# distributed-computing Published articles for distributed-computing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Revisiting HPX DevFeed: [Revisiting HPX]() Original publisher: [Read original article]() Author: brakmic Published: 2024-09-23T09:53:03Z Content type: tutorial Language: en Sources: [Harris Brakmic - Coding]() Topics: [distributed-systems](), [Programming](), [Testing]() Tags: [article](), [asynchronous](), [c-plus-plus](), [coding](), [cpp](), [distributed](), [distributed-computing](), [examples](), [framework](), [hpx](), [installation](), [macos](), [parallel](), [parallelism](), [programming](), [testing]() ### AI overview This hands-on article revisits HPX, a runtime system for asynchronous, parallel, and distributed computing. It covers installation on macOS, configuration, compilation, and testing, and discusses HPX features including task-based parallelism and components. ### Source excerpt This article explores the powerful parallelization and distributed computing capabilities of the HPX framework. Starting with installation on macOS, it walks through configuring, compiling, and testing HPX on a modest machine, highlighting key features like asynchronous programming, task-based parallelism, and HPX components. Whether new to HPX or looking for practical examples, this guide offers a hands-on approach to understanding and utilizing HPX in real-world scenarios. ## Automated Broken Link Checking with WebAssembly-Powered Distributed Computing DevFeed: [Automated Broken Link Checking with WebAssembly-Powered Distributed Computing]() Original publisher: [Read original article]() Author: Tim McCallum Published: 2024-06-04T12:00:00Z Content type: article Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [WebAssembly](), [distributed-systems]() Tags: [automated](), [distributed](), [distributed-computing](), [using](), [webassembly]() ### AI overview The article describes automated broken link checking using WebAssembly-powered distributed computing. ### Source excerpt Automated broken link checking using WebAssembly-powered distributed computing. ## Helios: hyperscale indexing for the cloud & edge - part 1 DevFeed: [Helios: hyperscale indexing for the cloud & edge - part 1]() Original publisher: [Read original article]() Author: adriancolyer Published: 2020-10-26T20:22:00Z Content type: article Language: en Sources: [Adrian Colyer]() Topics: [data-processing](), [big-data](), [Cloud](), [distributed-systems](), [data](), [Edge](), [Microsoft](), [client](), [servers]() Tags: [architecture](), [big-data](), [cloud](), [data](), [data-processing](), [datastores](), [distributed](), [distributed-computing](), [edge](), [idc](), [latency](), [microsoft](), [paper](), [real-time](), [server](), [streams](), [systems](), [uncategorized]() ### AI overview This first part of a discussion of Helios examines its role as a reference architecture for distributed big-data systems. It describes the pressures driving cloud platforms toward new approaches, including growing data volume, variable arrival rates, lower latency, reduced data-center costs, and stronger privacy considerations. ### Source excerpt Helios: hyperscale indexing for the cloud & edge, Potharaju et al., PVLDB'20 On the surface this is a paper about fast data ingestion from high-volume streams, with indexing to support efficient querying. As a production system within Microsoft capturing around a quadrillion events and indexing 16 trillion search keys per day it would be interesting in its own right, ... Continue reading Helios: hyperscale indexing for the cloud & edge - part 1 ## Applications of Blockchain to Programming Language Theory DevFeed: [Applications of Blockchain to Programming Language Theory]() Original publisher: [Read original article]() Published: 2020-04-13T14:38:12Z Content type: opinion Language: en Sources: [Lambda the Ultimate]() Topics: [Blockchain](), [Programming](), [Programming language](), [smart contract](), [distributed-systems]() Tags: [blockchain](), [bug](), [distributed-computing](), [ethereum](), [fun](), [implementation](), [programming](), [programming-language](), [semantics](), [smart-contract](), [transactions](), [verification]() ### AI overview A forum discussion explores how blockchain could contribute to programming language theory, practice, and developer productivity. It considers mutual and common knowledge, distributed computing fabrics, third-party transactions, and the verification and security of smart contracts, including an Ethereum smart-contract logic bug. ### Source excerpt Let's talk about Blockchain. Goal is to use this forum topic to highlight its usefulness to programming language theory and practice. If you're familiar with existing research efforts, please share them here. In addition, feel free to generate ideas for how Blockchain could improve languages and developer productivity. As one tasty example: Blockchain helps to formalize thinking about mutual knowledge and common knowledge, and potentially think about sharing intergalactic computing power through vast distributed computing fabrics. If we can design contracts in such a way that maximizes the usage of mutual knowledge while minimizing common knowledge to situations where you have to "prove your collateral", third-party transactions could eliminate a lot of back office burden. But, there might be benefits in other areas of computer science from such research, as well. Some language researchers, like Mark S. Miller, have always dreamed of Agoric and the Decades-Long Quest for Secure Smart Contracts. Some may also be aware that verification of smart contracts is an important research area, because of the notorious theft of purse via logic bug in an Ethereum smart contract. ## Teleport 2.5 release information DevFeed: [Teleport 2.5 release information]() Original publisher: [Read original article]() Author: ev@goteleport.com (Ev Kontsevoy) Published: 2018-03-10T00:00:00Z Content type: release Language: en Sources: [Teleport]() Topics: [Amazon Web Services](), [gateway](), [ssh](), [upgrade](), [Authentication](), [certificates](), [distributed-systems](), [Single sign-on (SSO)](), [audit](), [nginx](), [configuration]() Tags: [audit](), [authentication](), [aws](), [certificates](), [configuration](), [distributed-computing](), [load-balancer](), [release](), [ssh](), [sso](), [tokens](), [upgrade]() ### AI overview Teleport 2.5 is released with improvements for deploying Teleport at scale on AWS, including highly available auth servers, proxy certificates behind load balancers, and node-token generation using AWS secret-management facilities. The release also documents a cluster upgrade procedure. ### Source excerpt This release of Teleport brings easier AWS deployments at scale and includes several usability enhancements. ## One-box stream processing with CSP DevFeed: [One-box stream processing with CSP]() Original publisher: [Read original article]() Published: 2018-02-18T00:00:00Z Content type: tutorial Language: en Sources: [Adam Bard]() Topics: [streaming-data-processing](), [Clojure](), [stream-processing](), [Concurrent Programming](), [distributed-systems]() Tags: [clojure](), [concurrent](), [core-async](), [coroutine](), [distributed-computing](), [distributed-system](), [flink](), [spark](), [stream](), [stream-processing]() ### AI overview This article presents a small-scale stream-processing architecture in Clojure using core.async and component libraries. It describes modular components connected by asynchronous queues and explains how stream-processing design principles can support reusable, loosely coupled systems without requiring a distributed stream processor. ### Source excerpt If you're like me (that is, employed by an ad tech company), stream processing is usually associated with frameworks like Storm, Flink, Spark Streaming, and other such solutions. However, a lot of real-life software can be described as stream processing - data comes in one end, is transformed or aggregated, and goes somewhere else. Many of these workloads don't justify the overhead of a stream processor, but that doesn't mean they can't benefit from some of the lessons of stream processing systems. ## Running Docker Swarm inside LXC (outdated) DevFeed: [Running Docker Swarm inside LXC (outdated)]() Original publisher: [Read original article]() Author: andreacorbellini Published: 2016-04-13T18:00:00Z Content type: tutorial Language: en Sources: [Andrea Corbellini]() Topics: [Docker](), [Tutorial](), [Containers](), [Development](), [Security]() Tags: [apparmor](), [article](), [containers](), [distributed-computing](), [docker](), [information-technology](), [lxc](), [security](), [shell](), [swarm](), [ubuntu]() ### AI overview This outdated tutorial explains how to run legacy Docker Swarm inside LXC containers using a preferred setup with AppArmor disabled, Docker OverlayFS storage, and etcd discovery. It notes that newer Docker Swarm does not work in LXC as described. ### Source excerpt UPDATE: This article was written in 2016 and refers to a version of Docker Swarm that is now known as "legacy Swarm". The newer Docker Swarm won't work in LXC as described in this article. I've been using Docker Swarm inside LXC containers for a while now, and I thought that I could share my experience with you. Due to their nature ... ## On the Computational Complexity of MapReduce DevFeed: [On the Computational Complexity of MapReduce]() Original publisher: [Read original article]() Published: 2014-10-05T09:00:30Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [computational complexity](), [Algorithms, Complexity](), [Graphs]() Tags: [algorithm](), [algorithms](), [circuit-complexity](), [complexity](), [computational-complexity](), [distributed-computing](), [exponential-time-hypothesis](), [graphs](), [hierarchy](), [karloff](), [l-vs-p](), [map-reduce](), [padding](), [research]() ### AI overview The article presents an accessible discussion of research analyzing the computational complexity of a theoretical model of the MapReduce framework. It describes open questions about MapReduce's power, including whether constant-round algorithms can determine graph connectivity and how the model relates to complexity classes such as P and L. ### Source excerpt I recently wrapped up a fun paper with my coauthors Ben Fish, Adam Lelkes, Lev Reyzin, and Gyorgy Turan in which we analyzed the computational complexity of a model of the popular MapReduce framework. Check out the preprint on the arXiv. Update: this paper is now published in the proceedings of DISC2015. As usual I'll give a less formal discussion of the research here, and because the paper is a bit more technically involved than my previous work I'll be omitting some of the more pedantic details.