Publication:
Snoopie: a multi-GPU communication profiler and visualizer

dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorBaydamirli, Javid
dc.contributor.kuauthorErten, Didem Unat
dc.contributor.kuauthorIssa, Mohammad Kefah Taha
dc.contributor.kuauthorSağbili, Doğan
dc.contributor.kuauthorSasongko, Muhammad Aditya
dc.contributor.kuauthorTurimbetov, İlyas
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2024-12-29T09:39:32Z
dc.date.issued2024
dc.description.abstractWith data movement becoming one of the most expensive bottlenecks in computing, the need for profiling tools to analyze communication becomes crucial for effectively scaling multi-GPU applications. While existing profiling tools including first-party software by GPU vendors are robust and excel at capturing compute operations within a single GPU, support for monitoring GPU-GPU data transfers and calls issued by communication libraries is currently inadequate. To fill these gaps, we introduce Snoopie, an instrumentation-based multi-GPU communication profiling tool built on NVBit, capable of tracking peer-to-peer transfers and GPU-centric communication library calls. To increase programmer productivity, Snoopie can attribute data movement to the source code line and the data objects involved. It comes with multiple visualization modes at varying granularities, from a coarse view of the data movement in the system as a whole to specific instructions and addresses. Our case studies demonstrate Snoopie's effectiveness in monitoring data movement, locating performance bugs in applications, and understanding concrete data transfers abstracted beneath communication libraries. The tool is publicly available at https://github.com/ParCoreLab/snoopie.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.openaccessHybrid Gold Open Access
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipThis work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under Grant 949587 and in part by the Royal Society-Newton Advanced Fellowship under Grant NAF\R2\202207.
dc.identifier.doi10.1145/3650200.3656597
dc.identifier.isbn979-8-4007-0610-3
dc.identifier.quartileN/A
dc.identifier.scopus2-s2.0-85196304709
dc.identifier.urihttps://doi.org/10.1145/3650200.3656597
dc.identifier.urihttps://hdl.handle.net/20.500.14288/23027
dc.identifier.wos1255419500043
dc.keywordsData transfer
dc.keywordsData visualization
dc.keywordsLibraries
dc.keywordsProgram debugging
dc.language.isoeng
dc.publisherAssoc Computing Machinery
dc.relation.ispartofProceedings of the 38th ACM International Conference on Supercomputing, ACM ICS 2024
dc.subjectComputer science
dc.subjectArtificial intelligence
dc.subjectHardware and architecture
dc.subjectTheory and methods
dc.titleSnoopie: a multi-GPU communication profiler and visualizer
dc.typeConference Proceeding
dspace.entity.typePublication
local.contributor.kuauthorIssa, Mohammad Kefah Taha
local.contributor.kuauthorSasongko, Muhammad Aditya
local.contributor.kuauthorTurimbetov, İlyas
local.contributor.kuauthorBaydamirli, Javid
local.contributor.kuauthorSağbili, Doğan
local.contributor.kuauthorErten, Didem Unat
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Computer Engineering
local.publication.orgunit2Graduate School of Sciences and Engineering
relation.isOrgUnitOfPublication89352e43-bf09-4ef4-82f6-6f9d0174ebae
relation.isOrgUnitOfPublication3fc31c89-e803-4eb1-af6b-6258bc42c3d8
relation.isOrgUnitOfPublication.latestForDiscovery89352e43-bf09-4ef4-82f6-6f9d0174ebae
relation.isParentOrgUnitOfPublication8e756b23-2d4a-4ce8-b1b3-62c794a8c164
relation.isParentOrgUnitOfPublication434c9663-2b11-4e66-9399-c863e2ebae43
relation.isParentOrgUnitOfPublication.latestForDiscovery8e756b23-2d4a-4ce8-b1b3-62c794a8c164

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
IR04742.pdf
Size:
9.45 MB
Format:
Adobe Portable Document Format