BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Paper detail

Performance Evaluation of Dynamic Scaling on MPI

Dynamic scaling aims to elastically change the number of processes during runtime to tune the performance of the distributed applications. This report briefly presents a performance evaluation of MPI process provisioning / de-provisioning for dynamic scaling by using 16 to 128 cores. Our dynamic scaling implementation allows the new MPI processes from new hosts to communicate with the original ones immediately. Moreover, it forbids the removing MPI processes to communicate with others as well as gets the information whether the host node can be terminated or not. Such a simple feature is not supported as a single-line API in MPI-2 such as MPI_Comm_spawn(). We provide our implementation as a simple library to extend a non-dynamic-scalable MPI program into a dynamic-scalable one by adding only several lines of codes.

preprint2019arXivOpen access
Masatoshi HanaiGeorgios Theodoropoulos
Distributed, Parallel, and Cluster Computing
0citations
0reviews
0saves
Nocode
Nodataset
0institutions

Next steps

Decide what to do with this paper

Like0Dislike0Score 0

Use like or dislike for the fast social read. The more specific scholarly feedback stays available below when needed.

Save to reading list0
Log in to curate

Reading frame

Keep the important context close to the paper

Keep the important signals around this paper in one place: votes, save state, collection context, reviews and the metadata you need before deciding what to do next.

Authors

Masatoshi HanaiGeorgios Theodoropoulos

Institutions

No institution affiliation has been imported for this paper yet.

Add specific reaction

Move through the context

Research map

Open full explorer

Move through nearby people, institutions, topics and adjacent work without leaving the paper page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Structured reviews

0 review(s)

ContributeLeave structured feedbackUse the review template when you have a concrete strength, concern or method question.Open review form
Log in to review

No structured reviews yet. High-signal critique starts here.

Work discussion

0 comment(s)

DiscussAdd a high-signal commentKeep quick notes, caveats and replication pointers separate from formal reviews.Open comment form
Log in to comment

No discussion yet. The first strong comment sets the tone.