Al Farhan, Mohammed A.; Kaushik, Dinesh K.; Keyes, David E.(2017-03-13)[Poster]
Shared memory parallelization of PETSc-FUN3D, an unstructured tetrahedral mesh Euler code previously characterized for distributed memory Single Program, Multiple Data (SPMD) for thousands of nodes, is hybridized with shared memory Single Instruction, Multiple Data (SIMD) for hundreds of threads per node. We explore thread-level performance optimizations on state-of-the-art multi- and many-core Intel processors, including the second generation of Xeon Phi, Knights Landing (KNL). We study the performance on the KNL with different configurations of memory and cluster modes, with code optimizations to minimize indirect addressing and enhance the cache locality. The optimizations employed are expected to be of value other unstructured applications as many-core architecture evolves.
Charara, Ali; Keyes, David E.; Ltaief, Hatem(2017-03-13)[Poster]
In several scientific applications, like tensor contractions in deep learning computation or data compression in hierarchical low rank matrix approximation, the bulk of computation typically resides in performing thousands of independent dense linear algebra operations on very small matrix sizes (usually less than 100). Batched dense linear algebra kernels are becoming ubiquitous for such scientific computations. Within a single API call, these kernels are capable of simultaneously launching a large number of similar matrix computations, removing the expensive overhead of multiple API calls while increasing the utilization of the underlying hardware.
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