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Hybrid MPI/Open MP/GPU Parallelization of XGC1 Fusion Simulation Code? SC13, Denver, Colorado, November 17-22, 2013 Extended Poster Abstract Eduardo F. D Acevedo Jinking Lang Patrick H. Morley Oak

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01

Start by identifying the specific parallelization framework you want to use, such as MPI (Message Passing Interface) for inter-node communication, OpenMP for shared memory parallelism, and GPU acceleration for offloading computational tasks to the GPU.

02

Design your algorithm or program with parallelization in mind, keeping in mind the characteristics of your problem and the available hardware resources.

03

Determine which parts of your code can benefit from parallelization and modify them accordingly. This may involve restructuring your code to support distributed computing, adding parallel directives for OpenMP, or offloading computations to the GPU using CUDA or other GPU programming libraries.

04

Test your code on a small scale first to ensure its correctness and measure its performance. Use profiling tools to identify any bottlenecks and optimize your parallelization strategy if necessary.

05

Scale up your code to larger problem sizes or more compute resources and monitor its performance. Continue optimizing and fine-tuning your parallelization approach as needed.

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Researchers or scientists working on computationally intensive problems that require parallelization to achieve faster results or handle larger datasets.

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Users running simulations or data analysis tasks that can benefit from leveraging the capabilities of multiple parallelization frameworks, such as distributed MPI for inter-node communication, shared memory parallelism with OpenMP, and GPU acceleration.

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Hybrid mpiopenmpgpu parallelization is a method of parallelizing code using a combination of MPI for inter-node communication, OpenMP for intra-node parallelization, and GPU acceleration for certain tasks.

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Information related to the parallelization strategy, data distribution, communication patterns, and GPU usage must be reported on hybrid mpiopenmpgpu parallelization.

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