SOLR
| _version_ |
1768128433601118208 |
| access_facet |
Electronic Resources |
| author |
Zarubin, Mikhail |
| author2 |
Damme, Patrick, Krause, Alexander, Habich, Dirk, Lehner, Wolfgang |
| author2_role |
, , , |
| author2_variant |
p d pd, a k ak, d h dh, w l wl |
| author_facet |
Zarubin, Mikhail, Damme, Patrick, Krause, Alexander, Habich, Dirk, Lehner, Wolfgang |
| author_role |
|
| author_sort |
Zarubin, Mikhail |
| author_variant |
m z mz |
| building |
Library A |
| callnumber-sort |
|
| collection |
sid-22-col-qucosa |
| contents |
Hybrid memory systems consisting of DRAM and NVRAM offer a great opportunity for column-oriented data systems to persistently store and to efficiently process columnar data completely in main memory. While vectorization (SIMD) of query operators is state-of-the-art to increase the single-thread performance, it has to be combined with thread-level parallelism (MIMD) to satisfy growing needs for higher performance and scalability. However, it is not well investigated how such a SIMD-MIMD interplay could be leveraged efficiently in hybrid memory systems. On the one hand, we deliver an extensive experimental evaluation of typical workloads on columnar data in this paper. We reveal that the choice of the most performant SIMD version differs greatly for both memory types. Moreover, we show that the throughput of concurrent queries can be boosted (up to 2x) when combining various SIMD flavors in a multi-threaded execution. On the other hand, to enable that optimization, we propose an adaptive SIMD-MIMD cocktail approach incurring only a negligible runtime overhead. |
| dewey-full |
621.3 |
| dewey-hundreds |
600 - Technology |
| dewey-ones |
621 - Applied physics |
| dewey-raw |
621.3 |
| dewey-search |
621.3 |
| dewey-sort |
3621.3 |
| dewey-tens |
620 - Engineering |
| facet_avail |
Online, Free |
| finc_class_facet |
Technik |
| fincclass_txtF_mv |
engineering-electrical, engineering-process, technology |
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eBook |
| format_access_txtF_mv |
Book, E-Book |
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Ebook |
| format_de14 |
Book, E-Book |
| format_de15 |
Book, E-Book |
| format_del152 |
Buch |
| format_detail_txtF_mv |
text-online-monograph-independent-conference |
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e-Book |
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Book, E-Book |
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ElectronicBook |
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Book, E-Book |
| format_nrw |
Book, E-Book |
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E-Book |
| genre |
Konferenzschrift |
| genre_facet |
Konferenzschrift |
| geogr_code |
not assigned |
| geogr_code_person |
not assigned |
| id |
22-14-qucosa2-766710 |
| illustrated |
Not Illustrated |
| imprint |
New York, ACM, 2021 |
| imprint_str_mv |
Online-Ausg.: 2021 |
| institution |
DE-105, DE-Gla1, DE-Brt1, DE-D161, DE-540, DE-Pl11, DE-Rs1, DE-Bn3, DE-Zi4, DE-Zwi2, DE-D117, DE-Mh31, DE-D275, DE-Ch1, DE-15, DE-D13, DE-L242, DE-L229, DE-L328 |
| is_hierarchy_id |
|
| is_hierarchy_title |
|
| isil_str_mv |
DE-105, DE-15, DE-540, DE-Bn3, DE-Brt1, DE-Ch1, DE-D117, DE-D13, DE-D161, DE-D275, DE-Gla1, DE-L229, DE-L242, DE-L328, DE-Mh31, DE-Pl11, DE-Rs1, DE-Zi4, DE-Zwi2 |
| language |
English |
| last_indexed |
2023-06-08T10:04:34.996Z |
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zarubin2021simdmimdcocktailinahybridmemoryglassshakennotstirred |
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Qucosa |
| publishDate |
2021 |
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2021 |
| publishPlace |
New York |
| publisher |
ACM |
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marcfinc |
| record_id |
14-qucosa2-766710 |
| recordtype |
marcfinc |
| rvk_facet |
No subject assigned |
| score |
16,855717 |
| source_id |
22 |
| spelling |
Zarubin, Mikhail, SIMD-MIMD cocktail in a hybrid memory glass: shaken, not stirred, New York ACM 2021, txt, nc, Online-Ausg. 2021 Online-Ressource (Text) Technische Universität Dresden, Hybrid memory systems consisting of DRAM and NVRAM offer a great opportunity for column-oriented data systems to persistently store and to efficiently process columnar data completely in main memory. While vectorization (SIMD) of query operators is state-of-the-art to increase the single-thread performance, it has to be combined with thread-level parallelism (MIMD) to satisfy growing needs for higher performance and scalability. However, it is not well investigated how such a SIMD-MIMD interplay could be leveraged efficiently in hybrid memory systems. On the one hand, we deliver an extensive experimental evaluation of typical workloads on columnar data in this paper. We reveal that the choice of the most performant SIMD version differs greatly for both memory types. Moreover, we show that the throughput of concurrent queries can be boosted (up to 2x) when combining various SIMD flavors in a multi-threaded execution. On the other hand, to enable that optimization, we propose an adaptive SIMD-MIMD cocktail approach incurring only a negligible runtime overhead., Hybrid Memory; Column Store; Simd; Mimd; Optimization, Hybridspeicher; Spaltenorientierter Speicher; Simd; Mimd; Optimierung, Konferenzschrift, Damme, Patrick, Krause, Alexander, Habich, Dirk, Lehner, Wolfgang, text/html https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-766710 Online-Zugriff |
| spellingShingle |
Zarubin, Mikhail, SIMD-MIMD cocktail in a hybrid memory glass: shaken, not stirred, Hybrid memory systems consisting of DRAM and NVRAM offer a great opportunity for column-oriented data systems to persistently store and to efficiently process columnar data completely in main memory. While vectorization (SIMD) of query operators is state-of-the-art to increase the single-thread performance, it has to be combined with thread-level parallelism (MIMD) to satisfy growing needs for higher performance and scalability. However, it is not well investigated how such a SIMD-MIMD interplay could be leveraged efficiently in hybrid memory systems. On the one hand, we deliver an extensive experimental evaluation of typical workloads on columnar data in this paper. We reveal that the choice of the most performant SIMD version differs greatly for both memory types. Moreover, we show that the throughput of concurrent queries can be boosted (up to 2x) when combining various SIMD flavors in a multi-threaded execution. On the other hand, to enable that optimization, we propose an adaptive SIMD-MIMD cocktail approach incurring only a negligible runtime overhead., Hybrid Memory; Column Store; Simd; Mimd; Optimization, Hybridspeicher; Spaltenorientierter Speicher; Simd; Mimd; Optimierung, Konferenzschrift |
| title |
SIMD-MIMD cocktail in a hybrid memory glass: shaken, not stirred |
| title_auth |
SIMD-MIMD cocktail in a hybrid memory glass: shaken, not stirred |
| title_full |
SIMD-MIMD cocktail in a hybrid memory glass: shaken, not stirred |
| title_fullStr |
SIMD-MIMD cocktail in a hybrid memory glass: shaken, not stirred |
| title_full_unstemmed |
SIMD-MIMD cocktail in a hybrid memory glass: shaken, not stirred |
| title_short |
SIMD-MIMD cocktail in a hybrid memory glass: shaken, not stirred |
| title_sort |
simd mimd cocktail in a hybrid memory glass shaken not stirred |
| topic |
Hybrid Memory; Column Store; Simd; Mimd; Optimization, Hybridspeicher; Spaltenorientierter Speicher; Simd; Mimd; Optimierung, Konferenzschrift |
| topic_facet |
Hybrid Memory; Column Store; Simd; Mimd; Optimization, Hybridspeicher; Spaltenorientierter Speicher; Simd; Mimd; Optimierung, Konferenzschrift |
| url |
https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-766710 |
| urn |
urn:nbn:de:bsz:14-qucosa2-766710 |
