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sNPU: Trusted Execution Environments on Integrated NPUs

sNPU: Trusted Execution Environments on Integrated NPUs

引用

Annual international symposium on Computer Architecture, ISCA

作者： Erhu Feng Dahu Feng Dong Du Yubin Xia Haibo Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University Department of Precision Instrument Tsinghua University Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Key Laboratory of System Software (Chinese Academy of Sciences)

ISBN: (数字)9798350326581

ISBN: (纸本)9798350326598

Trusted execution environment (TEE) promises strong security guarantee with hardware extensions for security-sensitive tasks. Due to its numerous benefits, TEE has gained widespread adoption, and extended from CPU-only TEEs to FPGA and GPU TEE systems. However, existing TEE systems exhibit inadequate and inefficient support for an emerging (and significant) processing unit, NPU. For instance, commercial TEE systems resort to coarse-grained and static protection approaches for NPUs, resulting in notable performance degradation (10%–20%), limited (or no) multitasking capabilities, and suboptimal resource utilization. In this paper, we present a secure NPU architecture, known as sNPU, which aims to mitigate vulnerabilities inherent to the design of NPU architectures. First, sNPU proposes NPU Guarder to enhance the NPU’s access control. Second, sNPU defines new attack surfaces leveraging in-NPU structures like scratchpad and NoC, and designs NPU Isolator to guarantee the isolation of scratchpad and NoC routing. Third, our system introduces a trusted software module called NPU Monitor to minimize the software TCB. Our prototype, evaluated on FPGA, demonstrates that sNPU significantly mitigates the runtime costs associated with security checking (from upto 20% to 0%) while incurring less than 1% resource costs.

关键词： Costs Runtime Prototypes Computer architecture Routing Software Resource management

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Semantically Correct and Intent Defined Commutativity in distributed systems 19

Semantically Correct and Intent Defined Commutativity in Dis...

引用

19th international symposium on parallel and distributed Computing (ISPDC)

作者： Robinson, Courtney Univ Greenwich Dept Comp & Informat Syst London England

ISBN: (纸本)9781728189468

This paper reviews the problem of correctness in the context of scaleable distributed systems. It takes the concept of a strongly eventually consistent (SEC) data structure based on the work done on conflict free replicated data types and examines the theoretical basis of a practical application to generalised database management systems. It reasons that the condition required for a database management system to implement a monotonic semilattice and remain generic is not possible. It further proposes a framework which extends the SEC condition to capture enough information to make such a system practical to implement. Finally, it evaluates such an implementation using a modified storage engine for Apache Ignite demonstrating not just its practicality but its scaleability in Ignite and other database management systems like it. The key contributions of this work are Semantic and intent defined conflict free replicated data types, an enriched CRDT which enables generalised database systems to meet the SEC criteria for a monotonic semilattice object and Semantic Intent Configuration Language, SICL, a language and framework for distributed computing.

关键词： Semantics

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PRACTICE AND EXPERIENCE IN USING parallel AND SCALABLE MACHINE LEARNING IN REMOTE SENSING FROM HPC OVER CLOUD TO QUANTUM COMPUTING

PRACTICE AND EXPERIENCE IN USING PARALLEL AND SCALABLE MACHI...

引用

IEEE international Geoscience and Remote Sensing symposium (IGARSS)

作者： Rieder, Morris Cavallaro, Gabriele Benediktsson, Jon Atli Univ Iceland Sch Engn & Nat Sci Reykjavik Iceland Forschungszentrum Julich Julich Supercomp Ctr Julich Germany

ISBN: (纸本)9781665403696

Using computationally efficient techniques for transforming the massive amount of Remote Sensing (RS) data into scientific understanding is critical for Earth science. The utilization of efficient techniques through innovative computing systems in RS applications has become more widespread in recent years. The continuously increased use of Deep Learning (DL) as a specific type of Machine Learning (ML) for data-intensive problems (i.e., 'big data') requires powerful computing resources with equally increasing performance. This paper reviews recent advances in High-Performance Computing (HPC), Cloud Computing (CC), and Quantum Computing (QC) applied to RS problems. It thus represents a snapshot of the state-of-the-art in ML in the context of the most recent developments in those computing areas, including our lessons learned over the last years. Our paper also includes some recent challenges and good experiences by using Europeans fastest supercomputer for hyper-spectral and multi-spectral image analysis with state-of-the-art data analysis tools. It offers a thoughtful perspective of the potential and emerging challenges of applying innovative computing paradigms to RS problems.

关键词： High performance computing cloud computing quantum computing machine learning deep learning parallel and distributed algorithms remote sensing

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Large Language Model-Based Optimization for System-Level Test Program Generation

Large Language Model-Based Optimization for System-Level Tes...

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IEEE international symposium on Defect and Fault Tolerance in VLSI systems

作者： Denis Schwachhofer Peter Domanski Steffen Becker Stefan Wagner Matthias Sauer Dirk Pflüger Ilia Polian Institute of Computer Engineering and Computer Architecture University of Stuttgart Stuttgart Germany Institute of Software Engineering University of Stuttgart Stuttgart Germany Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany Technical University of Munich Heilbronn Germany Advantest Europe Boeblingen Germany

ISBN: (数字)9798350366884

ISBN: (纸本)9798350366891

System-Level Test (SLT) is essential for testing integrated circuits, focusing on functional and non-functional properties of the Device under Test (DUT). Traditionally, test engineers manually create tests with commercial software to simulate the DUT's end-user environment. This process is both time-consuming and offers limited control over non-functional properties. This paper proposes Large Language Models (LLMs) enhanced by Structural Chain of Thought (SCoT) prompting, a temperature schedule, and a pool of previously generated snippets to generate high-quality code snippets for SLT. We repeatedly query the LLM for a better snippet using previously generated snippets as examples, thus creating an iterative optimization loop. This approach can automatically generate snippets for SLT that target specific non-functional properties, reducing time and effort. Our findings show that this approach improves the quality of the generated snippets compared to unstructured prompts containing only a task description.

关键词： Measurement Schedules Codes Temperature Process control Very large scale integration Software Optimization Nanotechnology Testing

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G-Morph: Induced Subgraph Isomorphism Search of Labeled Graphs on a GPU 27th

G-Morph: Induced Subgraph Isomorphism Search of Labeled Grap...

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

作者： Rowe, Bryan Gupta, Rajiv Univ Calif Riverside CSE Dept Riverside CA 92521 USA

ISBN: (纸本)9783030856656;9783030856649

Induced subgraph isomorphism search finds the occurrences of embedded subgraphs within a single large data graph that are strictly isomorphic to a given query graph. Labeled graphs contain object types and are a primary input source to this core search problem, which applies to systems like graph databases for answering queries. In recent years, researchers have employed GPU parallel solutions to this problem to help accelerate runtimes by utilizing the filtering-and-joining framework, which first filters vertices that cannot be part of the solution then joins partial solutions with candidate edges until full isomorphisms are determined. However, the performance of current GPU-based solutions is hindered by limited filtering effectiveness and presence of extraneous computations. This paper presents G-Morph, a fast GPU-based induced subgraph isomorphism search system for labeled graphs. Our filtering-and-joining system parallelizes both phases by upfront eliminating irrelevant vertices via a novel space-efficient vertex signature hashing strategy and efficiently joining partial solutions through use of a novel sliding window algorithm (slide-join) that provides overflow protection for larger input. Together these techniques greatly reduce extraneous computations by reducing Cartesian products and improving edge verification while supporting large scan operations (split-scan). G-Morph outperforms the state-of-the-art GPU-based GSI and CPU-based VF3 systems on labeled real-world graphs achieving speedups of up to 15.78x and 43.56x respectively.

关键词： Graph databases Subgraph isomorphism Breadth-first search GPU programming parallel computing

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Performance Comparision of TPU, GPU, CPU on Google Colaboratory over distributed Deep Learning 14

Performance Comparision of TPU, GPU, CPU on Google Colaborat...

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14th IEEE international symposium on Embedded Multicore/Many-Core systems-on-Chip (MCSoC)

作者： Kimm, Haklin Paik, Incheon Kimm, Hanke East Stroudsburg Univ Dept Comp Sci East Stroudsburg PA 18301 USA Univ Aizu Sch Comp Sci & Engn Fukushima Japan SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

ISBN: (纸本)9781665438605

Deep Learning models need massive amounts compute powers and tend to improve performance running on special purpose processors accelerators designed to speed up compute-intensive applications. The accelerators like Tensor Processing Units (TPUs) and Graphics Processing Units (GPUs) are widely used as deep learning hardware platforms which can often achieve better performance than CPUs, with their massive parallel execution resources and high memory bandwidth. Google Colaboratory known as Colab is a cloud service based on Jupyter Notebook that allows the users to write and execute mostly Python in a browser and admits free access to TPUs and GPUs without extra configuration need, which are widely available cloud hardware platforms. In this paper, we present a through comparison of the hardware platforms on Google Colab that is benchmarked with distributed Bidirectional Long Short-Term Memory (dBLSTM) models upon the number of layers, the number of units each layer, and the numbers of input and output units the datasets. Human Activity Recognition (HAR) data from UCI machine-learning library have been applied to the proposed distributed bidirectional LSTM model to find the performance, strengths, bottlenecks of the hardware platforms of TPU, GPU and CPU upon hyperparameters, execution time, and evaluation metrics: accuracy, precision, recall and F1 score.

关键词： distributed Deep Learning Google Colab TPU and GPU Human Activity Recognition Bidirectional LSTM

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An Optical Transceiver Reliability Study based on SFP Monitoring and OS-level Metric Data

An Optical Transceiver Reliability Study based on SFP Monito...

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IEEE/ACM international symposium on Cluster Computing and the Grid (CCGRID)

作者： Paolo Notaro Qiao Yu Soroush Haeri Jorge Cardoso Michael Gerndt Huawei Technologies Duesseldorf GmbH Munich Germany Chair of Computer Architecture and Parallel Systems Technical University of Munich Germany Chair of Distributed and Operating Systems Technical University of Berlin Germany Department of Informatics Engineering/CISUC University of Coimbra Portugal

The increasing demand for cloud computing drives the expansion in scale of datacenters and their internal optical network, in a strive for increasing bandwidth, high reliability, and lower latency. Optical transceivers are essential elements of optical networks, whose reliability has not been well-studied compared to other hardware components. In this paper, we leverage high quantities of monitoring data from optical transceivers and OS-level metrics to provide statistical insights about the occurrence of optical transceiver failures. We estimate transceiver failure rates and normal operating ranges for monitored attributes, correlate early-observable patterns to known failure symptoms, and finally develop failure prediction models based on our analyses. Our results enable network administrators to deploy early-warning systems and enact predictive maintenance strategies, such as replacement or traffic re-routing, reducing the number of incidents and their associated costs.

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SeqDLM: A Sequencer-Based distributed Lock Manager for Efficient Shared File Access in a parallel File System

SeqDLM: A Sequencer-Based Distributed Lock Manager for Effic...

引用

Supercomputing Conference

作者： Qi Chen Shaonan Ma Kang Chen Teng Ma Xin Liu Dexun Chen Yongwei Wu Zuoning Chen Tsinghua University China Alibaba Inc China National Supercomputing Center in Wuxi China Chinese Academy of Engineering China

ISBN: (纸本)9781665454452

distributed locks are used to guarantee the distributed client-cache coherence in parallel file systems. However, they lead to poor performance in the case of parallel writes under high-contention workloads. We analyze the distributed lock manager and find out that lock conflict resolution is the root cause of the poor performance, which involves frequent lock revocations and slow data flushing from client caches to data servers. We design a distributed lock manager named SeqDLM by exploiting the sequencer mechanism. SeqDLM mitigates the lock conflict resolution overhead using early grant and early revocation while keeping the same semantics as traditional distributed locks. To evaluate SeqDLM, we have implemented a parallel file system called ccPFS using both SeqDLM and traditional distributed locks. Evaluations on 96 nodes show SeqDLM outperforms the traditional distributed locks by up to $\boldsymbol{10.3}\times$ for high-contention parallel writes on a shared file with multiple stripes.

关键词： File systems High performance computing Semantics distributed databases Coherence Servers

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NUSS:Non-Interactive Updatable of Secret Sharing Schemes Using Homomorphic Encryption

NUSS:Non-Interactive Updatable of Secret Sharing Schemes Usi...

引用

international symposium on Computing and Networking Workshops (CANDARW)

作者： Yixuan He Samsul Huda Yuta Kodera Yasuyuki Nogami Graduate School of Environmental Life Natural Science and Technology Okayama University Okayama Japan

ISBN: (数字)9798331505349

ISBN: (纸本)9798331505356

Secret sharing schemes (SSS) are essential for distributed cryptography due to their role in safeguarding sensitive data. They face significant challenges in maintaining longterm security, especially in dynamic environments that require frequent updates to shares, such as in cloud storage systems where data is continually modified and accessed by multiple parties. While proactive secret sharing (PSS) addresses this issue through periodic share renewal, current methods necessitate extensive inter-party communication, resulting in high bandwidth usage and increased vulnerability to synchronization attacks. Moreover, these interactive update processes risk leaking sensitive information, including threshold structures. Motivated by these challenges, we propose NUSS (Non-Interactive Updatable Secret Sharing), a novel approach leveraging homomorphic encryption for non-interactive updates of secret shares. Our method encrypts shared arrays homomorphically, enabling share updates within the encrypted domain without requiring share regeneration and redistribution. Specifically, the update process is conducted via homomorphic computation, which works directly on the ciphertext. This approach ensures data security and minimizes communication complexity, allowing each party to complete the update process independently using encrypted share arrays. Experimental evaluation confirms that NUSS effectively mitigates the risk of information leakage compared to traditional proactive secret sharing. Additionally, our proposal requires only n secure channels, significantly reducing communication costs by lowering the number of secure channels required from O(n 2 ) in traditional PSS to O(n).

关键词： Protocols distributed databases Information leakage Robustness Cryptography Proposals Arrays Synchronization Homomorphic encryption Faces

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MUAR: Maximizing Utilization of Available Resources for Query Processing

MUAR: Maximizing Utilization of Available Resources for Quer...

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Cluster, Cloud and Internet Computing Workshops (CCGridW), IEEE/ACM international symposium on

作者： Mayank Patel Minal Bhise Distributed Databases Group DA-IICT Gandhinagar Gujarat India

Processing large datasets requires significant hardware resources and energy. Researchers have observed that most database management systems could not utilize available resources efficiently, increasing data to result time and application running costs. This research explores techniques that can maximize the utilization of available resources to efficiently process large datasets on limited resource systems. The work implemented single and multiple resource maximization techniques and observed improvements in total workload execution time (WET). Results showed that combining CPU and RAM resource maximization techniques can reduce WET by 61-81% compared to the original WET observed with default resource allocation configuration. This work proposes a lightweight real-time resource allocation and task scheduling algorithm MUAR (Maximizing Utilization of Available Resources). It maximizes the utilization of available resources considering the real-time availability of resources and workload task complexity. The algorithm identifies complex multi-join queries and allocates maximum available resources for faster execution. MUAR is capable of estimating work memory value with 15-20% error required to achieve the best query performance with only single query run data. A comparison of MUAR with machine learning-based techniques like PCC and AutoToken is also presented.

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