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With the rapid scaling of large language models (LLMs), serving numerous low-rank adaptations (LoRAs) concurrently has become increasingly impractical, leading to unaffordable costs and necessitating more parameter-efficient finetuning…

Machine Learning · Computer Science 2024-05-28 Sheng Wang , Boyang Xue , Jiacheng Ye , Jiyue Jiang , Liheng Chen , Lingpeng Kong , Chuan Wu

Addressable self-assembly is the formation of a target structure from a set of unique molecular or colloidal building-blocks, each of which occupies a defined location in the target. The requirement that each type of building-block appears…

Soft Condensed Matter · Physics 2019-12-03 Jim Madge , David Bourne , Mark A. Miller

In this paper, we propose a class of threshold secret sharing schemes with repairing function between shares without the help of the dealer, that we called repairable threshold secret sharing schemes. Specifically, if a share fails, such as…

Cryptography and Security · Computer Science 2015-02-05 Xuan Guang , Jiyong Lu , Fang-Wei Fu

Distributed Machine Learning refers to the practice of training a model on multiple computers or devices that can be called nodes. Additionally, serverless computing is a new paradigm for cloud computing that uses functions as a…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-02-28 Amine Barrak , Fabio Petrillo , Fehmi Jaafar

Currently the only techniques for sharing governance of a deep learning model are homomorphic encryption and secure multiparty computation. Unfortunately, neither of these techniques is applicable to the training of large neural networks…

Machine Learning · Computer Science 2018-12-17 Miljan Martic , Jan Leike , Andrew Trask , Matteo Hessel , Shane Legg , Pushmeet Kohli

We study general techniques for implementing distributed data structures on top of future many-core architectures with non cache-coherent or partially cache-coherent memory. With the goal of contributing towards what might become, in the…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-04-09 Panagiota Fatourou , Nikolaos D. Kallimanis , Eleni Kanellou , Odysseas Makridakis , Christi Symeonidou

In this paper, we present several improvements in the parallelization of the in-place merge algorithm, which merges two contiguous sorted arrays into one with an O(T) space complexity (where T is the number of threads). The approach divides…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-05-27 Berenger Bramas , Quentin Bramas

Scheduling query execution plans is a particularly complex problem in shared-nothing parallel systems, where each site consists of a collection of local time-shared (e.g., CPU(s) or disk(s)) and space-shared (e.g., memory) resources and…

Databases · Computer Science 2014-04-01 Minos Garofalakis , Yannis Ioannidis

Multi-threaded programs have traditionally fallen into one of two domains: cooperative and competitive. These two domains have traditionally remained mostly disjoint, with cooperative threading used for increasing throughput in…

Programming Languages · Computer Science 2018-07-11 Stefan K. Muller , Umut A. Acar , Robert Harper

The MARS15 Monte Carlo code capabilities to deal with time-consuming deep penetration shielding problems and other computationally tough tasks in accelerator, detector and shielding applications, have been enhanced by a parallel processing…

High Energy Physics - Phenomenology · Physics 2008-11-26 M. A. Kostin , N. V. Mokhov

For the parallel-in-time integration method Parareal, pipelining can be used to hide some of the cost of the serial correction step and improve its efficiency. The paper introduces a basic OpenMP implementation of pipelined Parareal and…

Mathematical Software · Computer Science 2019-11-12 Daniel Ruprecht

In recent years, various means of efficiently detecting changepoints in the univariate setting have been proposed, with one popular approach involving minimising a penalised cost function using dynamic programming. In some situations, these…

Methodology · Statistics 2018-10-09 S. O. Tickle , I. A. Eckley , P. Fearnhead , K. Haynes

Dramatic increases in the capabilities of neural network models in recent years are driven by scaling model size, training data, and corresponding computational resources. To develop the exceedingly large networks required in modern…

Machine Learning · Computer Science 2025-04-15 Jared Fernandez , Luca Wehrstedt , Leonid Shamis , Mostafa Elhoushi , Kalyan Saladi , Yonatan Bisk , Emma Strubell , Jacob Kahn

Scalarisation functions are widely employed in MORL algorithms to enable intelligent decision-making. However, these functions often struggle to approximate the Pareto front accurately, rendering them unideal in complex, uncertain…

Machine Learning · Computer Science 2025-11-21 Muhammad Sa'ood Shah , Asad Jeewa

Model ensemble is a popular approach to produce a low-variance and well-generalized model. However, it induces large memory and inference costs, which are often not affordable for real-world deployment. Existing work has resorted to sharing…

Computation and Language · Computer Science 2022-04-19 Chen Liang , Pengcheng He , Yelong Shen , Weizhu Chen , Tuo Zhao

Quantum computers face inherent scaling challenges, a fact that necessitates investigation of distributed quantum computing systems, whereby scaling is achieved through interconnection of smaller quantum processing units. However,…

Quantum Physics · Physics 2026-01-21 Felix Burt , Kuan-Cheng Chen , Kin K. Leung

This article presents an automatic approach to quickly derive a good solution for hardware resource partition and task granularity for task-based parallel applications on heterogeneous many-core architectures. Our approach employs a…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-03-10 Peng Zhang , Jianbin Fang , Canqun Yang , Chun Huang , Tao Tang , Zheng Wang

Subverting the flow of instructions (e.g., by use of code-reuse attacks) still poses a serious threat to the security of today's systems. Various control flow integrity (CFI) schemes have been proposed as a powerful technique to detect and…

Hardware Architecture · Computer Science 2021-03-09 Mario Telesklav , Stefan Tauner

Federated testbeds enable collaborative research by providing access to diverse resources, including computing power, storage, and specialized hardware like GPUs, programmable switches and smart Network Interface Cards (NICs). Efficiently…

We describe an approach to parallel graph partitioning that scales to hundreds of processors and produces a high solution quality. For example, for many instances from Walshaw's benchmark collection we improve the best known partitioning.…

Distributed, Parallel, and Cluster Computing · Computer Science 2010-04-08 Manuel Holtgrewe , Peter Sanders , Christian Schulz