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Fast parallel Bayesian Network Structure Learning

Fast Parallel Bayesian Network Structure Learning

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international symposium on parallel and distributed Processing (IPDPS)

作者： Jiantong Jiang Zeyi Wen Ajmal Mian Department of Computer Science and Software Engineering The University of Western Australia

Bayesian networks (BNs) are a widely used graphical model in machine learning for representing knowledge with uncertainty. The mainstream BN structure learning methods require performing a large number of conditional independence (CI) tests. The learning process is very time-consuming, especially for high-dimensional problems, which hinders the adoption of BNs to more applications. Existing works attempt to accelerate the learning process with parallelism, but face issues including load unbalancing, costly atomic operations and dominant parallel overhead. In this paper, we propose a fast solution named Fast-BNS on multi-core CPUs to enhance the efficiency of the BN structure learning. Fast-Bns is powered by a series of efficiency optimizations including (i) designing a dynamic work pool to monitor the processing of edges and to better schedule the workloads among threads, (ii) grouping the CI tests of the edges with the same endpoints to reduce the number of unnecessary CI tests, (iii) using a cache-friendly data storage to improve the memory efficiency, and (iv) generating the conditioning sets on-the-fly to avoid extra memory consumption. A comprehensive experimental study shows that the sequential version of Fast-BNS is up to 50 times faster than its counterpart, and the parallel version of Fast-Bns achieves 4.8 to 24.5 times speedup over the state-of-the-art multi-threaded solution. Moreover, Fast-BNS has a good scalability to the network size as well as sample size.

关键词： Schedules Uncertainty Instruction sets Scalability Memory management parallel processing Dynamic scheduling

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A SystemC Model of the IEEE 802.11 EDCA Protocol for Virtual Prototyping

A SystemC Model of the IEEE 802.11 EDCA Protocol for Virtual...

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2021 international symposium on Networks, Computers and Communications, ISNCC 2021

作者： Beichler, Benjamin Rethfeldt, Michael Raddatz, Hannes Steffen, Lukas Timmermann, Dirk Haubelt, Christian Institute Of Applied Microelectronics And Computer Engineering University Of Rostock Germany

ISBN: (纸本)9780738113166

Due to the increasing complexity of distributed embedded systems, the development process demands early substantiation of design decisions for the targeted system. Virtual prototypes enable an early validation of hardware/software systems and they have proven to be very beneficial for target software development. However, these prototypes often do not include models for inter-device communication aspects of distributed systems. Especially the use of wireless communication has a significant impact on the overall system behavior and performance. This paper introduces a SystemC model of an essential part of IEEE 802.11 Wireless LAN communication, the Enhanced distributed Channel Access (EDCA) protocol. In comparison to widely used network simulations, the modeling with SystemC promises an easier integration into existing virtual prototypes. EDCA is the main part of the lower medium access layer (MAC) of IEEE 802.11 and interfaces to the physical-layer communication as well as to non-time-critical higher MAC-layer functions. We conducted experiments with an additional simple physical communication channel model to compare our solution in terms of precision and performance to the well-established network simulator ns-3. We show that our model differs by $\approx 0.3 \%$ of retransmitted frames and an accompanied difference of the overall simulated transmission time of $\approx 0.45 \%$. It meets the overall expectations for the stochastic EDCA protocol. Moreover, the presented model has a significantly better simulation performance with $3 \cdot 10^{-5}$ s wall-clock time per frame, which is at least two orders of magnitude better than with the compared ns-3 simulation. © 2021 IEEE.

关键词： Stochastic systems

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HPC Driven Innovations in Network Management for Greater Efficiency and Productivity

HPC Driven Innovations in Network Management for Greater Eff...

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international Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Harsha Veerla Vinay Chindukuri Yaseen Junaid Mohammed Vinay Palakonda Radhika Rani Chintala Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

High Performance Computing (HPC) is generally a powerful computing system and software which is used to solve complex computational problems that the today's computing methods fail to solve. HPC systems use parallel processing methods, distributed computing, and specialized hardware like FPGA (Field programmable gate arrays) and GPU (Graphics processing unit) to get high level of performance. Developing of HPC systems has become rapidly evolving field, to bring out the new technologies of hardware and software for optimization techniques and managing to get better performance. HPC systems are used in wide range of machine learning, scientific and engineering applications including weather forecasting etc. Users can also use HPC to run complex modelling problems such as nuclear physics, AI (Artificial Intelligence), climate modelling etc. It is applied to solve transaction processing, data ware houses like business uses. It will perform high number of operations in no time and completes the operations under a tight deadline. In this computing method user can solve many computational things parallelly. It is also known as Supercomputing which performs operations at highest rate on computers which can perform more than a quadrillion floating-point calculations per second. In this article the present and emerging application domains of HPC and the role of it in shaping the future of networking are addressed.

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Towards automated kernel selection in machine learning systems: A SYCL case study 34

Towards automated kernel selection in machine learning syste...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Lawson, John Codeplay Software Ltd Edinburgh Midlothian Scotland

ISBN: (纸本)9781728174457

Automated tuning of compute kernels is a popular area of research, mainly focused on finding optimal kernel parameters for a problem with fixed input sizes. This approach is good for deploying machine learning models, where the network topology is constant, but machine learning research often involves changing network topologies and hyperparameters. Traditional kernel auto-tuning has limited impact in this case;a more general selection of kernels is required for libraries to accelerate machine learning research. In this paper we present initial results using machine learning to select kernels in a case study deploying high performance SYCL kernels in libraries that target a range of heterogeneous devices from desktop GPUs to embedded accelerators. The techniques investigated apply more generally and could similarly be integrated with other heterogeneous programming systems. By combining auto-tuning and machine learning these kernel selection processes can be deployed with little developer effort to achieve high performance on new hardware.

关键词： Auto-tuning SYCL GPGPU Machine learning

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Optimizing Solar-Powered Electric Vehicle Management with distributed Decision Making Under Shading Conditions

Optimizing Solar-Powered Electric Vehicle Management with Di...

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international Conference on engineering and Emerging Technologies (ICEET)

作者： M. Ben Saïd-Romdhane S. Skander-Mustapha I. Slama-Belkhodja LR11ES15 Laboratoire de Systèmes Electriques Université de Tunis El Manar Ecole Nationale d'Ingénieurs de Tunis Tunis Université de Gabès Institut Supérieur des Sciences Appliquées et de Technologie de Gabès Gabès Tunisie Université de Carthage Ecole Nationale d'Architecture et d'Urbanisme Sidi Bou Saïd Tunisie

ISBN: (数字)9798350316926

ISBN: (纸本)9798350316933

Solar-powered electric vehicles (SEVs) have gained significant importance in the future of sustainable transportation due to their positive environmental impact and the increasing cost of fuel. However, a major limitation of SEVs is that photovoltaic (PV) panels supply less power than required to propel the vehicle, necessitating the use of energy storage systems. This situation presents several challenges, including shading obstacles in urban areas, driver and road safety concerns, the need to differentiate between shiftable and non-shiftable loads based on current conditions, and maximizing the lifetime of the storage system. To address these issues, this paper proposes a distributed energy management approach that incorporates four parallel functions: frequency separation, reduction, prediction, and shedding. Each function can operate independently, providing greater flexibility. The effectiveness and high performance of this proposed strategy were demonstrated through simulations conducted using Matlab software. To validate the efficacy of the approach, experimental results were achieved using the hardware-in-the-loop testing.

关键词： Photovoltaic systems Urban areas Supercapacitors Electric vehicles software Batteries Time factors

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EDAML 2022 Keynote Speaker: Machine Learning for Agile, Intelligent and Open-Source EDA

EDAML 2022 Keynote Speaker: Machine Learning for Agile, Inte...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： David Z. Pan Department of Electrical and Computer Engineering The University of Texas Austin TX

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

This talk will present some recent results and trends toward agile, intelligent and open-source design automation for digital/analog/mixed-signal ICs, in particular leveraging AI/machine learning with domainspecific customizations. Placement is a fundamental EDA problem. I will first show how we leverage deep learning hardware and software to develop an open-source VLSI placement engine, DREAMPlace [DAC'19 and TCAD'21 Best Paper Awards], which is around 40x faster than the previous state-of-the-art academic global placer. DREAMPlace has been further developed to tackle detailed placement acceleration and region constraints, and used together with reinforcement learning for macro placement to achieve superhuman results. I will then present the DARPA-funded project MAGICAL which leverages both machine and human intelligence to produce fully automated analog layout from netlists to GDSII, including automatic layout constraint generation, placement, and routing. MAGICAL 1.0 has been open-sourced, and validated with silicon tape-outs. The talk will conclude with some future research directions.

关键词： Open source software Silicon Layout Hardware distributed processing Design automation Conferences

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A Systematic Migration Methodology for Complex Real-time software systems 23

A Systematic Migration Methodology for Complex Real-time Sof...

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IEEE 23rd international symposium on Real-Time distributed Computing (ISORC)

作者： Salman, Shaik Mohammed Papadopoulos, Alessandro Vittorio Mubeen, Saad Nolte, Thomas ABB AB Vasteras Sweden Malardalen Univ Vasteras Sweden

ISBN: (纸本)9781728169583

This paper provides a systematic three-stage methodology for migrating complex real-time industrial software systems from single-core to multi-core computing platforms. Single-core platforms have limited computational capabilities that prevent integration of computationally demanding applications such as image processing within the existing system. Modern multi-core processors provide increased computing capacity and allow the parallel execution of different applications within the system. However, this transition is non-trivial and requires a systematic and well-defined migration process. This paper reviews some of the existing migration methods and provides a systematic multi-phase migration process with emphasis on software architecture recovery and transformation to explicitly address the timing and dependability attributes expected of industrial software systems.

关键词： real-time systems multi-core software architecture software migration robotics

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FAM-Graph: Graph Analytics on Disaggregated Memory

FAM-Graph: Graph Analytics on Disaggregated Memory

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international symposium on parallel and distributed Processing (IPDPS)

作者： Daniel Zahka Ada Gavrilovska Georgia Institute of Technology

Disaggregated memory is being proposed as a way to provide efficient memory scaling for data intensive applications. High performance interconnect technologies, such as CXL, make disaggregated, fabric-attached-memory (FAM) a viable secondary tier of memory. Previous work on remote memory relies on extending kernel level paging to utilize FAM as an additional storage tier after local memory. These approaches have the advantage of exposing remote memory in application transparent ways that do not require code changes, but they incur large overheads due to the mismatch between the abstraction of a flat virtual address space and the reality of the tiered nature of FAM. In this paper, we present an alternative approach to remote memory based on application-specific objects. We design FAM-Graph - a semi-external graph processing system that leverages application-level properties, such as read only edge data, to efficiently tier data between local and remote memory, and prefetch remote data for local computation. Using several graph algorithms and datasets, we demonstrate that FAM-Graph achieves end-to-end performance within factors of 1–6× of Galois, the state of the art shared memory graph processing system, while using up to 20× less local memory. When Galois is used in conjunction with an OS-level FAM solution, we show that FAM-Graph achieves better end-to-end performance by up to 9× when both systems are configured with the same amount of local memory.

关键词： distributed processing Runtime Costs Prefetching Memory management software algorithms Semantics

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Familia: A Configurable Topic Modeling Framework for Industrial Text engineering 26th

Familia: A Configurable Topic Modeling Framework for Industr...

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26th international Conference on Database systems for Advanced Applications (DASFAA)

作者： Jiang, Di Song, Yuanfeng Lian, Rongzhong Bao, Siqi Peng, Jinhua He, Huang Wu, Hua Zhang, Chen Chen, Lei Baidu Inc Beijing Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9783030731991;9783030732004

In this paper, we propose a configurable topic modeling framework named Familia. Familia supports an important line of topic models that are widely applicable in text engineering scenarios. In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models. Simply through changing the data organization of Familia, software engineers are able to easily explore a broad spectrum of existing topic models or even design their own topic models, and find the one that best suits the problem at hand. With its superior extendability, Familia has a novel sampling mechanism that strikes balance between effectiveness and efficiency of parameter inference. Furthermore, Familia is essentially a big topic modeling framework that supports parallel parameter inference and distributed parameter storage. The utilities and necessity of Familia are demonstrated in real-life industrial applications. Familia would significantly enlarge software engineers' arsenal of topic models and pave the way for utilizing highly customized topic models in real-life problems. Source code of Familia have been released at Github via https://***/baidu/Familia/.

关键词： Topic modeling Familia Text engineering

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Pre-LogMGAE: Identification of Log Anomalies Using a Pre-Trained Masked Graph Autoencoder

Pre-LogMGAE: Identification of Log Anomalies Using a Pre-Tra...

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Reliable distributed systems

作者： Aming Wu Young-Woo Kwon School of Computer Science and Engineering Kyungpook National University Daegu South Korea

ISBN: (数字)9798331530037

ISBN: (纸本)9798331530044

Log-based anomaly detection in software systems is becoming increasingly crucial for monitoring network operations and ensuring system security. Deep learning-based methods are widely used for large-scale log anomaly detection due to their capacity to learn complex features. However, current research predominantly treats original logs as simple sequences, ignoring their complex structure and dynamic dependency relationships. Additionally, these methods often rely on extensive labeled data or domain-specific vectors to represent logs for model training, which can be labor-intensive to label manually and ineffective across various domains within a system. To address these challenges, this paper proposes Pre-LogMGAE, a universal masked graph autoencoder (GAE) framework with contrastive learning for self-supervised pre-training for log anomaly detection. In contrast to graph or link reconstruction, Pre-LogMGAE focuses on node feature reconstruction using a masking strategy to reduce the impact of excessive redundant information. Furthermore, we introduce Graph Attention Networks (GAT) with the Gated Recurrent Unit (GRU) to incorporate sequence modeling, allowing for capturing long-term and short-term dependencies in log events. We include contrastive learning objectives in finetuning to extract diverse features and enhance the algorithm's robustness. Through an extensive evaluation of three real-world datasets and specific case studies with configuration error, Pre-LogMGAE demonstrates superior performance compared to the six baselines, including PCA, IM, DeepLog, LogRobust, LogBERT, and DeepTraLog. This superiority is evident in terms of precision, recall, F1 score, and time efficiency, highlighting Pre-LogMGAE's stability and reliability in anomaly detection. The study aims to improve anomaly detection capabilities in multi-source system logs, offering innovative technical support to enhance system security and reliability.

关键词： Training Autoencoders Semantics Contrastive learning Feature extraction software systems Vectors Stability analysis Security Anomaly detection

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