CDMPP: A Device-Model Agnostic Framework for Latency Prediction of Tensor Programs

Deep Neural Networks (DNNs) have shown excellent performance in a wide range of machine learning applications. Knowing the latency of running a DNN model or tensor program on a specific device is useful in various tasks, such as DNN graph- or tensor-level optimization and device selection. Considering the large space of DNN models and devices that impede direct profiling of all combinations, recent efforts focus on building a predictor to model the performance of DNN models on different devices. However, none of the existing attempts have achieved a cost model that can accurately predict the performance of various tensor programs while supporting both training and inference accelerators. We propose CDMPP, an efficient tensor program latency prediction framework for both cross-model and cross-device prediction. We design an informative but efficient representation of tensor programs, called compact ASTs, and a pre-order-based positional encoding method, to capture the internal structure of tensor programs. We develop a domain-adaption-inspired method to learn domain-invariant representations and devise a KMeans-based sampling algorithm, for the predictor to learn from different domains (i.e., different DNN operators and devices). Our extensive experiments on a diverse range of DNN models and devices demonstrate that CDMPP significantly outperforms state-of-the-art baselines with 14.03% and 10.85% prediction error for cross-model and cross-device prediction, respectively, and one order of magnitude higher training efficiency. The implementation and the expanded dataset are available at https://github.com/joapolarbear/cdmpp.Comment: Accepted by EuroSys 202

The Bayes factor and its implementation in JASP: A practical primer

Statistical inference plays a critical role in modern scientific research, however, the dominant method for statistical inference in science, null hypothesis significance testing (NHST), is often misunderstood and misused, which leads to unreproducible findings. To address this issue, researchers propose to adopt the Bayes factor as an alternative to NHST. The Bayes factor is a principled Bayesian tool for model selection and hypothesis testing, and can be interpreted as the strength for both the null hypothesis H0 and the alternative hypothesis H1 based on the current data. Compared to NHST, the Bayes factor has the following advantages: it quantifies the evidence that the data provide for both the H0 and the H1, it is not “violently biased” against H0, it allows one to monitor the evidence as the data accumulate, and it does not depend on sampling plans. Importantly, the recently developed open software JASP makes the calculation of Bayes factor accessible for most researchers in psychology, as we demonstrated for the t-test. Given these advantages, adopting the Bayes factor will improve psychological researchers’ statistical inferences. Nevertheless, to make the analysis more reproducible, researchers should keep their data analysis transparent and open

Arteriovenous fistulas in the craniocervical junction region: With vs. without spinal arterial feeders

ObjectiveArteriovenous fistulas (AVFs) in the craniocervical junction (CCJ) region are a rare occurrence with special clinical manifestations. This study retrospectively reviewed patients with CCJ AVFs treated at our neurosurgical center, aiming to enhance the understanding of CCJ AVFs.MethodsA total of 113 patients with CCJ AVFs treated at our neurosurgical center between January 2013 and December 2020 were enrolled. They were grouped as patients with CCJ AVFs with spinal arterial feeders (n = 20) and patients with CCJ AVF without spinal arterial feeders (n = 93). Clinical presentation, angiographic characteristics, intraoperative findings, and treatment outcomes were analyzed.ResultsThe patients’ median age was 55 years (IQR 47.5–62 years). The proportion of males in the group without spinal arterial feeders was significantly higher (p = 0.001). Subarachnoid hemorrhage (SAH) was the most common clinical presentation, especially in the group with spinal arterial feeders (p < 0.001). There were significant differences in AVF type, fistula location, and direction of the venous drainage between the two groups (p < 0.001). Intervention embolization combined with microsurgery was more common in treating AVFs with spinal arterial feeders (p = 0.006). Spinal arterial feeders did not affect the outcome (p = 0.275).ConclusionsSAH was the most common presentation of CCJ AVFs in this study. Microsurgery and interventional embolization were optional treatment strategies. The angioarchitecture of CCJ AVFs was essential for selecting treatment strategies

Dual hydrophobic modifications toward anion exchange membranes with both high ion conductivity and excellent dimensional stability

Abstract(#br)Anion exchange membrane (AEMs) as a kind of important functional material are widely used in many fields including fuel cell, electrodialysis and water treatment. However, synthetic AEMs generally suffer a pernicious trade-off: high ion-conductive AEMs lack dimensional stability and vice versa. Herein we demonstrate a versatile strategy to prepare the AEMs with both high ion conductivity and excellent dimensional stability ( i.e. , low swelling ratio) via hydrophobic crosslinking and introducing hydrophobic chains. The hydrophobic length of crosslinkers has great influence on construction of highly efficient ion channels in the AEMs. Amazingly, the hydrophilic poly (phenylene oxide) (PPO) AEM crosslinked by 1,8-diaminooctane has the highest hydroxide conductivity that is further improved to 157.2 mS cm −1 (10% increases) with a low swelling ratio of 12.9% at 80 °C by introducing hydrophobic PPO backbone. This AEM not only overcomes the trade-off between the ion conductivity and the dimensional stability of crosslinked AEMs, but also breaks the upper bound between the ion conductivity and the water uptake. The newly developed strategy of hydrophobic dual-modifications promises to be an effective approach to develop the high-performance AEMs