Tootfinder

Mastering #Caching in Distributed Applications
https://medium.com/@yt-cloudwaydigital/mastering-caching-in-distributed-applications-e744…

DistR: Language-Guided Distributed Shared Memory with Fine Granularity, Full Transparency, and Ultra Efficiency
Haoran Ma, Yifan Qiao, Shi Liu, Shan Yu, Yuanjiang Ni, Qingda Lu, Jiesheng Wu, Yiying Zhang, Miryung Kim, Harry Xu
https://arxiv.org/abs/2406.02803

DistR: Language-Guided Distributed Shared Memory with Fine Granularity, Full Transparency, and Ultra Efficiency
Despite being a powerful concept, distributed shared memory (DSM) has not been made practical due to the extensive synchronization needed between servers to implement memory coherence. This paper shows a practical DSM implementation based on the insight that the ownership model embedded in programming languages such as Rust automatically constrains the order of read and write, providing opportunities for significantly simplifying the coherence implementation if the ownership semantics can be ex…

trec_web: TREC WT10g (2003)
A web graph network originally constructed in 2003 as a testbed for information-retrieval techniques, including web search engines. Distributed by University of Glasgow.
This network has 1601787 nodes and 8063026 edges.
Tags: Informational, Web graph, Unweighted
https://netw…

This https://arxiv.org/abs/2212.02459 has been replaced.
link: https://scholar.google.com/scholar?q=a

Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems
This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agents' iterates are influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case we show that convergence to the tru…

So appropriate on the birth week of Kubernetes!
https://opinuendo.com/@matthew/112575463606403347

In lecture 05 of our #ise2024 lecture series, we are introducing the concept of distributed semantics and are referring (amongst others) to Ludwig Wittgenstein and his approach to the philosophy of language, and combine it with the idea of word vectors and embeddings.
lecture slides:

This https://arxiv.org/abs/2212.01976 has been replaced.
link: https://scholar.google.com/scholar?q=a

FedCC: Robust Federated Learning against Model Poisoning Attacks
Federated Learning, designed to address privacy concerns in learning models, introduces a new distributed paradigm that safeguards data privacy but differentiates the attack surface due to the server's inaccessibility to local datasets and the change in protection objective--parameters' integrity. Existing approaches, including robust aggregation algorithms, fail to effectively filter out malicious clients, especially those with non-Independently and Identically Distributed data. Furthermore, t…

Brief Announcement: Distributed Unconstrained Local Search for Multilevel Graph Partitioning
Peter Sanders, Daniel Seemaier
https://arxiv.org/abs/2406.03169

Brief Announcement: Distributed Unconstrained Local Search for Multilevel Graph Partitioning
Partitioning a graph into blocks of roughly equal weight while cutting only few edges is a fundamental problem in computer science with numerous practical applications. While shared-memory parallel partitioners have recently matured to achieve the same quality as widely used sequential partitioners, there is still a pronounced quality gap between distributed partitioners and their sequential counterparts. In this work, we shrink this gap considerably by describing the engineering of an unconstr…

This https://arxiv.org/abs/2403.09347 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences
Effective attention modules have played a crucial role in the success of Transformer-based large language models (LLMs), but the quadratic time and memory complexities of these attention modules also pose a challenge when processing long sequences. One potential solution for the long sequence problem is to utilize distributed clusters to parallelize the computation of attention modules across multiple devices (e.g., GPUs). However, adopting a distributed approach inevitably introduces extra mem…

Efficient Data-Parallel Continual Learning with Asynchronous Distributed Rehearsal Buffers
Thomas Bouvier (KerData), Bogdan Nicolae (ANL), Hugo Chaugier (KerData), Alexandru Costan (KerData), Ian Foster (ANL), Gabriel Antoniu (KerData)
https://arxiv.org/abs/2406.03285

Efficient Data-Parallel Continual Learning with Asynchronous Distributed Rehearsal Buffers
Deep learning has emerged as a powerful method for extracting valuable information from large volumes of data. However, when new training data arrives continuously (i.e., is not fully available from the beginning), incremental training suffers from catastrophic forgetting (i.e., new patterns are reinforced at the expense of previously acquired knowledge). Training from scratch each time new training data becomes available would result in extremely long training times and massive data accumulati…