Ansor : Generating High-Performance Tensor Programs for Deep Learning

High-performance tensor programs are crucial to guarantee efficient execution of deep neural networks. However, obtaining performant tensor programs for different operators on various hardware platforms is notoriously challenging. Currently, deep learning systems rely on vendor-provided kernel libraries or various search strategies to get performant tensor programs. These approaches either require significant engineering effort to develop platform-specific optimization code or fall short of finding high-performance programs due to restricted search space and ineffective exploration strategy. We present Ansor, a tensor program generation framework for deep learning applications. Compared with existing search strategies, Ansor explores many more optimization combinations by sampling programs from a hierarchical representation of the search space. Ansor then fine-tunes the sampled programs with evolutionary search and a learned cost model to identify the best programs. Ansor can find high-performance programs that are outside the search space of existing state-of-the-art approaches. In addition, Ansor utilizes a task scheduler to simultaneously optimize multiple subgraphs in deep neural networks. We show that Ansor improves the execution performance of deep neural networks relative to the state-of-the-art on the Intel CPU, ARM CPU, and NVIDIA GPU by up to $3.8\times$, $2.6\times$, and $1.7\times$, respectively.Comment: Published in OSDI 202

Endoplasmic reticulum stress mediates nickel chloride-induced epithelial‑mesenchymal transition and migration of human lung cancer A549 cells through Smad2/3 and p38 MAPK activation

Background: The endoplasmic reticulum (ER) is a cellular membrane-bound organelle whereby proteins are synthesized, folded and glycosylated. Due to intrinsic (e.g., genetic) and extrinsic (e.g., environmental stressors) perturbations, ER proteostasis can be deregulated within cells which triggers unfolded protein response (UPR) as an adaptive stress response that may impact the migration and invasion properties of cancer cells. However, the mechanisms underlying the nickel compounds on lung cancer cell migration and invasion remain uncertain. Objective: We aimed to study whether Nickel chloride (NiCl2) induces ER stress in lung cancer cells, and whether ER stress is involved in modulating epithelial-mesenchymal transition (EMT) and migration by Smads and MAPKs pathways activation following NiCl2 treatment. Methods: A549 cells were treated with NiCl2 to determine the cell viability using MTT assay. The wound healing assay was used to evaluate cell migration ability. ER ultrastructure was observed by transmission electron microscopy. Western blotting assay was performed to evaluate the protein levels of BIP, PERK, IRE-1α, XBP-1 s, and ATF6 for ER stress and UPR, E-cadherin and Vimentin for EMT, p-Smad2/3, p-ERK, p-JNK, and p-P38 for activation of Smads and MAPKs signaling pathways. Results: The expression levels of BIP, PERK, IRE-1α, XBP-1 s, and ATF6 were significantly increased following treatment with NiCl2 in time- and dose-effect relationship. The ER stress inhibitor 4-PBA downregulated the expression levels of the above five proteins, and reversed the decrease in E-cadherin protein level and the increase in vimentin protein expression and cell migration abilities caused by NiCl2. Furthermore, 4-PBA significantly reduced nickel chloride-induced Smad2/3 and p38 MAPK pathway activation, while not affected ERK and JNK MAPK pathways. Conclusion: NiCl2 triggers ER stress and UPR in A549 cells. Moreover, 4-PBA alleviates NiCl2-induced EMT and migration ability of A549 cells possibly through the Smad2/3 and p38 MAPK pathways activation, rather than ERK and JNK MAPK pathways