A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Filters
PrIter: A Distributed Framework for Prioritizing Iterative Computations
2013
IEEE Transactions on Parallel and Distributed Systems
Preserved Fulltext
We develop a distributed computing framework, PrIter, which supports the prioritized execution of iterative computations. ...
In addition, PrIter is shown better performance for iterative computations than other state-of-the-art distributed frameworks such as Spark and Piccolo. ...
Yanfeng Zhang was a visiting student at UMass Amherst, supported by China Scholarship Council, when this work was performed. ...
doi:10.1109/tpds.2012.272
fatcat:kugnwaenfncpxa33lr7ohwia74
Efficient Hybrid framework for parallel Resource and task scheduling in the Map reduce programming
2016
2016 International Conference on Computer Communication and Informatics (ICCCI)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
In order to achieve the good performance, model a framework based on Service Level Agreement (SLA) and its strategies. ...
The parallel scheduler is modeled for resource and task using particle swarm optimization to manage the assignments of map and reduce task. ...
Prlter: A Distributed Framework for Prioritizing Iterative Computations [8] The data points are so far accessed as a whole without any importance, so we prioritize the computations by its importance ...
doi:10.1109/iccci.2016.7479961
fatcat:zogbemfth5ejpcvznymb2brf7i
Efficient Two-Level Scheduling for Concurrent Graph Processing
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Secondly, multiple priority-based data scheduling provides the support of prioritized iteration for concurrent jobs, which is based on the global priority generated by individual priority of each job. ...
Simultaneously, we adopt block priority instead of fine-grained priority to schedule graph data to decrease the computation cost. ...
PrIter [2] enables fast iterative computation by providing the support of prioritized iteration, it means extracts the subset of data that have higher priority values to perform the iterative updates ...
arXiv:1806.00777v1
fatcat:vgx6zoio55df3ekpsjtc3wws2a
Maiter: An Asynchronous Graph Processing Framework for Delta-based Accumulative Iterative Computation
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
These algorithms are implemented in a large-scale distributed environment in order to scale to massive data sets. ...
To accelerate these large-scale graph-based iterative computations, we propose delta-based accumulative iterative computation (DAIC). ...
PrIter [39] enables prioritized iteration by modifying iMapReduce. ...
arXiv:1710.05785v1
fatcat:34cxcgdbhffgjl7getxble2obi
Accelerate large-scale iterative computation through asynchronous accumulative updates
2012
Proceedings of the 3rd workshop on Scientific Cloud Computing Date - ScienceCloud '12
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2012; you can also visit the original URL.
The file type is application/pdf.
We present a general computation model to describe asynchronous accumulative iterative computation. Based on the computation model, we design and implement a distributed framework, Maiter. ...
To accelerate iterative computations in a large-scale distributed environment, we identify a broad class of iterative computations that can accumulate iterative update results. ...
Yanfeng Zhang was a visiting student at UMass Amherst, supported by China Scholarship Council. ...
doi:10.1145/2287036.2287041
fatcat:j55m6mcsdjh6fglmjon7y6sopa
Maiter: An Asynchronous Graph Processing Framework for Delta-Based Accumulative Iterative Computation
2014
IEEE Transactions on Parallel and Distributed Systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
These algorithms are implemented in a large-scale distributed environment in order to scale to massive data sets. ...
To accelerate these large-scale graph-based iterative computations, we propose delta-based accumulative iterative computation (DAIC). ...
PrIter [39] enables prioritized iteration by modifying iMapReduce. ...
doi:10.1109/tpds.2013.235
fatcat:2okpzddpzndzvbojl2v6mxcrzq
A Smart Strategy for Speculative Execution Based on Hardware Resource in a Heterogeneous Distributed Environment
2016
International Journal of Grid and Distributed Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
MapReduce, as a popular programming model for processing large data sets, has been widely applied. MapReduce 2.0 (MRV2) is a newly adopted one, which has a better performance. ...
Speculative execution known as an approach for dealing with the above problems works by backing up those tasks running on a low performance machine to a higher one. ...
In addition, PrIter is shown better performance for iterative computations than other state-of-the-art distributed frameworks such as Spark and Piccolo [17] . ...
doi:10.14257/ijgdc.2016.9.2.18
fatcat:rlsvn6ue2ffljoxwagjpxdrwve
Accelerating iterative algorithms with asynchronous accumulative updates on FPGAs
2013
2013 International Conference on Field-Programmable Technology (FPT)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
Existing techniques to parallelize such algorithms typically use software frameworks such as MapReduce and Hadoop to distribute data for an iteration across multiple CPU-based workstations in a cluster ...
Computation is dynamically prioritized to accelerate algorithm convergence. A general-class of iterative algorithms have been implemented on a cluster of four FPGAs. ...
We thank Altera Corporation for the donation of the DE4 boards and supporting tools. ...
doi:10.1109/fpt.2013.6718332
dblp:conf/fpt/UnnikrishnanVKGT13
fatcat:x6hv35be6bb37ln4vewp7zhgcq
Scalable Distributed Belief Propagation with Prioritized Block Updates
2014
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Therefore, we design a distributed framework, Prom, to facilitate the implementation of BP algorithms. ...
Belief propagation (BP) is a popular method for performing approximate inference on probabilistic graphical models. ...
Acknowledgments We would like to thank anonymous reviewers for their insightful comments and suggestions. This work is partially supported by NSF grants CNS-1217284 and CCF-1018114. ...
doi:10.1145/2661829.2662081
dblp:conf/cikm/YinG14
fatcat:kgcemvdqqzhvlmacizs7dyk4ai
Asynchronous Large-Scale Graph Processing Made Easy
2013
Conference on Innovative Data Systems Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Scaling large iterative graph processing applications through parallel computing is a very important problem. ...
GRACE provides a synchronous iterative graph programming model for users to easily implement, test, and debug their applications. ...
We thank David Bindel for helpful discussions, and we thank the anonymous CIDR reviewers for their insightful comments which helped us to improve this paper. ...
dblp:conf/cidr/WangXDG13
fatcat:d4uzfuu45rfa5afhj4jzqodqiy
MapReduce is Good Enough? If All You Have is a Hammer, Throw Away Everything That's Not a Nail!
[article]
2012
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
To be more specific, much discussion in the literature surrounds the fact that iterative algorithms are a poor fit for MapReduce: the simple solution is to find alternative non-iterative algorithms that ...
This essay captures my personal experiences as an academic researcher as well as a software engineer in a "real-world" production analytics environment. ...
PrIter [56] , in contrast, takes a slightly different approach to speeding up iterative computation: it prioritizes those computations that are likely to lead to convergence. ...
arXiv:1209.2191v1
fatcat:yu4z5pwryjghtaspm6qjvyna4y
STRADS
2016
Proceedings of the Eleventh European Conference on Computer Systems - EuroSys '16
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
To support SchMP at scale, we develop a distributed framework STRADS which optimizes the throughput of SchMP programs, and benchmark four common ML applications written as SchMP programs: LDA topic modeling ...
Hence, the effectiveness of a model-parallel algorithm is greatly affected by its schedule -which parameters are updated in parallel, and how they are prioritized [22, 23, 41] . ...
Acknowledgements This research is supported in part by Intel as part of the Intel Science and Technology Center for Cloud Computing (ISTC-CC), the National Science Foundation under awards CNS-1042537, ...
doi:10.1145/2901318.2901331
dblp:conf/eurosys/KimHLZDGX16
fatcat:hf7j7vy3p5ckbbndkqe2bswoo4
The Seven Deadly Sins of Cloud Computing Research
2012
USENIX Workshop on Hot Topics in Cloud Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Research into distributed parallelism on "the cloud" has surged lately. ...
, present evidence illustrating that they pose real problems, and discuss ways for the community to avoid them in the future. ...
In a cluster shared between heterogeneous jobs, we may wish to prioritize some workloads over others. ...
dblp:conf/hotcloud/SchwarzkopfMH12
fatcat:5mn5nfilhfbsfjvq4b62fbjuky
Efficient Iterative Processing in the SciDB Parallel Array Engine
[article]
2015
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, we develop a model for iterative array computations and a series of optimizations. ...
Many scientific data-intensive applications perform iterative computations on array data. There exist multiple engines specialized for array processing. ...
Acknowledgments This work is supported in part by NSF grant IIS-1110370 and the Intel Science and Technology Center for Big Data. ...
arXiv:1506.00307v1
fatcat:ahbj3bbkfrayhcunkkt6t46ip4
A survey of large-scale analytical query processing in MapReduce
2013
The VLDB journal
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Arguably the most popular framework for contemporary large-scale data analytics is MapReduce, mainly due to its salient features that include scalability, fault-tolerance, ease of programming, and flexibility ...
A taxonomy is presented for categorizing existing research on MapReduce improvements according to the specific problem they target. ...
Acknowledgments We would like to thank the editors and the anonymous reviewers for their very helpful comments that have significantly improved this paper. The research of C. ...
doi:10.1007/s00778-013-0319-9
fatcat:3gkpguiwnre2jduhjssuqgydfq
« Previous
Showing results 1 — 15 out of 32 results