The spread of infectious diseases in networked populations poses significant challenges for public health intervention strategies. Traditional centrality-based and heuristic network dismantling approaches prioritize highly connected nodes but often fail to account for individual mortality risk, limiting their effectiveness in minimizing overall fatalities. While recent advances in machine learning have improved intervention strategies, existing models largely focus on reducing disease transmission rather than directly targeting mortality outcomes. To address this gap, we propose a reinforcement learning-based framework that integrates graph representation learning to identify and remove high-risk nodes, thereby maximizing network fragmentation while minimizing overall deaths. The framework is trained using synthetic networks and evaluated on five synthetic and four real-world datasets, benchmarking its performance against state-of-the-art network dismantling methods [graph dismantling with machine learning (GDM), generalized network dismantling (GND), and graph enhanced reinforcement learning (GERL)]. Experimental results demonstrate that the proposed method consistently outperforms baseline approaches, particularly in scale-free and community-structured networks, where targeted node removal significantly weakens network connectivity and suppresses epidemic spread. Moreover, in real-world networks, the method achieves lower cumulative death rates and higher epidemic thresholds, highlighting its robustness in controlling disease propagation. By incorporating mortality risk into network representation learning, the proposed framework offers a scalable, adaptive, and socially responsible approach to epidemic mitigation, misinformation control, and network resilience enhancement.

Advances in distributed machine learning can empower future communications and networking. The emergence of federated learning (FL) has provided an efficient framework for distributed machine learning, which, however, still faces many security challenges. Among them, model poisoning attacks have a significant impact on the security and performance of FL. Given that there have been many studies focusing on defending against model poisoning attacks, it is necessary to survey the existing work and provide insights to inspire future research. In this paper, we first classify defense mechanisms for model poisoning attacks into two categories: evaluation methods for local model updates and aggregation methods for the global model. Then, we analyze some of the existing defense strategies in detail. We also discuss some potential challenges and future research directions. To the best of our knowledge, we are the first to survey defense methods for model poisoning attacks in FL.

Recently, there is a flurry of projects that develop data plane systems in programmable switches, and these systems perform far more sophisticated processing than simply deciding a packet's next hop (i.e., traditional forwarding). This presents challenges to existing network program profilers, which are developed primarily to handle stateless forwarding programs.

Recently, the dramatic increase of the data amounts drives up the demand for data compression among HPC applications. Although many file systems and I/O middlewares have incorporated compression features, few high-level parallel I/O libraries support data compression due to the challenges of achieving scalable performance on HPC systems. This paper presents the design and implementation of the variable compression feature in the Parallel NetCDF library. Our design employs the same concept of chunking used by the HDF5 library, but we focus on enabling I/O aggregation across multiple requests to address the challenges on performance and scalability. We evaluate our solution using the I/O kernel of real-world scientific applications and analyze the impacts of data compression on parallel I/O performance. Our result suggests that handling multiple requests at once can significantly improve the parallel I/O performance on chunked and compressed data.

Accessing non-contiguous blocks in multiple array variables is a challenging I/O pattern for parallel applications to obtain good I/O performance. High-level I/O libraries such as HDF5 allow users to implement this pattern conveniently, but users have observed significant performance bottlenecks in the two-phase I/O implementation of MPI-IO. Recent studies have advanced the two-phase I/O performance by novel communication algorithms, but such improvements still have limitations. Two-phase I/O has to faithfully process inputs from high-level I/O libraries, so that implementation overheads can accumulate for improper usage of high-level I/O libraries. In this paper, we propose approaches for efficient usage of high-level I/O libraries that can circumvent major collective I/O overheads. We adopt a multi-dataset implementation of HDF5 dataset I/O to aggregate non-contiguous requests for array blocks and provide corresponding parameter assignment strategies. These approaches reduce the overheads caused by communication straggler effects in two-phase I/O. We show that our proposed methods can improve the parallel I/O performance up to 8× on two supercomputing systems for the HDF5 implementations of an I/O kernel extracted from climate simulation code compared with its baseline implementations.

Many scientific applications have started using deep learning methods for their classification or regression problems. However, for data-intensive scientific applications, I/O performance can be the major performance bottleneck. In order to effectively solve important real-world problems using deep learning methods on High-Performance Computing (HPC) systems, it is essential to address the poor I/O performance issue in large-scale neural network training. In this paper, we propose an asynchronous I/O strategy that can be generally applied to deep learning applications. Our I/O strategy employs an I/O -dedicated thread per process, that performs I/O operations independently of the training progress. The I/O thread reads many training samples at once to reduce the total number of I/O operations per epoch. Given the fixed amount of training data, the fewer the I/O operations per epoch, the shorter the overall I/O time. The I/O operations are also overlapped with the computations using the double-buffering method. We evaluate our I/O strategy using two real-world scientific applications, CosmoFlow and Neuron-Inverter. Our experimental results demonstrate that the proposed I/O strategy significantly improves the scaling performance without affecting the regression performance.

The gap between CPU and networking speeds has motivated the development of SmartNICs for NF (network functions) offloading. However, offloading performance is predicated upon intricate knowledge about SmartNIC hardware and careful hand-tuning of the ported programs. Today, developers cannot easily reason about the offloading performance or the effectiveness of different porting strategies without resorting to a trial-and-error approach.

Synchronous Stochastic Gradient Descent (SGD) with data parallelism, the most popular parallel training strategy for deep learning, suffers from expensive gradient communications. Local SGD with periodic model averaging is a promising alternative to synchronous SGD. The algorithm allows each worker to locally update its own model, and periodically averages the model parameters across all the workers. While this algorithm enjoys less frequent communications, the convergence rate is strongly affected by the number of workers. In order to scale up the local SGD training without losing accuracy, the number of workers should be sufficiently small so that the model converges reasonably fast. In this paper, we discuss how to exploit the degree of parallelism in local SGD while maintaining model accuracy. Our training strategy employs multiple groups of processes and each group trains a local model based on data parallelism. The local models are periodically averaged across all the groups. Based on this hierarchical parallelism, we design a model averaging algorithm that has a cheaper communication cost than allreduce-based approach. We also propose a practical metric for finding the maximum number of workers that does not cause a significant accuracy loss. Our experimental results demonstrate that our proposed training strategy provides a significantly improved scalability while achieving a comparable model accuracy to synchronous SGD.

Quickly identifying astronomical transients from synoptic surveys is critical to many recent astrophysical discoveries. However, each of the data processing pipelines in these surveys contains dozens of stages with highly varying time and space requirements. Properly predicting the resources required to run these pipelines is critical for the allocation of computing resources and reducing the discovery response time. We propose a machine learning strategy for this prediction task and demonstrate its effectiveness using a set of timing measurements from the intermediate Palomar Transient Factory (iPTF) workflow. The proposed model utilizes the spatiotemporal correlation of astronomical images, where nearby patches of the sky (space) are likely to have a similar number of objects of interest and workflows executed in the recent past (time) are likely to use a similar amount of time because the machines and data storage systems are likely to be in similar states. We capture the relationship among these spatial and temporal features in a Bayesian network and study how they impact the prediction accuracy. This Bayesian network helps us to identify the most influential features for predictions. With proper features, our models achieve errors close to the random variance boundary within batches of images taken at the same time, which can be regarded as the intrinsic limit of prediction accuracy.

The gap between CPU and networking speeds has motivated the development of SmartNICs for near-network processing. Recent work has shown that many network functions can benefit from SmartNIC offloading, but identifying the best porting strategy requires hand-tuning and workload-specific optimizations. The developer has no easy way to understand the ported performance beforehand

Dynamic spectrum access (DSA) is a promising platform to solve the spectrum shortage problem, in which auction based mechanisms have been extensively studied due to good spectrum allocation efficiency and fairness. Recently, Sybil attacks were introduced in DSA, and Sybil-proof spectrum auction mechanisms have been proposed, which guarantee that each single secondary user (SU) cannot obtain a higher utility under more than one fictitious identities. However, existing Sybil-poof spectrum auction mechanisms achieve only Sybil-proofness for SUs, but not for primary users (PUs), and simulations show that a cheating PU in those mechanisms can obtain a higher utility by Sybil attacks. In this paper, we propose TSUNAMI, the first Truthful and primary user Sybil-proof aUctioN mechAnisM for onlIne spectrum allocation. Specifically, we compute the opportunity cost of each SU and screen out cost-efficient SUs to participate in spectrum allocation. In addition, we present a bid-independent sorting method and a sequential matching approach to achieve primary user Sybil-proofness and 2-D truthfulness, which means that each SU or PU can gain her maximal utility by bidding with her true valuation of spectrum. We evaluate the performance and validate the desired properties of our proposed mechanism through extensive simulations.

As modern parallel computers enter the exascale era, the communication cost for redistributing requests becomes a significant bottleneck in MPIIO routines. The communication kernel for request redistribution, which has an all-to-many personalized communication pattern for application programs with a large number of noncontiguous requests, plays an essential role in the overall performance. This paper explores the available communication kernels for two-phase I/O communication. We generalize the spread-out algorithm to adapt to the all-to-many communication pattern of two-phase I/O by reducing the communication straggler effect. Communication throttling methods that reduce communication contention for asynchronous MPI implementation are adopted to improve communication performance further. Experimental results are presented using different communication kernels running on Cray XC40 Cori and IBM AC922 Summit supercomputers with different I/O patterns. Our study shows that adjusting communication kernel algorithms for different I/O patterns can improve the end-to-end performance up to 10 times compared with default MPI-IO implementations.

Two-phase I/O is a well-known strategy for implementing collective MPI-IO functions. It redistributes I/O requests among the calling processes into a form that minimizes the file access costs. As modern parallel computers continue to grow into the exascale era, the communication cost of such request redistribution can quickly overwhelm collective I/O performance. This effect has been observed from parallel jobs that run on multiple compute nodes with a high count of MPI processes on each node. To reduce the communication cost, we present a new design for collective I/O by adding an extra communication layer that performs request aggregation among processes within the same compute nodes. This approach can significantly reduce inter-node communication contention when redistributing the I/O requests. We evaluate the performance and compare it with the original two-phase I/O on Cray XC40 parallel computers (Theta and Cori) with Intel KNL and Haswell processors. Using I/O patterns from two large-scale production applications and an I/O benchmark, we show our proposed method effectively reduces the communication cost and hence maintains the scalability for a large number of processes.

The demands of increasingly large scientific application workflows lead to the need for more powerful supercomputers. As the scale of supercomputing systems have grown, the prediction of fault tolerance has become an increasingly critical area of study, since the prediction of system failures can improve performance by saving checkpoints in advance. We propose a real-time failure detection algorithm that adopts an event-based prediction model. The prediction model is a convolutional neural network that utilizes both traditional event attributes and additional spatio-temporal features. We present a case study using our proposed method with six years of reliability, availability, and serviceability event logs recorded by Mira, a Blue Gene/Q supercomputer at Argonne National Laboratory. In the case study, we have shown that our failure prediction model is not limited to predict the occurrence of failures in general. It is capable of accurately detecting specific types of critical failures such as coolant and power problems within reasonable lead time ranges. Our case study shows that the proposed method can achieve a F1score of 0.56 for general failures, 0.97 for coolant failures, and 0.86 for power failures.

Training Convolutional Neural Network (CNN) is a computationally intensive task, requiring efficient parallelization to shorten the execution time. Considering the ever-increasing size of available training data, the parallelization of CNN training becomes more important. Data-parallelism, a popular parallelization strategy that distributes the input data among compute processes, requires the mini-batch size to be sufficiently large to achieve a high degree of parallelism. However, training with large batch size is known to produce a low convergence accuracy. In image restoration problems, for example, the batch size is typically tuned to a small value between 16 ~ 64, making it challenging to scale up the training. In this paper, we propose a parallel CNN training strategy that gradually increases the mini-batch size and learning rate at run-time. While improving the scalability, this strategy also maintains the accuracy close to that of the training with a fixed small batch size. We evaluate the performance of the proposed parallel CNN training algorithm with image regression and classification applications using various models and datasets.

MPI inter-group collective communication patterns can be viewed as bipartite graphs that divide processes into two disjoint groups in which messages are transferred between but not within the groups. Such communication patterns can serve as basic operations for scientific application workflows. In this paper, we present parallel algorithms for inter-group all-to-all broadcast (Allgather) communication with optimal bandwidth for any message size and process number under single-port communication constraints. We implement the algorithms using MPI point-to-point and intra-group collective communication functions and evaluate their performance on the Cori supercomputer at NERSC. Using message sizes ranging from 256B to 64MB, the experiments show a significant performance improvement achieved by our algorithm, which is up to 9.27 times faster than production MPI libraries that adopt the so called root-gathering algorithm.

Geostatistical interpolation is the process that uses existing data and statistical models as inputs to predict data in unobserved spatio-temporal contexts as output. Kriging is a well-known geostatistical interpolation method that minimizes mean square error of prediction. The result interpolated by Kriging is accurate when consistency of statistical properties in data is assumed. However, without this assumption, Kriging interpolation has poor accuracy. To address this problem, this paper presents a new filtering-based clustering algorithm that partitions data into clusters such that the interpolation error within each cluster is significantly reduced, which in turn improves the overall accuracy. Comparisons to traditional Kriging are made with two real-world datasets using two error criteria: normalized mean square error(NMSE) and χ2 test statistics for normalized deviation measurement. Our method has reduced NMSE by more than 50% for both datasets over traditional Kriging. Moreover, χ2 tests have also shown significant improvements of our approach over traditional Kriging.