9 research outputs found

    The Relative Power of Composite Loop Agreement Tasks

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    Loop agreement is a family of wait-free tasks that includes set agreement and simplex agreement, and was used to prove the undecidability of wait-free solvability of distributed tasks by read/write memory. Herlihy and Rajsbaum defined the algebraic signature of a loop agreement task, which consists of a group and a distinguished element. They used the algebraic signature to characterize the relative power of loop agreement tasks. In particular, they showed that one task implements another exactly when there is a homomorphism between their respective signatures sending one distinguished element to the other. In this paper, we extend the previous result by defining the composition of multiple loop agreement tasks to create a new one with the same combined power. We generalize the original algebraic characterization of relative power to compositions of tasks. In this way, we can think of loop agreement tasks in terms of their basic building blocks. We also investigate a category-theoretic perspective of loop agreement by defining a category of loops, showing that the algebraic signature is a functor, and proving that our definition of task composition is the "correct" one, in a categorical sense.Comment: 18 page

    An Empirical Study of Speculative Concurrency in Ethereum Smart Contracts

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    We use historical data to estimate the potential benefit of speculative techniques for executing Ethereum smart contracts in parallel. We replay transaction traces of sampled blocks from the Ethereum blockchain over time, using a simple speculative execution engine. In this engine, miners attempt to execute all transactions in a block in parallel, rolling back those that cause data conflicts. Aborted transactions are then executed sequentially. Validators execute the same schedule as miners. We find that our speculative technique yields estimated speed-ups starting at about 8-fold in 2016, declining to about 2-fold at the end of 2017, where speed-up is measured using either gas costs or instruction counts. We also observe that a small set of contracts are responsible for many data conflicts resulting from speculative concurrent execution

    High-Resolution Convolutional Neural Networks on Homomorphically Encrypted Data via Sharding Ciphertexts

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    Recently, Deep Convolutional Neural Networks (DCNNs) including the ResNet-20 architecture have been privately evaluated on encrypted, low-resolution data with the Residue-Number-System Cheon-Kim-Kim-Song (RNS-CKKS) homomorphic encryption scheme. We extend methods for evaluating DCNNs on images with larger dimensions and many channels, beyond what can be stored in single ciphertexts. Additionally, we simplify and improve the efficiency of the recently introduced multiplexed image format, demonstrating that homomorphic evaluation can work with standard, row-major matrix packing and results in encrypted inference time speedups by 4.6−6.5×4.6-6.5\times. We also show how existing DCNN models can be regularized during the training process to further improve efficiency and accuracy. These techniques are applied to homomorphically evaluate a DCNN with high accuracy on the high-resolution ImageNet dataset, achieving 80.2%80.2\% top-1 accuracy. We also achieve an accuracy of homomorphically evaluated CNNs on the CIFAR-10 dataset of 98.3%98.3\%.Comment: 14 pages, 9 figure

    DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference

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    Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure. Thus, improving the execution efficiency of neural recommendation directly translates into infrastructure capacity saving. In this paper, we devise a novel end-to-end modeling infrastructure, DeepRecInfra, that adopts an algorithm and system co-design methodology to custom-design systems for recommendation use cases. Leveraging the insights from the recommendation characterization, a new dynamic scheduler, DeepRecSched, is proposed to maximize latency-bounded throughput by taking into account characteristics of inference query size and arrival patterns, recommendation model architectures, and underlying hardware systems. By doing so, system throughput is doubled across the eight industry-representative recommendation models. Finally, design, deployment, and evaluation in at-scale production datacenter shows over 30% latency reduction across a wide variety of recommendation models running on hundreds of machines

    A memetic algorithm approach to network alignment: Mapping the classification of mental disorders of DSM-IV with ICD-10

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    © 2019 Association for Computing Machinery. Given two graphs modelling related, but possibly distinct, networks, the alignment of the networks can help identify signiicant structures and substructures which may relate to the functional purpose of the network components. The Network Alignment Problem is the NP-hard computational formalisation of this goal and is a useful technique in a variety of data mining and knowledge discovery domains. In this paper we develop a memetic algorithm to solve the Network Alignment Problem and demonstrate the efectiveness of the approach on a series of biological networks against the existing state of the art alignment tools. We also demonstrate the use of network alignment as a clustering and classiication tool on two mental health disorder diagnostic databases
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