Sökning: id:"swepub:oai:DiVA.org:hb-1419" >
Accelerating Text M...
Accelerating Text Mining Workloads in a MapReduce-based Distributed GPU Environment
-
- Wittek, Peter (författare)
- Högskolan i Borås,Institutionen Biblioteks- och informationsvetenskap / Bibliotekshögskolan
-
- Darányi, Sándor (författare)
- Högskolan i Borås,Institutionen Biblioteks- och informationsvetenskap / Bibliotekshögskolan
-
(creator_code:org_t)
- Elsevier Inc, 2013
- 2013
- Engelska.
-
Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier Inc. - 0743-7315 .- 1096-0848. ; 73:2, s. 198-206
- Relaterad länk:
-
https://hb.diva-port... (primary) (Raw object)
-
visa fler...
-
http://bada.hb.se/bi...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- Scientific computations have been using GPU-enabled computers successfully, often relying on distributed nodes to overcome the limitations of device memory. Only a handful of text mining applications benefit from such infrastructure. Since the initial steps of text mining are typically data intensive, and the ease of deployment of algorithms is an important factor in developing advanced applications, we introduce a flexible, distributed, MapReduce-based text mining workflow that performs I/O-bound operations on CPUs with industry-standard tools and then runs compute-bound operations on GPUs which are optimized to ensure coalesced memory access and effective use of shared memory. We have performed extensive tests of our algorithms on a cluster of eight nodes with two NVidia Tesla M2050s attached to each, and we achieve considerable speedups for random projection and self-organizing maps.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Nyckelord
- GPU computing
- MapReduce
- Text mining
- Self-organizing maps
- Random projection
- Library and Information Science
- Library and Information Science
- Biblioteks- och informationsvetenskap
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas