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An Intelligent parallel distributed Streaming Framework for near Real-time Science Sensors and High-Resolution Medical Images 21

An Intelligent Parallel Distributed Streaming Framework for ...

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50th international conference on parallel processing (ICPP)

作者： Shivadekar, Samit Mangalagiri, Jayalakshmi Phuong Nguyen Chapman, David Halem, Milton Gite, Rahul Univ Maryland Baltimore Cty Baltimore MD 21228 USA OpenKneck Inc Halethorpe MD USA

ISBN: (纸本)9781450384414

Our goals are to address challenges such as latency, scalability, throughput and heterogeneous data sources of streaming analytics and deep learning pipelines in science sensors and medical imaging applications. We present a prototype Intelligent parallel distributed Streaming Framework (IPDSF) that is capable of distributed streaming processing as well as performing distributed deep training in batch mode. IPDSF is designed to run streaming Artificial Intelligent (AI) analytic tasks using data parallelism including partitions of multiple streams of short time sensing data and high-resolution 3D medical images, and fine grain tasks distribution. We will show the implementation of IPDSF for two real world applications, (i) an Air Quality Index based on near real time streaming of aerosol Lidar backscatter and (ii) data generation of Covid-19 Computing Tomography (CT) scans using deep learning. We evaluate the latency, throughput, scalability, and quantitative evaluation of training and prediction compared against a baseline single instance. As the results, IPDSF scales to process thousands of streaming science sensors in parallel for Air Quality Index application. IPDSF uses novel 3D conditional Generative Adversarial Network (cGAN) training using parallel distributed Graphic processing Units (GPU) nodes to generate realistic 3D high resolution Computed Tomography scans of Covid-19 patient lungs. We will show that IPDSF can reduce cGAN training time linearly with the number of GPUs.

关键词： distributed streaming Data parallelism Deep Learning Lidar backscatter Computed Tomography cGAN

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Heterogeneous Voltage Frequency Scaling of Data-parallel applications for Energy Saving on Homogeneous Multicore Platforms 27th

Heterogeneous Voltage Frequency Scaling of Data-Parallel App...

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27th international European conference on parallel and distributed Computing (Euro-Par)

作者： Bratek, Pawel Szustak, Lukasz Wyrzykowski, Roman Olas, Tomasz Chmiel, Tomasz Czestochowa Tech Univ Dept Comp Sci Dabrowskiego 69 PL-42201 Czestochowa Poland

ISBN: (纸本)9783031061561;9783031061554

In this paper, for the first time, we explore and establish the combined benefits of heterogeneous DVFS (dynamic voltage frequency scaling) control in improving the energy-performance behavior of data-parallel applications on shared-memory multicore systems. We propose to customize the clock frequency individually for the appropriately selected groups of cores corresponding to the diversified time of actual computation. In consequence, the advantage of up to 20% points over the homogeneous frequency scaling is achieved on the ccNUMA server with two 18-core Intel Xeon Gold 6240 containing 72 logical cores in total. The cost and efficiency of the proposed pruning algorithm for selecting heterogeneous DVFS configurations against the brute-force search are verified and compared experimentally.

关键词： Data-parallel applications Energy saving Heterogeneous voltage frequency scaling Multicore ccNUMA

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An Efficient Private Information Retrieval Protocol Based on TFHE 19th

An Efficient Private Information Retrieval Protocol Based on...

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19th international conference on Security and Privacy in Communication Networks

作者： Tian, Haibo Lin, Yini Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China

ISBN: (纸本)9783031649479;9783031649486

Private Information Retrieval (PIR) allows a user to query an entry from a database without revealing the index of the entry to the database owner. It is a building tool for many privacy enhancement applications, such as compromised credential checking and stock value query. However, the efficiency of PIR protocols has always been a bottleneck in their practical deployment. To address the challenge, we propose leveraging the fully homomorphic encryption over the torus (TFHE) scheme, which supports implementation on 32-bit unsigned integers and features a fast controlled selector (CMux) gate. The CMux gate can naturally construct a table lookup algorithm to serve PIR. We introduce compression and expansion algorithms for a PIR query, resulting in an encrypted query only a few hundred bytes in size. Further, we present a parallel PIR protocol and four simultaneous table lookup (STLU) algorithms based on TFHE, which are implemented on graphics processing units (GPUs) and make a distributed PIR service with a fast response time. On four servers with five GPUs, we obtain a PIR service with a response time of less than 2 seconds and a query size of less than 200 bytes on a database containing 2(22) entries, with each entry encoding a message of at least 256 bytes.

关键词： private information retrieval fully homomorphic encryption over the torus simultaneous table lookup graphics processing units

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A Performance Analysis of Autovectorization on RVV RISC-V Boards

A Performance Analysis of Autovectorization on RVV RISC-V Bo...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Lorenzo Carpentieri Mohammad VazirPanah Biagio Cosenza Department of Computer Science University of Salerno Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The RISC-V instruction set architecture has become increasingly popular due to its open source and extensible design, making it a competitive choice in high-performance computing and embedded systems. The RISC-V Vector extension (RVV) empowers RISC-V processors with length-agnostic vectorization capabilities, a critical feature for efficiently handling parallel processing demands across different hardware. Compiler support for autovectorization allows to generate vector instructions automatically without requiring any effort to programmers. Given the limited yet evolving compiler support for RVV, this paper offers an in-depth examination of autovectorization capabilities in GCC and LLVM, for RVV version 0.7 and 1.0. We evaluated the autovectorization performance of LLVM, LLVM-EPI and GCC compilers across 151 loops from the Test Suite for Vectorizing Compilers (TSVC) ans seven real-world applications on the AllWinner D1 and BananaPi-F3 boards, representing RISC-V vector hardware. Our study focuses on quantifying and comparing the level of vectorization each compiler achieves across a diverse range of vectorization patterns and workloads, providing insight into their strengths and limitations with respect to RISC-V RVV. Our findings highlight that the LLVM-19 compiler outperforms GCC-14 in 76 out of 151 loops, and its performance is more sensitive to the selection of vector length. Additionally, tuning the vector Length Multiplier (LMUL) parameter can lead to performance improvements of up to $3 x$, and leveraging knowledge of the vector length can further enhance LMUL optimization in compilers.

关键词： Embedded systems Instruction sets High performance computing parallel processing Vectors Hardware Registers Performance analysis Tuning Optimization

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Enhancing Resolution of Inferring Hi-C Data Integrating U-Net and ResNet Networks 23rd

Enhancing Resolution of Inferring Hi-C Data Integrating U-Ne...

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23rd international conference on parallel and distributed Computing, applications, and Technologies, PDCAT 2022

作者： Zhao, Fengjiao Li, Na Zhong, Cheng School of Computer Electronics and Information Guangxi University Guangxi Nanning530004 China Key Laboratory of Parallel and Distributed Computing Technology in Guangxi Universities Guangxi Nanning530004 China

ISBN: (纸本)9783031299261

The Hi-C data is the basis of three-dimensional chromosome structure reconstruction. In this paper, a method integrating U-Net and ResNET network is proposed to infer high resolution Hi-C data from existing low-resolution Hi-C data. Firstly, the U-Net is used as the backbone network to make full use of the shallow layer details and deep layer essential features of Hi-C data. Secondly, the residual module is introduced into the coding path and decoding path of the network to increase the depth of the model and prevent network degradation. Finally, the down sampling is used to reduce the calculation amount to decrease the required running time for the network model. The experimental results on real datasets showed that the proposed method had better performance than existing methods, and the inferred high resolution Hi-C data was closer to the real Hi-C data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chromosomes

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A Performance Model of In-Situ Techniques

A Performance Model of In-Situ Techniques

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Yi Ju Nicolas Vidal Adalberto Perez Ana Gainaru Fred Suter Stefano Markidis Philipp Schlatter Scott Klaskyll Erwin Laure Max Planck Computing and Data Facility Oak Ridge National Laboratory Friedrich-Alexander-Universität Erlangen-Nürnberg KTH Royal Institute of Technology

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The computational capacity of High-Performance Computing (HPC) systems increases continuously with the rapid development of central processing units (CPUs) and graphic processing units (GPUs), while the in-/output (IO) subsystem develops relatively slowly and storage capacity is also limited. Data-intensive applications, which are designed to leverage the high computational capacity of HPC resources, typically generate a considerable amount of data for post-processing visualizations and data analytics. The limited IO speed and storage space could lead to constraints in the actual performance of these applications and, therefore, scientific discovery. In-situ techniques, where data is visualized/analysed while still in memory rather than through disk, can contribute to alleviating these problems as they can reduce or even fully avoid data writing/reading through the IO subsystem to/from storage. However, the overall efficiency of insitu techniques crucially depends on the characteristics of both the in-situ tasks and the applications, and the resource distribution among them. Therefore, choosing the right in-situ approach (synchronous, asynchronous, or hybrid) and resource allocation is essential to minimize overhead and maximize the benefits of concurrent execution. In this paper, we present a performance model of in-situ techniques to find the most beneficial in-situ approach and the preferred resource configuration. We verify the high accuracy of our approach with over 6800 measurements and provide use cases with different applications.

关键词： Lead acid batteries Data analysis Accuracy High performance computing Graphics processing units Data visualization Central processing Unit Resource management

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High-Performance Spatial Data Compression for Scientific applications 28th

High-Performance Spatial Data Compression for Scientific App...

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28th international conference on parallel and distributed Computing (Euro-Par)

作者： Kriemann, Ronald Ltaief, Hatem Minh Bau Luong Perez, Francisco E. Hernandez Im, Hong G. Keyes, David Max Planck Inst Math Sci Leipzig Germany KAUST Extreme Comp Res Ctr Thuwal Saudi Arabia KAUST Clean Combust Res Ctr Thuwal Saudi Arabia

ISBN: (纸本)9783031125973;9783031125966

We implement an efficient data compression algorithm that reduces the memory footprint of spatial datasets generated during scientific simulations. Storing regularly these datasets is typically needed for checkpoint/restart or for post-processing purposes. Our lossy compression approach, codenamed HLRcompress (https://***. de/rok/HLRcompress), combines a hierarchical low-rank approximation technique with binary compression. This novel hybrid method is agnostic to the particular domain of application. We study the impact of HLRcompress on accuracy using synthetic datasets to demonstrate the software capabilities, including robustness and versatility. We assess different algebraic compression methods and report performance results on various parallel architectures. We then integrate it into a workflow of a direct numerical simulation solver for turbulent combustion on distributed-memory systems. We compress the generated snapshots during time integration using accuracy thresholds for each individual chemical species, without degrading the practical accuracy of the overall pressure and temperature. We eventually compare against state-of-the-art compression software. Our implementation achieves on average greater than 100-fold compression of the original size of the datasets.

关键词： Algebraic/Binary Compression Scientific Datasets Hierarchical Matrices

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Evaluating and Analyzing Irregular Tree Search in the Tascell and HOPE parallel Programming Languages 23rd

Evaluating and Analyzing Irregular Tree Search in the Tas...

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23rd international conference on parallel and distributed Computing, applications, and Technologies, PDCAT 2022

作者： Shiki, Yusuke Yasugi, Masahiro Hiraishi, Tasuku Kyushu Institute of Technology Fukuoka Japan Kyoto Tachibana University Kyoto Japan

ISBN: (纸本)9783031299261

Well-designed parallel programming languages are useful for safely parallelizing irregular tree search. The Tascell language can speed up a tree search with work stealing;every Tascell worker mostly performs a computation sequentially, promoting long-term sequential (re)use of workspaces. We comprehensively examine techniques for reusing a workspace among siblings of a depth-first search, sequential update of a single partial solution in a backtracking search, and direct merging of search results. The HOPE fault-tolerant language can also speed up a tree search;every HOPE worker executes the entire computation redundantly and sequentially in its own planned order and exchanges partial results sophisticatedly at runtime to omit redundant subcomputations. For HOPE, we propose an adaptive workspace-sharing technique for unmerged distinct search results. In this study, we evaluate and analyze individual techniques by using the UTS benchmark and multiple backtracking search algorithms, optionally combined with histogram generation;Tascell and HOPE show better performance than Cilk Plus and OpenMP. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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Towards an Autoscaling Service for Real-Time Online Interactive applications on Clouds

Towards an Autoscaling Service for Real-Time Online Interact...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Sezar Jarrous-Holtrup Jona Abdinghoff Folker Schamel Sergei Gorlatch University of Muenster Muenster Germany Spinor GmbH Munich Germany

We develop a novel autoscaling service (autoscaler) to provide elasticity for Real-Time Online Interactive applications (ROIA) running on clouds for thousands of concurrent users. High-performance ROIA include real-time 3D product configurators, multiplayer online gaming, digital twins for the industry 4.0 market, and e-learning. Using our autos caler for ROIA on clouds facilitates meeting high demands on Quality of Experience (QoE) and the economic utilization of cloud resources. Compared to existing autoscaling solutions (e.g., in Kubernetes), our autoscaler is based not on the classical metrics (CPU/GPU load, memory usage, etc.), but rather on the session slots which limit the number of concurrent sessions for a service instance. We design a novel auto scaling algorithm using linear regression of the session slots usage, and we mathematically analyze and experimentally evaluate autoscaler's dynamic reaction to changing workload while avoiding overswinging (creating more service instances than needed). We also report our preliminary experimental results.

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Halide Code Generation Framework in Phylanx 28th

Halide Code Generation Framework in Phylanx

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28th international European conference on parallel and distributed Computing , Euro-Par 2022

作者： Tohid, R. Shirzad, Shahrzad Taylor, Christopher Sakin, Sayef Azad Isaacs, Katherine E. Kaiser, Hartmut Center for Computation and Technology Louisiana State University Baton Rouge United States Tactical Computing Labs Muenster United States SCI Institute University of Utah Salt Lake City United States

ISBN: (纸本)9783031312083

Separating algorithms from their computation schedule has become a de facto solution to tackle the challenges of developing high performance code on modern heterogeneous architectures. Common approaches include Domain-specific languages (DSLs) which provide familiar APIs to domain experts, code generation frameworks that automate the generation of fast and portable code, and runtime systems that manage threads for concurrency and parallelism. In this paper, we present the Halide code generation framework for Phylanx distributed array processing platform. This extension enables compile-time optimization of Phylanx primitives for target architectures. To accomplish this, (1) we implemented new Phylanx primitives using Halide, and (2) partially exported Halide’s thread pool API to carry out parallelism on HPX (Phylanx’s runtime) threads. (3) showcased HPX performance analysis tools made available to Halide applications. The evaluation of the work has been done in two steps. First, we compare the performance of Halide applications running on its native runtime with that of the new HPX backend to verify there is no cost associated with using HPX threads. Next, we compare performances of a number of original implementations of Phylanx primitives against the new ones in Halide to verify performance and portability benefits of Halide in the context of Phylanx. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Array processing

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