Distributed Computing on an Ensemble of Browsers

Cushing, Reginald; Putra, Ganesh; Koulouzis, Spiros; Belloum, Adam; Bubak, Marian; Laat, Cees de

doi:10.1109/mic.2013.3

Cited by 27 publications

(26 citation statements)

References 3 publications

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“…One reason is that starting with Google's Chrome v8 in 2008, new JavaScript (JS) engines with just-in-time (JIT) compilers allowed JS code to rival Java [19]. When compared to Java, JavaScript was found to be slower only by about one third in math-intensive computation [60], though significant speed differences may exist between JS engines of different browsers [47].…”

Section: The Third Generation: Fast and The Furiously Multithreadingmentioning

confidence: 99%

“…WeevilScout [19] uses a WebCL code wrapped in JavaScript. New jobs submitted to WeevilScout can be written in JavaScript.…”

Section: The Third Generation: Fast and The Furiously Multithreadingmentioning

confidence: 99%

“…We identify common problems 3 http://www.internetworldstats.com/stats.htm Therefore, the ideal jobs for BBVC should be easily divisible into small, computationally independent tasks -or, at least, if the computation runs in phases, tasks should be computationally autonomous within each phase. The general problem categories considered by researchers working on BBVC were called piecework computations [16], master-worker [26], coarse-grained [22], malleable applications [19] or bags of tasks [23].…”

Section: Introductionmentioning

confidence: 99%

“…During our survey we encountered the following problems identified as well-suited for BBVC: evolutionary/genetic algorithms [23][25] [44][58] [64], simulated annealing [23] [64], colony optimization [23], particle swarm optimization [23], artificial bee colonies [23], brute force search [64], cryptography [9][64] [80], game-tree evaluation [64], mapreduce problems with computation-intensive map phase [22] [47][50] [57][67] [79], montecarlo simulations [19][54] [55] branch-and-bound [16] and its limited subset, bulksynchronous-parallel (BSP) [68], and, finally, the algorithms based on directed acyclic graphs [19].…”

Section: Introductionmentioning

confidence: 99%

“…[19]. Other problems used in experiments were: DES code breaking [10], matrix multiplication [16] [68], Traveling Salesman Problem [16][24] [67], Mandelbrot generation [69] [70], searching for Mersenne primes using LucasLehmer primality test [56] [29], Collatz hypothesis verification [56], the factorization of large integer into two primes and Pearson correlation evaluation on set of genetic samples [9].…”

Section: Introductionmentioning

confidence: 99%

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Browser-based Harnessing of Voluntary Computational Power

Fabisiak

Danilecki

2017

Foundations of Computing and Decision Sciences

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Abstract. Computers connected to internet represent an immense computing power, mostly unused by their owners. One way to utilize this public resource is via world wide web, where users can share their resources using nothing more except their browsers. We survey the techniques employing the idea of browser-based voluntary computing (BBVC), discuss their commonalities, recognize recurring problems and their solutions and finally we describe a prototype implementation aiming at efficient mining of voluntary-contributed computing power.

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Section: The Third Generation: Fast and The Furiously Multithreadingmentioning

confidence: 99%

“…WeevilScout [19] uses a WebCL code wrapped in JavaScript. New jobs submitted to WeevilScout can be written in JavaScript.…”

Section: The Third Generation: Fast and The Furiously Multithreadingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Browser-based Harnessing of Voluntary Computational Power

Fabisiak

Danilecki

2017

Foundations of Computing and Decision Sciences

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Resilient parallel computing on volunteer PC grids

Subhlok

Nguyen

Gabriel

et al. 2018

Concurrency and Computation

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Volunteer PC hosts represent massive computation capacity at a low cost but are challenging to employ for general parallel computing. This paper presents the design, execution model, implementation, and evaluation of the Volpex framework for robust execution of parallel codes on volunteer PC grids characterized by system and network heterogeneity, varying availability, and frequent failures. The communication model is based on one-sided Put/Get calls to an abstract global shared space enhanced to support multiple autonomous instances of the same process at different stages of execution. Our approach customizes and combines the use of replication, checkpointing, and host selection. This presents formidable challenges that are addressed in this work; efficient checkpointing of distributed replicated processes, dynamic management of redundancy, quick restart in a distributed environment, and application specific host selection. The integrated runtime system is shown to effectively execute moderate size, coarse-grain, communicating codes on a worldwide distributed volunteer environment, a new milestone in volunteer computing. Extensive evaluation is conducted with example scientific codes on a pool of around 600 volunteer hosts. The results demonstrate the trade-offs in deploying checkpointing, redundancy, and host selection, and how these methods combine to provide application performance that is close to the ideal failure free performance. INTRODUCTIONDistributed computing is widely deployed for resource hungry applications that need massive computational power, large storage capacity, high processing speed or quick accessibility of data. Grid computing addresses this challenge with geographically distributed networked loosely coupled clusters. Popular grid computing middleware systems include Globus Toolkit 1 and UNICORE 2,3 (Uniform Interface to COmpute REsources). Ordinary PCs have been employed successfully for large scale scientific computing, most commonly using BOINC 4 as middleware. Web browsers can host middleware for distributed computing, a popular example being Weevilscout. 5The BOINC middleware uses volunteered public PCs for scientific applications when idle. It has been remarkably successful, managing as much as 5 Petaflops of aggregate compute power and supporting over 60 scientific research projects. However, this is a tiny fraction of the volunteer compute power that could be exploited. The research presented in this paper aims to enhance the state-of-the-art for parallel computing on volunteer PC grids. We will refer to PCs made available for scientific computing when idle as volunteer nodes (or hosts, or PCs) and an execution environment composed of PCs connected by a LAN or Internet as a volunteer PC grid (VPG); even if the PCs are managed hosts in an organization. Volunteer PCs represent a potentially immense but volatile resource; they are heterogeneous in terms of architecture, networking, and operating system, prone to failure, and their availability to execute guest scientific applications...

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Considerations of Computational Efficiency in Volunteer and Cluster Computing

Czarnul

Matuszek

2016

Parallel Processing and Applied Mathematics

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Distributed Computing on an Ensemble of Browsers

Cited by 27 publications

References 3 publications

Browser-based Harnessing of Voluntary Computational Power

Browser-based Harnessing of Voluntary Computational Power

Resilient parallel computing on volunteer PC grids

Considerations of Computational Efficiency in Volunteer and Cluster Computing

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