arXiv subjects

Ge Huang

Publications and source records attributed to Ge Huang.

9 recordsLinked to original sources

arXiv2026

Every Kernel Is a Join: Automatic Multi-GPU Parallelism for AI Computations in Einsummable

Distributing an AI computation across the GPUs of a multi-GPU server is one of the central problems in systems-for-AI. We present Einsummable, a prototype system that accepts a PyTorch-like description of an AI computation and automatically distributes it across a multi-GPU server, with no device assignments, sharding annotations, or communication operations written by the programmer. Einsummable models every operation as a relational join followed by an aggregation over tensor relations, in which the tuples contain sub-tensors. Each operation exposes its possible decompositions through what we call "join-agg specs". An optimizer then selects decompositions across the whole computation to minimize a communication-cost proxy. Because it searches decompositions rather than a menu of named strategies, Einsummable discovers plans that mesh-based auto-parallelizers cannot. Each decomposed operation is implemented by synthesizing an exchange program, which is a topology-aware generalization of Volcano's exchange operator. Einsummable invokes no canned collectives: all communication and aggregation is special-purpose, derived at compile time. Despite being fully automatic, Einsummable can outperform custom-designed implementations. For example, on LLaMA transformer blocks on an eight-GPU A100 server, Einsummable achieves a geometric-mean runtime of 8.97 ms, versus 13.80 ms for hand-tuned PyTorch and 15.90 ms for vLLM.

cs.DC↗

arXiv2024

Probing superconducting gap in CeH$_9$ under pressure

The recent discovery of superconductivity in hydrogen-rich compounds has garnered significant experimental and theoretical interest because of the record-setting critical temperatures. As the direct observation of the superconducting (SC) gap in these superhydrides is rare, the underlying mechanism behind its occurrence has yet to be settled down. Here, we report a successful synthesis of the $\textit{P6$_3$}$/$\textit{mmc}$ phase of CeH$_9$ that exhibits the SC transition with SC critical temperature of about 100 K at a pressure of about 100 GPa. The observation of the zero electrical resistance and the critical current demonstrates that the SC phase is realized in Ce-based superhydride. Quasiparticle scattering spectroscopy (QSS) reveals the Andreev reflection at zero bias voltage, a hallmark of superconductivity, in the differential conductance. The obtained SC gap-to-$\textit{T}$$_c$ ratio of 4.36 and temperature dependence of SC gap are consistent with the prediction from the Bardeen-Cooper-Schrieffer theory with a moderate coupling strength. The successful realization of QSS under Megabar conditions is expected to provide a desired route to the study of the mechanism of superconductivity as well as the establishment of the SC phase in superhydride high-$\textit{T}$$_c$ systems.

cond-mat.supr-con↗

arXiv2022

Transport evidence for the surface state and spin-phonon interaction in FeTe$_{0.5}$Se$_{0.5}$

The iron chalcogenides have been proved to be intrinsic topological superconductors to implement quantum computation because of their unique electronic structures. The topologically nontrivial surface states of FeTe$_{0.5}$Se$_{0.5}$ have been predicted by several calculations and then confirmed by high-resolution photoemission and scanning tunneling experiments. However, so far, the shreds of the electrical transport evidence for topological surface states are still in absence. By carrying out electrical transport experiments, we observe a topological transition with a nonlinear Hall conductivity and simultaneous linear magnetoresistance near the superconducting transition temperature. Furthermore, we observe a sign reversal of the Hall coefficient accompanied by a concurrently softening of the ${A}_{1g}$ phonon mode at about 40 K, indicating a nematic transition. The synchronized phonon softening with nematicity manifests an enhanced fluctuation state through spin-phonon interaction. Our results solidly corroborate the topological surface states of FeTe$_{0.5}$Se$_{0.5}$ and provide an understanding of the mechanism of the superconductivity in iron chalcogenides.

cond-mat.supr-con↗

arXiv2022

Synthesis and Superconductivity in Yttrium-Cerium Hydrides at Moderate Pressures

Inspired by the high critical temperature in yttrium superhydride and the low stabilized pressure in superconducting cerium superhydride, we carry out four independent runs to synthesize yttrium-cerium alloy hydrides. The phases examined by the Raman scattering and x-ray diffraction measurements. The superconductivity is detected with the zero-resistance state at the critical temperature in the range of 97-140 K at pressures ranging from 114 GPa to 120$\pm$4 GPa. The maximum critical temperature of the synthesized hydrides is larger than those reported for cerium hydrides, while the corresponding stabilized pressure is much lower than those for superconducting yttrium hydrides. The structural analysis and theoretical calculations suggest that the phase of Y$_{0.5}$Ce$_{0.5}$H$_9$ has the space group $P6_3/mmc$ with the calculated critical temperature of 119 K, in fair agreement with the experiments. These results indicate that alloying superhydrides indeed can maintain relatively high critical temperature at modest pressures accessible by many laboratories.

cond-mat.supr-con↗

arXiv2022

Synthesis of Superconducting Phase of La$_{0.5}$Ce$_{0.5}$H$_{10}$ at High Pressures

Clathrate hydride \emph{Fm}\={3}\emph{m}-LaH$_{10}$ has been proven as the most extraordinary superconductor with the critical temperature $T_c$ above 250 K upon compression of hundreds of GPa in recent years. A general hope is to reduce the stabilization pressure and maintain the high $T_c$ value of the specific phase in LaH$_{10}$. However, strong structural instability distorts \emph{Fm}\={3}\emph{m} structure and leads to a rapid decrease of $T_c$ at low pressures. Here, we investigate the phase stability and superconducting behaviors of \emph{Fm}\={3}\emph{m}-LaH$_{10}$ with enhanced chemical pre-compression through partly replacing La by Ce atoms from both experiments and calculations. For explicitly characterizing the synthesized hydride, we choose lanthanum-cerium alloy with stoichiometry composition of 1:1. X-ray diffraction and Raman scattering measurements reveal the stabilization of \emph{Fm}\={3}\emph{m}-La$_{0.5}$Ce$_{0.5}$H$_{10}$ in the pressure range of 140-160 GPa. Superconductivity with $T_c$ of 175$\pm$2 K at 155 GPa is confirmed with the observation of the zero-resistivity state and supported by the theoretical calculations. These findings provide applicability in the future explorations for a large variety of hydrogen-rich hydrides.

cond-mat.supr-con↗

arXiv2021

Recurrent networks improve neural response prediction and provide insights into underlying cortical circuits

Feedforward CNN models have proven themselves in recent years as state-of-the-art models for predicting single-neuron responses to natural images in early visual cortical neurons. In this paper, we extend these models with recurrent convolutional layers, reflecting the well-known massive recurrence in the cortex, and show robust increases in predictive performance over feedforward models across thousands of hyperparameter combinations in three datasets of macaque V1 and V2 single-neuron responses. We propose the recurrent circuit can be conceptualized as a form of ensemble computing, with each iteration generating more effective feedforward paths of various path lengths to allow a combination of solutions in the final approximation. The statistics of the paths in the ensemble provide insights to the differential performance increases among our recurrent models. We also assess whether the recurrent circuits learned for neural response prediction can be related to cortical circuits. We find that the hidden units in the recurrent circuits of the appropriate models, when trained on long-duration wide-field image presentations, exhibit similar temporal response dynamics and classical contextual modulations as observed in V1 neurons. This work provides insights to the computational rationale of recurrent circuits and suggests that neural response prediction could be useful for characterizing the recurrent neural circuits in the visual cortex.

cs.NE↗

arXiv2019

A Neurally-Inspired Hierarchical Prediction Network for Spatiotemporal Sequence Learning and Prediction

In this paper we developed a hierarchical network model, called Hierarchical Prediction Network (HPNet), to understand how spatiotemporal memories might be learned and encoded in the recurrent circuits in the visual cortical hierarchy for predicting future video frames. This neurally inspired model operates in the analysis-by-synthesis framework. It contains a feed-forward path that computes and encodes spatiotemporal features of successive complexity and a feedback path for the successive levels to project their interpretations to the level below. Within each level, the feed-forward path and the feedback path intersect in a recurrent gated circuit, instantiated in a LSTM module, to generate a prediction or explanation of the incoming signals. The network learns its internal model of the world by minimizing the errors of its prediction of the incoming signals at each level of the hierarchy. We found that hierarchical interaction in the network increases semantic clustering of global movement patterns in the population codes of the units along the hierarchy, even in the earliest module. This facilitates the learning of relationships among movement patterns, yielding state-of-the-art performance in long range video sequence predictions in the benchmark datasets. The network model automatically reproduces a variety of prediction suppression and familiarity suppression neurophysiological phenomena observed in the visual cortex, suggesting that hierarchical prediction might indeed be an important principle for representational learning in the visual cortex.

cs.NE↗

arXiv2018

Explaining Neural Networks Semantically and Quantitatively

This paper presents a method to explain the knowledge encoded in a convolutional neural network (CNN) quantitatively and semantically. The analysis of the specific rationale of each prediction made by the CNN presents a key issue of understanding neural networks, but it is also of significant practical values in certain applications. In this study, we propose to distill knowledge from the CNN into an explainable additive model, so that we can use the explainable model to provide a quantitative explanation for the CNN prediction. We analyze the typical bias-interpreting problem of the explainable model and develop prior losses to guide the learning of the explainable additive model. Experimental results have demonstrated the effectiveness of our method.

cs.CV↗

arXiv2018

Observation of Meissner effect in potassium-doped \emph{p}-quinquephenyl}

The chain-like organic compounds with conjugated structure have the potential to become high temperature superconductors. We examine this idea by choosing p-quinquephenyl with five phenyl rings connected in para position. The dc magnetic susceptibility measurements provide solid evidence for the presence of Meissner effect when the compound is doped by potassium. The real part of the ac susceptibility shows exactly same transition temperature as that in dc magnetization, and the imaginary part of nearly zero value after transition implies the realization of zero-resistivity. All these features support the existence of superconductivity with a critical temperature of 7.3 K in this material. The occurrence of bipolarons revealed by Raman spectra guarantees potassium metal intercalated into p-quinquephenyl and suggests the important role of this elementary excitation played on superconductivity.

cond-mat.supr-con↗