9 research outputs found
The Relative Power of Composite Loop Agreement Tasks
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
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
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 . 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 top-1
accuracy. We also achieve an accuracy of homomorphically evaluated CNNs on the
CIFAR-10 dataset of .Comment: 14 pages, 9 figure
DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference
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
© 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