Data Transfer Strategies Research Articles

Porting LHAASO WFCTA simulation job to ARM computing cluster

With the advancement of many large-scale high-energy physics experiments, the amount of data to be processed and analyzed has significantly increased. For example, since the start of the Large High Altitude Air Shower Observatory (LHAASO) experiment in 2020, their simulation jobs have been running on an Intel X86 cluster, producing only a fraction of the planned data for the first phase due to limited CPU resources. Therefore, it is necessary to explore and expand other computing service devices. We built an application ecosystem based on the ARM architecture to support offline data processing for high-energy physics. The main work includes porting the offline software based on LHAASO experiments to run on ARM machines, formulating data transfer and job scheduling strategies in the ARM cluster, and evaluating performance and power consumption in both Intel X86 and ARM clusters. The results show that the LHAASO simulation jobs can run correctly on the ARM computing cluster. The singlecore performance of Intel X86 CPUs is better than ARM CPUs, but for the entire server with a multicore architecture, ARM servers perform better.

Using transfer learning-based causality extraction to mine latent factors for Sjögren's syndrome from biomedical literature

Understanding causality is a longstanding goal across many different domains. Different articles, such as those published in medical journals, disseminate newly discovered knowledge that is often causal. In this paper, we use this intuition to build a model that leverages causal relations to unearth factors related to Sjögren's syndrome from biomedical literature. Sjögren's syndrome is an autoimmune disease affecting up to 3.1 million Americans. Due to the uncommon nature of the illness, symptoms across different specialties coupled with common symptoms of other autoimmune conditions such as rheumatoid arthritis, it is difficult for clinicians to diagnose the disease timely. Due to the lack of a dedicated dataset for causal relationships built from biomedical literature, we propose a transfer learning-based approach, where the relationship extraction model is trained on a wide variety of datasets. We conduct an empirical analysis of numerous neural network architectures and data transfer strategies for causal relation extraction. By conducting experiments with various contextual embedding layers and architectural components, we show that an ELECTRA-based sentence-level relation extraction model generalizes better than other architectures across varying web-based sources and annotation strategies. We use this empirical observation to create a pipeline for identifying causal sentences from literature text, extracting the causal relationships from causal sentences, and building a causal network consisting of latent factors related to Sjögren's syndrome. We show that our approach can retrieve such factors with high precision and recall values. Comparative experiments show that this approach leads to 25% improvement in retrieval F1-score compared to several state-of-the-art biomedical models, including BioBERT and Gram-CNN. We apply this model to a corpus of research articles related to Sjögren's syndrome collected from PubMed to create a causal network for Sjögren's syndrome. The proposed causal network for Sjögren's syndrome will potentially help clinicians with a holistic knowledge base for faster diagnosis.

Comparing SYCL data transfer strategies for tracking use cases

The aim of this work is to compare the performance and ease of programming of the various data transfer strategies provided by SYCL 2020: buffers/accessors on one hand and the different storage types exposed by Unified Shared Memory (USM) on the other hand. We measured the relative performance of USM exclusively located either on the host (USM host) or on the device (USM device), or automatically managed and moved (USM shared). We also tried to quantify the impact of formatting data in a GPU-friendly manner as opposed to using a regular pointer-based structure in which the C++ allocator was replaced by the SYCL allocator sycl::malloc. We first made a PCIe-bound micro-benchmark to test the SYCL memory models with a simple access pattern. We then switched to a real use-case provided by Traccc, a research project associated to the ACTS particle tracking library. The algorithm of interest is SparseCCL, a clustering algorithm used on the first step of track reconstruction. For consistency, all tests were made on a single hardware architecture: a recent server having a discrete GPU with dedicated VRAM, representative of the hardware currently used by the ACTS team.

A method for efficient radio astronomical data gridding on multi-core vector processor

Gridding is the performance-critical step in the data reduction pipeline for radio astronomy research, allowing astronomers to create the correct sky images for further analysis. Like the 2D stencil computation, gridding iteratively updates the output cells by convolution, where the value at each output cell in the space is computed as a weighted sum of neighboring point values. Existing state-of-the-art works have achieved performance improvement of gridding by using multi-core CPUs and GPUs in real-world applications, and their study proved that gridding is a type of scientific computation with high-density computing characteristics. However, low computational performance or high power consumption becomes the main limitation for their processing of large-scale astronomical data. The high-density computing feature of gridding provides opportunities to accelerate it on the multi-core vector processor with vector-SIMD architectures. However, existing works’ (such as those implemented on CPUs or GPUs) task parallelization and data transfer strategies are inefficient to perform gridding directly on the vector processor without any dedicated mapping algorithm.M-DSP is a multi-core vector processor with vector-SIMD architectures designed for the next-generation exascale supercomputer, delivering high performance with ultra-low power consumption. In this paper, we present, for the first time, a novel method to achieve efficient gridding on the M-DSP. Specifically, we propose a gridding workflow designed for the vector-SIMD architectures and present a vectorized version of the gridding convolution algorithm to fully exploit the computational power of the M-DSP. In addition, centering on the processor architectures, we propose task-based parallelization strategies for block and line computing as well as different data loading strategies to achieve high parallel performance and high data transfer efficiency. Experimental results show that our work on M-DSP exhibits very competitive performance compared to other methods running on CPUs or GPUs. This demonstrates the efficiency of our method and the fact that the vector-SIMD architecture is beneficial for scientific computing with ”high density” characteristics, which can exploit its wide vector core and achieve higher performance than its competitors.

Toward efficient execution of data-intensive workflows

Workflows that consume and produce large amounts of data are being widely used in modern scientific computing and data processing pipelines. Scheduling of data-intensive workflows requires a careful management of data transfers between tasks, since network contention can significantly impact the workflow execution time. The paper presents and evaluates several scheduling algorithms, data transfer strategies and optimizations aimed at efficient execution of data-intensive workflows. The studied approaches reduce or completely avoid network contention by explicit scheduling of data transfers and incorporate several optimizations, such as data caching, chunked and peer-to-peer data transfers. The results of experimental study demonstrate that the relative performance of different approaches depends on the workflow properties, data staging strategy and system configuration. The proposed CAS-L1 heuristic with additional data transfer optimizations achieves the best results.

UAV-Assisted Wireless Energy and Data Transfer With Deep Reinforcement Learning

As a typical scenario in future generation communication network applications, UAV-assisted communication can perform autonomous data delivery for massive machine type communication (mMTC), where the data generated from Internet of Things (IoT) devices can be carried and delivered to the corresponding locations with no direct communication channels to the IoT devices. Wireless energy transfer technique can recharge the UAV when the system is in operation, assisting the UAV to continuously collect and deliver data. In this work, we formulate a Markov decision process (MDP) model to describe the energy and data transfer optimization problem for the UAV. To maximize the long-term utility of the UAV, the MDP model is solved by value iteration algorithm to obtain the optimal strategies of the UAV to collect data, deliver data, and receive transferred energy to replenish on-device battery energy storage. Furthermore, to tackle the issues of system state uncertainties, partially observable states, and large state space in UAV-assisted communication systems, we extend the MDP model and solve it by using a ${Q}$ -learning and a deep reinforcement learning (DRL) schemes. Simulations and numerical results validate that, compared with baseline schemes, the proposed MDP model with DRL based scheme can achieve better wireless energy and data transfer strategies in terms of the higher long-term utility of the UAV.

GreenBDT: Renewable-aware scheduling of bulk data transfers for geo-distributed sustainable datacenters

The fast proliferation of cloud computing promotes the rapid growth of datacenters. More and more cloud service providers use geo-distributed green datacenters to support the expanding scale of cloud applications as well as minimize the carbon footprint. In such a geo-distributed green datacenter system, a basic and urgent demand is inter-datacenter bulk data transfer that is usually used for periodic data backup, software distribution, virtual machines cloning, etc. Though many existing research efforts have been made to build green datacenters or provide optimal scheduling for inter-datacenter bulk data transfers separately, still the goal for optimal scheduling of inter-green-datacenter bulk data transfers is being underachieved. This is an important problem, especially when an increasing number of geo-distributed datacenters are powered by renewable energy for reducing energy cost and protecting environment. In this paper, we study the problem of maximizing renewable energy use and minimizing grid energy cost for bulk data transfers between sustainable and green datacenters. We model this problem and propose a heuristic method to solve it. The proposed method is the first to explicitly address the green energy use maximization and grid energy cost minimization problem of inter-green-datacenter bulk data transfers for green and sustainable datacenters in the multi-electricity market environment. Extensive evaluations with real-life network topology, available wind power, and electricity prices show that our method can maximize renewable energy use and bring more energy cost savings over existing bulk data transfer strategies.

Privacy and Quality Preserving Multimedia Data Aggregation for Participatory Sensing Systems

With the popularity of mobile wireless devices equipped with various kinds of sensing abilities, a new service paradigm named participatory sensing has emerged to provide users with brand new life experience. However, the wide application of participatory sensing has its own challenges, among which privacy and multimedia data quality preservations are two critical problems. Unfortunately, none of the existing work has fully solved the problem of privacy and quality preserving participatory sensing with multimedia data. In this paper, we propose SLICER, which is the first k-anonymous privacy preserving scheme for participatory sensing with multimedia data. SLICER integrates a data coding technique and message transfer strategies, to achieve strong protection of participants' privacy, while maintaining high data quality. Specifically, we study two kinds of data transfer strategies, namely transfer on meet up (TMU) and minimal cost transfer (MCT). For MCT, we propose two different but complimentary algorithms, including an approximation algorithm and a heuristic algorithm, subject to different strengths of the requirement. Furthermore, we have implemented SLICER and evaluated its performance using publicly released taxi traces. Our evaluation results show that SLICER achieves high data quality, with low computation and communication overhead.

An energy-efficient data transfer strategy with link rate control for Cloud

Data transfer is an indispensable step that is widely involved in the maintenance and processing of Cloud data. Due to rapid growth in Cloud data, methods of reducing the huge energy consumption of data transfer in the Cloud have become a challenge. In this paper, we propose a novel energy-efficient data transfer strategy called link rate controlled data transfer LRCDT. By scheduling bandwidth in a link rate controlled fashion, LRCDT intends to reduce the energy consumption specifically for data transfer that does not require the maximum transfer speed, which is referred to as 'lazy' data transfer, so to achieve the energy-efficient data transfer goal in the overall term. The result in our simulation indicates that LRCDT is able to reduce energy consumption by up to 63% when compared to existing data transfer strategies.

Patient‐specific simulation for endovascular procedures: qualitative evaluation of the development process

Recent advancements in simulation permit patient-specific rehearsal of carotid artery stenting procedures. This study evaluates the feasibility of transferring patient-specific CT data into the simulator, creating a 3D reconstruction and performing a rehearsal. The face validity of the model was assessed. By thematic analysis of qualitative data, an algorithm was generated, focusing on simulation set-up, time of data transfer, software/compatibility issues and problem-solving strategies. The face validity of the simulated case was evaluated by 15 expert interventionalists: realism (median 4/5), training potential (median 4/5) and pre-procedure rehearsal potential for challenging CAS cases (median 4/5) were rated highly. Setting up a procedure rehearsal is feasible and reproducible for different patients in different hospital settings without major software compatibility issues. The time to create a 3D reconstruction of patient-specific CT data is a major factor in the total time necessary to set up a rehearsal. The face validity is highly rated by experts.

Design and implementation of large data buffer based on multithreading using dynamic feedback

The designing principle of large data driver buffer was studied.Ordinary data transfer strategies have their limitations either in time efficiency or performance stability.A multithreading positive waiting strategy based on dynamic feedback was proposed.Firstly,multithreading data transfer scheme was implemented in the form of main thread's positive wait.For high performance guarantees under unpredictable workloads' condition,dynamic feedback was imported to balance the data and instruction transfer between server-to-driver and driver-to-client.

Data transfer concepts and strategies in a psychological research environment

In this paper, we discuss the importance and problems of data transfer in psychological research environments. Discussion includes how simple text file transfer systems meet researchers’ needs. The systems’ shortcomings are also analyzed. Strategies for customizing systems, using pre-prepared software packages, point-to-point systems, and “networked” systems, are carefully analyzed.