Using Advanced Vector Extensions AVX-512 for MPI Reductions

Author:

Zhong Dong1,Cao Qinglei1,Bosilca George1,Dongarra Jack1

Affiliation:

1. The University of Tennessee, USA

Publisher

ACM

Reference41 articles.

1. [n. d.]. A benchmark framework for Tensorflow. https://github.com/tensorflow/benchmarks [n. d.]. A benchmark framework for Tensorflow. https://github.com/tensorflow/benchmarks

2. ARM. 2018. Arm Architecture Reference Manual Armv8 for Armv8-A architecture profile. https://developer.arm.com/docs/ddi0487/latest/arm-architecture-reference-manual-armv8-for-armv8-a-architecture-profile ARM. 2018. Arm Architecture Reference Manual Armv8 for Armv8-A architecture profile. https://developer.arm.com/docs/ddi0487/latest/arm-architecture-reference-manual-armv8-for-armv8-a-architecture-profile

3. Stencil codes on a vector length agnostic architecture

4. Using Arm’s scalable vector extension on stencil codes;Armejach Adrià;The Journal of Supercomputing,2019

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2. Analyzing Data Reordering of a combined MPI and AVX execution of a Jacobi Method;2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP);2023-03

3. MARs: Memory Access Rearrangements in Open MPI;2022 IEEE/ACM Workshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous Systems (ScalAH);2022-11

4. Accelerating distributed deep neural network training with pipelined MPI allreduce;Cluster Computing;2021-08-07

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