smaller-scale runs with slightly different initial conditions (the so-called ensemble runs). In the fifth Coupled Model Intercomparison Project (CMIP5), the produced datasets require more than three Petabytes of storage and the compute and storage requirements are increasing significantly for CMIP6. Climate scientists across the globe are developing next-generation models based on improved numerical formulation leading to grids that are discretized in alternative forms such as an icosahedral (geodesic) grid. The developers of these models face similar problems in scaling, maintaining and optimizing code. Performance portability and the maintainability of code are key concerns of scientists as, compared to industry projects, model code is continuously revised and extended to incorporate further levels of detail. This leads to a rapidly growing code base that is rarely refactored. However, code modernization is important to maintain productivity

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Section: Evaluating Blockingmentioning

confidence: 99%

“…To explore the benefit of kernel merging, experiments were done using the shallow water equation solver code on the NEC test system (for vector engines) and Piz Daint (for GPUs and CPUs) [29]. The performance of regular and merged kernels are shown in Table 3.…”

Section: Evaluating Inter-kernel Optimizationmentioning

confidence: 99%