GPU Algorithms

Scalable Methods for Multivariate Normal Probability Estimation with Applications in Confidence Region Detection, Transport Phenomena, and Parallel Computing Using RCOMPSs

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B2 R5209

This thesis addresses computing high-dimensional multivariate normal (MVN) probabilities in environmental and geospatial applications by combining high-performance computing, numerical approximation, and transport-based covariance modeling to make several important spatial procedures usable at larger scales.

Accelerating Branch-and-Bound Graph Algorithms with GPUs

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B9 L2 R2325

This talk presents multiple techniques that we have developed to load balance the search tree traversal on GPUs and mitigate the strain on memory capacity and bandwidth.

KAUST master’s student Osama Dabbousi awarded Rhodes Scholarship

1 min read ·
KAUST master’s student Osama Dabbousi earns the prestigious Rhodes Scholarship, reflecting KAUST’s growing role in developing national research talent.

Distributed multi-GPU Algorithms for Hierarchical Matrices

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B9 L2 R2322

In this talk, we show that, besides their optimal O(N) algorithmic complexity, hierarchical matrix operations also benefit from parallel scalability on distributed machines with extremely large core counts. In particular, we describe high-performance, distributed-memory, GPU-accelerated algorithms for matrix-vector multiplication and other operations on hierarchical matrices in the H^2 format.