Deep neural network (DNN) applications are typically represented by tensor programs. To boost the performance of DNN computations, existing works adopt fully equivalent transformations for tensor program optimization by guaranteeing the equivalence on each element of tensors. However, as there are thousands of elements in a tensor, such optimization misses the opportunities that allow the in-equivalence of minority elements. In this work, we proposePet, the first work that introduces partially equivalent transformations to optimize tensor programs. To maintain the functional equivalence of tensor programs,Petautomatically finds and corrects the in-equivalent positions by leveraging the multi-linearity of DNN computations.Petfurther uses a mutation manager to improve search efficiency. Evaluation results show thatPetcan achieve up to 1.98$\times$and 2.20$\times$speedups on NVIDIA Tesla A100 and V100 respectively compared with existing DNN frameworks by introducing new optimization opportunities of partially equivalent transformations.

Srivastava et al. propose a parallel framework to optimize Bayesian network learning in the SC20 article entitled “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery”. They parallelize all the phases in network constructing algorithms to achieve high performance and scalability. In this article, we reproduce the strong scaling and weak scaling experiments in that SC article. We conduct experiments on a 4-node cluster with Intel CPUs provided by the SCC committee. We further analyze the results of communication overhead. Our results show that the proposed method in that SC article scales well on the provided cluster, in accordance with the SC article.Author: Please confirm or add details for any funding or financial support for the research of this article. ?>

Quantum computing has shown its strong potential in solving certain important problems. Due to the intrinsic limitations of current real quantum computers, quantum circuit simulation still plays an important role in both research and development of quantum computing. GPU-based quantum circuit simulation has been explored due to GPU's high computation capability. Despite previous efforts, existing quantum circuit simulation systems usually rely on a single method to improve poor data locality caused by complex quantum entanglement. However, we observe that existing simulation methods show significantly different performance for different circuit patterns. The optimal performance cannot be obtained only with any single method.