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Accelerating Unstructured-Grid CFD Algorithms on NVIDIA and AMD GPUs
Event Type
Big Data
Data Analytics
Memory Systems
Numerical Algorithms
Registration Categories
TimeMonday, 15 November 202111:30am - 12pm CST
DescriptionComputational performance of the FUN3D unstructured-grid computational fluid dynamics application on GPUs is highly dependent on the efficiency of floating-point atomic updates needed to support the irregular cell-, edge-, and node-based data access patterns in massively parallel GPU environments. We examine several optimization methods to improve GPU throughput on kernels that are dominated by atomic updates on NVIDIA V100/A100 and AMD MI100 GPUs. Optimization on the AMD MI100 GPU was of primary interest since similar hardware will be used in the upcoming Frontier supercomputer. Techniques combining register shuffling and on-chip shared memory were used to transpose and/or aggregate results amongst collaborating threads before atomically updating global memory. These techniques, along with algorithmic optimizations to reduce the update frequency, reduced the run-time of select kernels on the MI100 GPU by a factor of between 2.5 and 6.0 over atomically updating global memory directly.
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