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iNUMAlloc: Towards Intelligent Memory Allocation for AI Accelerators with NUMA 21

iNUMAlloc: Towards Intelligent Memory Allocation for AI Acce...

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21st IEEE international symposium on parallel and distributed Processing with Applications, 13th IEEE international Conference on Big Data and Cloud computing, 16th IEEE international Conference on Social computing and Networking and 13th international Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xu, Yuanchao Qian, Ruyi Wang, Yida Huo, Qirun Capital Normal University College of Information Engineering Beijing China Skl of Computer Architecture Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9798350329223

The amazing success of deep neural network benefits from the rise of big data. As deep learning models are becoming more scale than ever before, their requirements for memory bandwidth are growing at a tremendous pace. Some AI accelerators adopt non-uniform memory access (NUMA) architecture to mitigate this issue and hence complicate device memory allocation. Although extensive studies have been conducted on how to mitigate resource contention and reduce latency, almost all of them target on CPU-oriented NUMA systems but not on AI accelerators where memory allocation precedes task scheduling. The current memory allocator generally adopts an interleaved memory allocation strategy, which is very easy to implement but far from *** tackle this issue, this paper proposes iNUMAlloc, an intelligent memory allocator specialized for AI accelerators with NUMA architecture by combining program behavior and predictable hardware resources altogether. Preliminary evaluation shows that it can help to improve the accuracy and efficiency of memory allocation, thereby achieving stable execution time. © 2023 IEEE.

关键词： Deep neural networks

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Efficient Hybrid Fuzzing with Syntax-aware Input Trim Based on Lightweight Taint Analysis 21

Efficient Hybrid Fuzzing with Syntax-aware Input Trim Based ...

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作者： Gao, Long Zhao, Yiru Wan, Qihan Zhao, Lei Wuhan University China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering China

ISBN: (纸本)9798350329223

Hybrid fuzzing as an automated vulnerability detection technique, has gained widespread attention in recent years. It combines the advantages of fuzzing and concolic execution. Yet, existing hybrid fuzzing techniques face practical problems such as the high performance overhead of concolic execution and the difficulty of utilizing the results of the solution in fuzzing. We noted that concolic execution often falls into repetitive loop structures when analyzing testcases generated by fuzzing, leading to the constant construction of redundant constraint expressions, which severely impacts the efficiency of concolic execution. Based on this observation, we design a testcases trimming method to improve the efficiency of concolic execution. The key idea is to use lightweight taint analysis to infer and restore the syntactic structure of the testcase, and through the concept of delta debugging, gradually confirm nodes that do not affect the program coverage path, and remove the corresponding data segments from the testcase. We apply our method to hybrid fuzzing, called TrimFuzz. Our evaluation shows that for testing programs that process highly structured data, TrimFuzz brings an average of 8.2% improvement in code coverage and triggers 115 more unique crashes compared to QSYM. © 2023 IEEE.

关键词： Efficiency

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OPTIMAL ASSIGNMENT STRATEGY FOR DYNAMIC WORKFLOW OF REMOTE SENSING BIG DATA PROCESSING

OPTIMAL ASSIGNMENT STRATEGY FOR DYNAMIC WORKFLOW OF REMOTE S...

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IEEE international Geoscience and Remote Sensing symposium (IGARSS)

作者： Zhang, Sheng Xue, Yong Ming, Yang Zhang, Xiaopeng Jin, Chunlin Jiang, Xingxing Zhou, Xiran China Univ Min & Technol Sch Environm & Spatial Informat Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221116 Jiangsu Peoples R China Shandong Prov Inst Land Surveying & Mapping Jinan 250013 Shandong Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

The advent of the era of Remote Sensing Big Data has produced a large number of processing and analysis tasks, which require powerful computing capabilities to support. The computational efficiency of distributed computer clusters which are the most commonly used parallel computing architecture for high performance computing can be significantly improved through an effective task scheduling strategy. In this paper, in order to improve data computing efficiency, we propose a dynamic load balancing strategy for remote sensing data processing workflow tasks based on the Hungarian algorithm for heterogeneous distributed computing clusters. We also compare this strategy with the classic load balancing algorithm. We find that the speed-up effect of the strategy proposed in this paper is better, and the speedups become more pronounced as the number of tasks increases.

关键词： workflow heterogeneous computing platform Hungarian algorithm optimal assignment

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OmpSs@cloudFPGA: An FPGA Task-Based Programming Model with Message Passing 36

OmpSs@cloudFPGA: An FPGA Task-Based Programming Model with M...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Miguel de Haro, Juan Cano, Ruben Alvarez, Carlos Jimenez-Gonzalez, Daniel Martorell, Xavier Ayguade, Eduard Labarta, Jeses Abel, Francois Ringlein, Burkhard Weiss, Beat Barcelona Supercomp Ctr Barcelona Spain Univ Politecn Cataluna Barcelona Spain IBM Res Europe Zurich Switzerland

ISBN: (纸本)9781665481069

Nowadays, a new parallel paradigm for energyefficient heterogeneous hardware infrastructures is required to achieve better performance at a reasonable cost on highperformance computing applications. Under this new paradigm, some application parts are offloaded to specialized accelerators that run faster or are more energy-efficient than CPUs. FieldProgrammable Gate Arrays (FPGA) are one of those types of accelerators that are becoming widely available in data centers. This paper proposes OmpSs@cloudFPGA, which includes novel extensions to parallel task-based programming models that enable easy and efficient programming of heterogeneous clusters with FPGAs. The programmer only needs to annotate, with OpenMP-like pragmas, the tasks of the application that should be accelerated in the cluster of FPGAs. Next, the proposed programming model framework automatically extracts parts annotated with High-Level Synthesis (HLS) pragmas and synthesizes them into hardware accelerator cores for FPGAs. Additionally, our extensions include and support two novel features: 1) FPGA-toFPGA direct communication using a Message Passing Interface (MPI) similar Application Programming Interface (API) with one-to-one and collective communications to alleviate host communication channel bottleneck, and 2) creating and spawning work from inside the FPGAs to their own accelerator cores based on an MPI rank-like identification. These features break the classical host-accelerator model, where the host (typically the CPU) generates all the work and distributes it to each accelerator. We also present an evaluation of OmpSs@cloudFPGA for different parallel strategies of the N-Body application on the IBM cloudFPGA research platform. Results show that for cluster sizes up to 56 FPGAs, the performance scales linearly. To the best of our knowledge, this is the best performance obtained for N-body over FPGA platforms, reaching 344 Gpairs/s with 56 FPGAs. Finally, we compare the performance and power consu

关键词： FPGA MPI OpenMP programming models network-attached FPGA stand-alone FPGA High-Level Synthesis heterogeneous programming High-performance computing

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Contrastive Learning-Based Generic Audio-to-Lyrics Alignment 21

Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

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作者： Jin, Zhenkun Huang, Shuai Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business UIniversity Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on BigData Security Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350329223

In this research, we tackled the complex task of aligning audio and lyrics content automatically. This task entails the precise matching of lyrics with the corresponding audio segments in songs, necessitating the coordination of two distinct modalities: text and audio. To surmount this challenge, we employed a contrastive learning approach that generates cross-modal embeddings capable of establishing connections between the audio and text domains. Furthermore, we adopted a hierarchical and cascading design. Initially, the system predicts synchronization at the sentence level and subsequently refines the alignment at the word level. This design equips the system to handle extended sequences effectively, as cross-correlations leverage secondary memory that corresponds to sequence length. Through this innovative approach, we developed a trainable end-to-end system that can harness weakly annotated training data, concurrently learning a robust text model optimized for alignment tasks. Our experiments demonstrated substantial enhancements in average alignment accuracy when utilizing our proposed system, underscoring its robustness compared to previous state-of-the-art models. © 2023 IEEE.

关键词： Alignment

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computing Everywhere, All at Once: Harnessing the computing Continuum for Science 23

Computing Everywhere, All at Once: Harnessing the Computing ...

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32nd international symposium on High-Performance parallel and distributed computing (HPDC) part of the ACM Federated computing Research Conference (FCRC)

作者： Parashar, Manish Univ Utah Sci Comp & Imaging SCI Inst Salt Lake City UT 84112 USA

ISBN: (纸本)9798400701559

The exponential growth of available digital data sources at the edge, and the ubiquity of non-trivial computational power for processing this data are enabling novel data-driven applications. Emerging scientific workflows are leveraging distributed data sources and the edge-HPC continuum, to understand end-to-end phenomenon, drive experimentation, and facilitate important decision making. However, while our capacity for collecting data is expanding dramatically, our ability to manage, manipulate, and analyze this data, transform it into knowledge and understanding, and integrate it with practice has not kept pace, and realizing such science workflows remain challenging. For example, most popular data analytics solutions, including those based on AI/ML, are cloud-based, and require transporting data from often distant edge devices to a central location for processing. This limits the amount of data that we can process and our ability to analyze and transform this data into knowledge in a timely manner. In this talk I will propose the notion of a continuum computing as a fluid ecosystem where distributed resources and services, at the edge, in the core, in the network, and along the data path, are programmatically aggregated on-demand to support emerging data-driven application workflows [1,2]. Using science usecases, I will also explore key research questions such as: (1) how do we determine what, where, and when data gets collected and analyzed based on application objectives and user/system constraints;(2) how do we program services to respond to changes in application behaviors/objectives, and data availability/content;and (3) how does the application react during execution to events of interest and trigger appropriate actions. Finally, I will describe recent research as part of the R-Pulsar project [3], exploring novel solutions for federating infrastructure, programming services, and composing dynamic workflows that are capable of reacting to data availabilities

关键词： R-Pulsar translational computer science research edge-cloud/HPC integration computing continuum urgent computing

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval 21

Ranking on Heterogeneous Manifold for Multimodal Information...

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作者： Jin, Zhenkun Wan, Xingshi Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350329223

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR. © 2023 IEEE.

关键词： Knowledge representation

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A distributed AND parallel METHOD OF HYPERSPECTRAL COMPUTATIONAL IMAGING VIA COLLABORATIVE TUCKER3 TENSOR DECOMPOSITION

A DISTRIBUTED AND PARALLEL METHOD OF HYPERSPECTRAL COMPUTATI...

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IEEE international Geoscience and Remote Sensing symposium (IGARSS)

作者： Zhang, Ling Wu, Zebin Sun, Jin Xu, Yang Wei, Zhihui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Hyperspectral computational imaging (HCI) is to reconstruct hyperspectral images (HSIs) based on the compressed signals collected by remote sensing and imaging systems. Collaborative Tucker3 tensor decomposition is beneficial for HCI models in reconstructing high-fidelity HSIs. However, the ever-increasing amount of compressed data leads to heavy computation burden for tensor decomposition-based HCI models, which may exceed the computing capacity of a single machine. For this reason, this paper proposes a Spark-based distributed and parallel HCI implementation via collaborative Tucker3 tensor decomposition. The proposed implementation decomposes the processing flow of the HCI algorithm into several stages, each of which can be processed in parallel on Spark. In addition, we develop parallel strategies for improving the performance of the redundant computational procedure and data storage procedure, respectively. Experimental results demonstrate that the parallel algorithm not only achieves high accuracy but also improves the computational efficiency when processing large-scale HSI datasets.

关键词： Hyperspectral computational imaging Spark distributed and parallel processing Tucker3 tensor decomposition

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Automated selection of build configuration based on machine learning 36

Automated selection of build configuration based on machine ...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Furuhata, Reo Zhao, Minglu Takahashi, Keichi Shimomura, Yoichi Takizawa, Hiroyuki Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan Tohoku Univ Cybersci Ctr Sendai Miyagi Japan

ISBN: (纸本)9781665497473

Nowadays, High-Performance computing (HPC) application codes are becoming larger and more complex. There is an increasing demand for optimizing such codes to reduce execution time and hence achieve high performance. Compilers are used to translate the codes into executable programs. Many optimization techniques have already been incorporated into compilers, and the performance gain by each combination of optimization techniques strongly depends on the code. By selecting a compiler and its option flags, programmers can change the compilation process and its behaviors so that an HPC application can achieve high performance. However, it is not easy to express the selection of a compiler and its option flags as an explicit algorithm. So far, a compiler and its option flags have been selected in a trial-and-error fashion, which is labor-intensive, time-consuming, and potentially leading to inappropriate selection. In addition, configuring the build process for more complex and heterogeneous HPC systems, in which different kinds of processors such as accelerators are employed, becomes even more challenging. Therefore, we propose an automatic compilation process configuration approach based on machine learning that uses dynamic information obtained by executing the code. A feature selection method is proposed to remove the irrelevant attributes for the machine learning model. A neural network is used to learn useful features from data to predict the appropriate build configuration for individual application codes. The evaluation results show that the proposed method outperforms other existing methods in the literature. The proposed feature selection helps the machine learning model characterize the application codes more accurately. Furthermore, it is demonstrated that the proposed approach can select the optimal configuration for each of different processors.

关键词： Codes Program processors High performance computing Machine learning Artificial neural networks Predictive models Performance gain

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A Hybrid Blockchain Scheme for Tracing Manufacturing Processes and Trading Transactions 22

A Hybrid Blockchain Scheme for Tracing Manufacturing Process...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Yuan, Ye Yang, Mingxing Xiong, Ruoting Sun, Yani Zhang, Xianchao Ren, Wei HuBei Land Resources Vocational College China China University of Geosciences Gemological Institute Wuhan China University of East Anglia School of Computing Science United Kingdom Yunnan Innovation Institute Beihang University Yunnan Key Laboratory of Blockchain Application Technology Kunming China Jiaxing University Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing China State Key Laboratory of Geo-Information Engineering Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology Mnr Beijing China Ministry of Education China China University of Geosciences School of Computer Science Wuhan China

ISBN: (纸本)9798331509712

Blockchain can be envisioned as an enabling framework in terms of cryptography and distributed computing to construct trusted data records among un-trusted users. It can be applied in two typical scenarios - historical process provenance and transaction tracing, by immutably sequentialized blocks. In the former there exists no tokens;but in the latter token handover among accounts should be cryptographically guaranteed. In some situations, above two applications may be both required. E.g., in the manufacture of a jewel, processing flow by different operators should be recorded for quality guaranteeing, and in its trading stage, the ownership handover should be recorded for value confirmation. To tackle above two requirements, two chains are usually thus demanded. In this paper, we propose a single chain solution to avoid cross-chain burden, by a hybrid block structure. The process data can be imported from the legacy (non-anonymous) information processing system, and transaction data stems from anonymous payment demand for ownership handover. We formally describe and prove token handover model with cryptographical authenticity. We also propose several security enhancements in the design for shortening the length of the chain, and protecting the data confidentiality in the chain. Our design is general and not limited to single application. The experimental results and extensive analysis justified that the performance and security of our proposed scheme is efficient. © 2024 IEEE.

关键词： Data Provenance Data Tracing Hybrid Blockchain Token Handover

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