dc.contributor.author | Jordà Peroliu, Marc |
dc.contributor.author | Rai, Siddharth |
dc.contributor.author | Ayguadé Parra, Eduard |
dc.contributor.author | Labarta Mancho, Jesús José |
dc.contributor.author | Peña Monferrer, Antonio José |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.contributor.other | Barcelona Supercomputing Center |
dc.date.accessioned | 2022-11-10T11:07:34Z |
dc.date.available | 2022-11-10T11:07:34Z |
dc.date.issued | 2022 |
dc.identifier.citation | Jorda, M. [et al.]. ecoHMEM: Improving object placement methodology for hybrid memory systems in HPC. A: IEEE International Conference on Cluster Computing. "2022 IEEE International Conference on Cluster Computing, Cluster 2022: Heidelberg, Germany, 6-9 September 2022: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 278-288. ISBN 978-1-6654-9856-2. DOI 10.1109/CLUSTER51413.2022.00040. |
dc.identifier.isbn | 978-1-6654-9856-2 |
dc.identifier.uri | http://hdl.handle.net/2117/375994 |
dc.description.abstract | Recent byte-addressable persistent memory (PMEM) technology offers capacities comparable to storage devices and access times much closer to DRAMs than other non-volatile memory technology. To palliate the large gap with DRAM performance, DRAM and PMEM are usually combined. Users have the choice to either manage the placement to different memory spaces by software or leverage the DRAM as a cache for the virtual address space of the PMEM. We present novel methodology for automatic object-level placement, including efficient runtime object matching and bandwidth-aware placement. Our experiments leveraging Intel® Optane™ Persistent Memory show from matching to greatly improved performance with respect to state-of-the-art software and hardware solutions, attaining over 2x runtime improvement in miniapplications and over 6% in OpenFOAM, a complex production application. |
dc.description.sponsorship | This paper received funding from the Intel-BSC Exascale Laboratory SoW 5.1, the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 749516, the EPEEC project from the European Union’s Horizon 2020 research and innovation program under grant agreement No 801051, the DEEP-SEA project from the European Commission’s EuroHPC program under grant agreement 955606, and the Ministerio de Ciencia e Innovacion—Agencia Estatal de Investigación (PID2019-107255GB-C21/AEI/10.13039/501100011033). |
dc.format.extent | 11 p. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors |
dc.subject.lcsh | Memory management (Computer science) |
dc.subject.lcsh | Supercomputers |
dc.subject.other | Data placement |
dc.subject.other | Hybrid memory systems |
dc.subject.other | Optane |
dc.title | ecoHMEM: Improving object placement methodology for hybrid memory systems in HPC |
dc.type | Conference report |
dc.subject.lemac | Gestió de memòria (Informàtica) |
dc.subject.lemac | Supercomputadors |
dc.identifier.doi | 10.1109/CLUSTER51413.2022.00040 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9912666 |
dc.rights.access | Open Access |
local.identifier.drac | 34857331 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/749516/EU/Advanced Ecosystem for Broad Heterogeneous Memory Usage/ECO-H-MEM |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/801051/EU/European joint Effort toward a Highly Productive Programming Environment for Heterogeneous Exascale Computing (EPEEC)/EPEEC |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/955606/EU/DEEP – SOFTWARE FOR EXASCALE ARCHITECTURES/DEEP-SEA |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107255GB-C21/ES/BSC - COMPUTACION DE ALTAS PRESTACIONES VIII/ |
local.citation.author | Jorda, M.; Rai, S.; Ayguade, E.; Labarta, J.; Peña, A. |
local.citation.contributor | IEEE International Conference on Cluster Computing |
local.citation.publicationName | 2022 IEEE International Conference on Cluster Computing, Cluster 2022: Heidelberg, Germany, 6-9 September 2022: proceedings |
local.citation.startingPage | 278 |
local.citation.endingPage | 288 |