To reduce the traffic volume and accelerate communication in distributed training (DT) jobs, recent works introduce In-Network Aggregation (INA) to move the gradient summation into network programmable switches. However, switch memory is a scarce resource, unable to support massive DT jobs in data centers, and existing INA solutions have not utilized switch memory to the best extent. We propose DSA, an Efficient Data-Plane switch memory Scheduler for in-network Aggregation. DSA introduces preemption to the switch memory management for INA jobs. Furthermore, under packet preemption scenarios, DSA optimizes the selective retransmission mechanism to reduce redundant retransimtting packets to alleviate congestion. In the data plane, DSA allows gradient tensors with high priority to preempt the switch aggregators (basic computation unit in INA) from tensors with low priority, which avoids an aggregator wasting time in idle. In the control plane, DSA devises a priority policy which assigns high priority to gradient tensors that benefit overall job efficiency more, e.g., communication-intensive jobs. We implement the prototype of DSA. The experimental results show that DSA can improve the average job completion time (JCT) by up to 1.35x compared with baseline solutions.

ByteScheduler partitions and rearranges tensor transmissions to improve the communication efficiency of distributed Deep Neural Network (DNN) training. The configuration of hyper-parameters (i.e., the partition size and the credit size) is critical to the effectiveness of partitioning and rearrangement. Currently ByteScheduler adopts Bayesian Optimization (BO) to find the optimal configuration for the hyper-parameters beforehand. In practice, however, various runtime factors (such as worker node status and network conditions) change over time, making the statically-determined one-shot configuration result suboptimal for real-world DNN training. To address this problem, in this paper we present a realtime configuration method (called AutoByte) that automatically and timely searches the optimal hyper-parameters as the training systems dynamically change. AutoByte extends the ByteScheduler framework with a meta network, which takes the systems’ runtime statistics as its input, dynamically adjusts the triggering threshold based on system environment characteristics, and outputs predictions for speedups under specific configurations. Evaluation results on various DNN models show that AutoByte can dynamically tune the hyper-parameters with low resource usage, and deliver up to 33.2% higher performance than the best static configuration method on the ByteScheduler framework.

The convergence of large language models (LLMs) and virtual reality (VR) technologies has led to significant breakthroughs across multiple domains, particularly in healthcare and medicine. Owing to its immersive and interactive capabilities, VR technology has demonstrated exceptional utility in surgical simulation, rehabilitation, physical therapy, mental health, and psychological treatment. By creating highly realistic and precisely controlled environments, VR not only enhances the efficiency of medical training but also enables personalized therapeutic approaches for patients. The convergence of LLMs and VR extends the potential of both technologies. LLM-empowered VR can transform medical education through interactive learning platforms and address complex healthcare challenges using comprehensive solutions. This convergence enhances the quality of training, decision-making, and patient engagement, paving the way for innovative healthcare delivery. This study aims to comprehensively review the current applications, research advancements, and challenges associated with these two technologies in healthcare and medicine. The rapid evolution of these technologies is driving the healthcare industry toward greater intelligence and precision, establishing them as critical forces in the transformation of modern medicine.