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MANGO: A PYTHON LIBRARY FOR parallel HYPERPARAMETER TUNING

MANGO: A PYTHON LIBRARY FOR PARALLEL HYPERPARAMETER TUNING

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IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Sandha, Sandeep Singh Aggarwal, Mohit Fedorov, Igor Srivastava, Mani Univ Calif Los Angeles Los Angeles CA 90024 USA Arm Research Cambridge MA USA

ISBN: (纸本)9781509066315

Tuning hyperparameters for machine learning algorithms is a tedious task, one that is typically done manually. To enable automated hyperparameter tuning, recent works have started to use techniques based on Bayesian optimization. However, to practically enable automated tuning for large scale machine learning training pipelines, significant gaps remain in existing libraries, including lack of abstractions, fault tolerance, and flexibility to support scheduling on any distributed computing framework. To address these challenges, we present Mango, a Python library for parallel hyperparameter tuning. Mango enables the use of any distributed scheduling framework, implements intelligent parallel search strategies, and provides rich abstractions for defining complex hyperparameter search spaces that are compatible with scikit-learn. Mango is comparable in performance to Hyperopt [1], another widely used library. Mango is available open-source [2] and is currently used in production at Arm Research to provide state-of-art hyperparameter tuning capabilities.

关键词： Hyperparameter tuning Bayesian optimization Python library parallel Optimizer

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Application of distributed computation of texture features to the analysis of biomedical images

Application of distributed computation of texture features t...

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18th international Scientific and Technical conference on Optical Technologies for Communications

作者： Ilyasova, Nataly Yu Demin, Nikita S. Shirokanev, Alexandr S. Samara Natl Res Univ Moskovskoye Shosse 34 Samara 443086 Russia RAS IPSI RAS Branch FSRC Crystallog & Photon Molodogvardeyskaya 151 Samara 443001 Russia

ISBN: (数字)9781510644250

ISBN: (纸本)9781510644250

Medical applications are among the tasks of optical technology. The processing of two-dimensional optical signals and images is an urgent task today. One of the most dangerous eye diseases is diabetic macular retinopathy. The first stage in the laser coagulation operation is the stage of fundus image segmentation. The calculation of texture features for solving this problem takes a lot of time. In this paper, we consider the use of a high-performance algorithm for calculating texture features based on distributed computing to speed up the processing and analysis of medical images. Various use cases of the high-performance algorithm on a single node were investigated and compared with sequential and parallel algorithms. The high-performance algorithm achieves a 40x speedup and more under some parameters. Using a high-performance algorithm, analysis and segmentation is performed in less than 1 minute for standard images. The use of a high-performance algorithm for the analysis and segmentation of fundus images avoids the need for a sequential skip-step algorithm, which, due to interpolation, reduces the execution time, but at the same time, accuracy is lost.

关键词： fundus images image processing laser coagulation image segmentation eye retina texture features high-performance algorithm

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Refined Cryptanalysis of the GPRS Ciphers GEA-1 and GEA-2 41st

Refined Cryptanalysis of the GPRS Ciphers GEA-1 and GEA-2

引用

41st Annual international conference on the Theory and applications of Cryptographic techniques (Eurocrypt)

作者： Amzaleg, Dor Dinur, Itai Ben Gurion Univ Negev Dept Comp Sci Beer Sheva Israel

ISBN: (纸本)9783031070822;9783031070815

At EUROCRYPT 2021, Beierle et al. presented the first public analysis of the GPRS ciphers GEA-1 and GEA-2. They showed that although GEA-1 uses a 64-bit session key, it can be recovered with the knowledge of only 65 bits of keystream in time 2(40) using 44 GiB of memory. The attack exploits a weakness in the initialization process of the cipher that was presumably hidden intentionally by the designers to reduce its security. While no such weakness was found for GEA-2, the authors presented an attack on this cipher with time complexity of about 2(45). The main practical obstacle is the required knowledge of 12800 bits of keystream used to encrypt a full GPRS frame. Variants of the attack are applicable (but more expensive) when given less consecutive keystream bits, or when the available keystream is fragmented (it contains no long consecutive block). In this paper, we improve and complement the previous analysis of GEA-1 and GEA-2. For GEA-1, we devise an attack in which the memory complexity is reduced by a factor of about 2(13) = 8192 from 44 GiB to about 4 MiB, while the time complexity remains 2(40). Our implementation recovers the GEA-1 session key in average time of 2.5 h on a modern laptop. For GEA-2, we describe two attacks that complement the analysis of Beierle et al. The first attack obtains a linear tradeoff between the number of consecutive keystream bits available to the attacker (denoted by l) and the time complexity. It improves upon the previous attack in the range of (roughly) l <= 7000. Specifically, for l = 1100 the complexity of our attack is about 2(54), while the previous one is not faster than the 2(64) brute force complexity. In case the available keystream is fragmented, our second attack reduces the memory complexity of the previous attack by a factor of 512 from 32 GiB to 64 MiB with no time complexity penalty. Our attacks are based on new combinations of stream cipher crypt-analytic techniques and algorithmic techniques used in other con

关键词： parallel processing systems

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Evaluating the performance and improving the usability of parallel and distributed Word Embeddings tools 28

Evaluating the performance and improving the usability of pa...

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28th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： da Silva, Matheus L. Meyer, Vinicius Kirchoff, Dionatra F. Nero, Joaquim F. S. Vieira, Renata De Rose, Cesar A. F. Pontific Catholic Univ Rio Grande PUCKS Polytech Sch Ipiranga Ave 6681 BR-90619900 Porto Alegre RS Brazil

ISBN: (纸本)9781728165820

The representation of words by means of vectors, also called Word Embeddings (WE), has been receiving great attention from the Natural Language processing (NLP) field. WE models are able to express syntactic and semantic similarities, as well as relationships and contexts of words within a given corpus. Although the most popular implementations of WE algorithms present low scalability, there are new approaches that apply High-Performance Computing (HPC) techniques. This is an opportunity for an analysis of the main differences among the existing implementations, based on performance and scalability metrics. In this paper, we present a study which addresses resource utilization and performance aspects of known WE algorithms foetid in the literature. To improve scalability and usability we propose a wrapper library for local and remote execution environments that contains a set of optimizations such as the pWord2vec, pWord2vec_MPI, Wang2vec and the original Word2vec algorithm. Utilizing these optimizations it is possible to achieve an average performance gain of 15x for multicores and 105x for multinodes compared to the original version. There is also a big reduction in the memory footprint compared to the most popular python versions.

关键词： Natural Language processing Word2vec Performance Evaluation

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Comparative Study of Single MAC FIR Filter Architectures with Different Multiplication techniques

Comparative Study of Single MAC FIR Filter Architectures wit...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), international conference on

作者： Dhandapani Vaithiyanathan Britto Pari James Karuthapandian Mariammal Department of ECE National Institute of Technology Delhi Delhi India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics Engineering Madras Institute of Technology Anna University Chennai India

Emerging technologies in VLSI signal processing systems demand FIR filters' optimal design to support a wide range of applications. This study presents the architectures for single-channel and multichannel FIR filters employing the Time-division multiplexing (TDM) scheme. The studied architecture is associated with one multiplication and addition unit to handle a wide range of channels and filter taps to have efficient resource utilization. Further accumulator-based Radix-4 multiplier, shift and add multiplication, and parallel pipelined multiplication operations involved in the architectures effectively utilize the resources to a considerable extent. The studied 16-tap multiple channel FIR filter design is simulated using Verilog Hardware Description Language (HDL) and synthesis is carried out using Xilinx Vertex Field Programmable Gate Array (FPGA). In addition, single multiply-accumulate (MAC) based FIR filter architectures with different multiplication-based approaches are implemented, and the results are reported. The analysis and synthesis results conclude that the studied 16 taps single MAC FIR structure offers area (slices) optimization of about 89.6% when examining with the conventional parallel MAC FIR filter structure. Similarly, the 16-tap single MAC multichannel structure offers area (slices) minimization of about 90.01 % over the corresponding parallel MAC multichannel implementation. Further, the single MAC structure with a single-channel employing OPC (Output Product Coding) scheme offers 95% area reduction and 86% speed increment when compared to the parallel MAC structure with single-channel implementation. Also, the single MAC multichannel design with the OPC scheme offers 19.84% SDP (slice delay product) optimization when compared to the other studied architecture.

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Real-Time applications of Sensor Analytics for Production and Injection Profiling

Real-Time Applications of Sensor Analytics for Production an...

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Abu Dhabi international Petroleum Exhibition and conference 2022, ADIPEC 2022

作者： Cerrahoglu, Cagri Alyan, Mohand Chavez, Alberto Mendoza Delfino, Alessandro Sundin, Martin Othman, Alaa Kiyoumi, Ahmed Altamimi, Ahmed Parihar, Shardul Fonseca, Rahul-Mark LYTT ADNOC United Arab Emirates

ISBN: (纸本)9781613998724

This paper introduces a new sensor analytics application for flow profiling that is based on physics-informed machine learning (ML) techniques. The application was utilized in the interpretation of point and distributed sensor data acquired from a carbonate reservoir in the Middle East and the results enhanced the confidence in the utilization of fiber-based flow monitoring solutions. The application was tested on two data sets: (1) a distributed fiber optic sensing (DFOS) data set acquired during acid stimulation and water injection periods from an injector and (2) a point acoustic sensor data set acquired from an oil producer. In both cases, the output of the application was qualified using independent measurements. A combination of ML and first-principles models has been used to develop the real-time sensor analytics application. Over 1500 data points were acquired in laboratory conditions where flow conditions were simulated. Various ML models were trained on the labelled data from these experiments to provide flow diagnostics for the subject well completion type and those with highest accuracy were selected for development. Independently interpreted production and petrophysical logs were used in the qualification process to validate the interpretation results obtained with the developed sensor analytics technology. The results from both cases agreed with the results from the qualifying measurements. In the first case, the production log and the DFOS measurements were taken under two different injection conditions. Hence, some discrepancies were observed between the two which were explained by the heterogeneity within the reservoir section. In the second case, data from the point acoustic sensor and the production logging (PL) tool were acquired under the same conditions. The flow profiles predicted by the ML model applied on the point acoustic sensor data agreed with the production log. Copyright © 2022, Society of Petroleum Engineers.

关键词： Well completion

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Demand-Driven Data Provisioning in Data Lakes 23

Demand-Driven Data Provisioning in Data Lakes

引用

23rd international conference on Information Integration and Web Intelligence, iiWAS 2021

作者： Stach, Christoph Bräcker, Julia Eichler, Rebecca Giebler, Corinna Mitschang, Bernhard Institute for Parallel and Distributed Systems University of Stuttgart Germany Institute of Biochemistry and Technical Biochemistry University of Stuttgart Germany

ISBN: (纸本)9781450395564

Data has never been as significant as it is today. It can be acquired virtually at will on any subject. Yet, this poses new challenges towards data management, especially in terms of storage (data is not consumed during processing, i. e., the data volume keeps growing), flexibility (new applications emerge), and operability (analysts are no IT experts). The goal has to be a demand-driven data provisioning, i. e., the right data must be available in the right form at the right time. Therefore, we introduce a tailorable data preparation zone for Data Lakes called BARENTS. It enables users to model in an ontology how to derive information from data and assign the information to use cases. The data is automatically processed based on this model and the refined data is made available to the appropriate use cases. Here, we focus on a resource-efficient data management strategy. BARENTS can be embedded seamlessly into established Big Data infrastructures, e. g., Data Lakes. © 2021 ACM.

关键词： Lakes

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SCGraph: Accelerating Sample-based GNN Training by Staged Caching of Features on GPUs

SCGraph: Accelerating Sample-based GNN Training by Staged Ca...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Yuqi He Zhiquan Lai Zhejiang Ran Lizhi Zhang Dongsheng Li National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

Graph neural networks (GNNs) have been becoming important tools for processing structured graph data and successfully applied to multiple graph-based application scenarios. The existing GNN systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead of transferring vertex features between CPUs and GPUs is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. SCGraph classifies the graph vertices sorted by out-degrees. For high out-degree vertices, SCGraph sets grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, SCGraph expands training vertices' neighborhood in advance to regenerate cache. We evaluate SCGraph against two state-of-the-art industrial GNN frameworks, i.e., DGL and PaGraph on various benchmarks. Experimental results show that SCGraph improves the cache hit rate over GPUs up to 23.6%, and achieves up to 1.71x performance speedup over the state-of-the-art baselines while the convergence almost constant.

关键词： Training Memory management Loading Graphics processing units Benchmark testing Graph neural networks Data communication

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Aperiodic Local SGD: Beyond Local SGD 22

Aperiodic Local SGD: Beyond Local SGD

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Proceedings of the 51st international conference on parallel processing

作者： Hao Zhang Tingting Wu Siyao Cheng Jie Liu Harbin Institute of Technology China Harbin Institute of Technology (Shenzhen) China

ISBN: (纸本)9781450397339

Variations of stochastic gradient decedent (SGD) methods are at the core of training deep neural network models. However, in distributed deep learning, where multiple computing devices and data segments are employed in the training process, the performance of SGD can be significantly limited by the overhead of gradient communication. Local SGD methods are designed to overcome this bottleneck by averaging individual gradients trained over parallel workers after multiple local iterations. Currently, both for theoretical analyses and for practical applications, most studies employ periodic synchronization scheme by default, while few of them focus on the aperiodic schemes to obtain better performance models with limited computation and communication overhead. In this paper, we investigate local SGD with an arbitrary synchronization scheme to answer two questions: (1) Is the periodic synchronization scheme best? (2) If not, what is the optimal one? First, for any synchronization scheme, we derive the performance boundary with fixed overhead, and formulate the performance optimization under given computation and communication constraints. Then we find a succinct property of the optimal scheme that the local iteration number decreases as training continues, which indicates the periodic one is suboptimal. Furthermore, with some reasonable approximations, we obtain an explicit form of the optimal scheme and propose Aperiodic Local SGD (ALSGD) as an improved substitute for local SGD without any overhead increment. Our experiments also confirm that with the same computation and communication overhead, ALSGD outperforms local SGD in performance, especially for heterogeneous data.

关键词： computation and communication cost federated learning distributed learning local SGD data heterogeneity

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Efficient Stencil Computation with Temporal Blocking by Halide DSL

Efficient Stencil Computation with Temporal Blocking by Hali...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Hiroki Aikawa Toshio Endo Tomoya Yuki Takahiro Hirofuchi Tsutomu Ikegami Tokyo Institute of Technology Tokyo Japan AIST Tokyo Japan

The stencil computation is a memory centric computation, where memory access becomes a bottleneck. Therefore locality improvement method like temporal blocking is essential to improve the speed. Introducing temporal blocking, however, introduces heavy programming costs. This paper proposes a methodology to implement efficient stencil software with temporal blocking easier using the domain-specific language Halide. Generally, boundary conditions, such as Neumann and Dirichlet boundary conditions, are needed in stencil computations. Also user-defined boundary computations may be needed in general fluid dynamics applications. We indicate a pitfall in combining temporal blocking and boundary conditions on Halide and show a solution to improve performance. We also apply our methodology to the Hotspot3D bench-mark from Rodinia benchmark suite and demonstrate 3.5 to 5.0 times performance improvement over the original OpenMP implementation.

关键词： Costs Fluid dynamics Software algorithms Graphics processing units Benchmark testing Programming Boundary conditions

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