Leveraging the latest Sunway supercomputer, we developed a fully optimized earthquake simulation model that accurately captures topographic effects for realistic seismic analysis. Optimizing for the SW26010Pro architecture with DMA/RMA communication mechanisms, data compression schemes, and vectorization, we achieved a speedup exceeding 160×. Our pipeline-based computation and communication overlapping scheme, combined with performance prediction models further minimized computational costs. These optimizations enabled the largest-scale curvilinear grid finite-difference method (CGFDM) earthquake simulations to date, covering 197 trillion grid points and achieving 86.7 PFLOPS on 39 million cores with a weak scaling efficiency of 97.9%. These advancements enabled the successful simulation of the 2008 Wenchuan earthquake, providing high-resolution seismic insights and robust assessments for regional hazard mitigation and disaster preparedness.

Distributed training is the most common way to scale out and accelerate Deep Neural Network (DNN) training. Distributed DNN training requires synchronization of gradient aggregation among all workers through collective communication operations before proceeding to the next training round. Any long-tail delay would be an obstacle to model training performance and convergence. This paper proposes a load balancing scheme SOLB for distributed DNN training acceleration, which utilizes a synchronization-objective load balancing to ensure packets with the same gradient indices from the same training job arrive at the receiver back to back, enabling optimal synchronous communication. Furthermore, leveraging the independence of parameter updates, we design an order-agnostic transmission protocol to avoid the overhead of packet reordering without affecting the training accuracy. Through both testbed and simulation experiments, our scheme significantly reduces gradient aggregation time by up to 82.5% and accelerates overall model training by up to 84.4% compared to the state-of-the-art load balancing schemes.

Most existing flow scheduling schemes aim to minimize the flow completion time in data center network. However, these schemes either lack fine-grained flow differentiation, sacrificing performance for deployability or require precise flow information and significant hardware modifications for nearoptimal transmission. Thus, we present Sadra, a novel flow scheduling solution leveraging multiple priority queues available in existing commodity switches to minimize FCT by assigning different initial priorities and demotion thresholds to flows with various sizes. Our intuition is that the average queuing length of small flows increases as the number of large flows grows, yet reducing queuing length improves latency of small flows with negligible impact on throughput of large flows. Sadra consists of two key parts: i) A novel scheduling scheme that utilizes imprecise predictions to assign initial priorities to flows with different sizes, enabling differentiation from the outset of transmission; ii) A size-aware demotion threshold strategy that assigns different demotion thresholds to further differentiate flows with the same initial priority, thereby reducing the blocking of small flows by large ones. We have implemented a Sadra prototype and evaluated Sadra through both testbed experiments and NS3 simulations. Simulation and testbed evaluations show that Sadra significantly reduces the average and tail FCT of small flows by up to 61% and 89% compared with the state-of-the-art schemes, respectively.

We put forward a neural network approach to predict existing and in-fill oil well performance. Multiple wells history production data were used to train the neural network, and the established neural network can be used to predict future performance of oil wells. No reservoir data is currently involved in the establishment of neural network, therefore it can predict well production performance in absence of reservoir data. Since both the static and dynamic data are used in the training, we combine the spatial and time series prediction together in this approach. Primary production of a 9-well area in North Robertson Unit located in west Texas was tested in this paper. The results demonstrate that our approach is powerful in rapid projection of existing wells future performance, as well as the performance prediction of in-fill drilling wells. By incorporating the appropriate optimization technique, it can be further extended for use in location optimization of in-fill drilling wells.