Vector Extensions for Decision Support DBMS Acceleration

Database management systems (DBMS) have become an essential tool for industry and research and are often a significant component of data centres. As a result of this criticality, efficient execution of DBMS engines has become an important area of investigation. This work takes a top-down approach to...

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Vydáno v:2012 45th Annual IEEE/ACM International Symposium on Microarchitecture s. 166 - 176
Hlavní autoři: Hayes, T., Palomar, O., Unsal, O., Cristal, A., Valero, M.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2012
Témata:
dbms
decision
dlp
microarchitecture
parallelism
simd
support
vector
ISSN:1072-4451
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Abstract Database management systems (DBMS) have become an essential tool for industry and research and are often a significant component of data centres. As a result of this criticality, efficient execution of DBMS engines has become an important area of investigation. This work takes a top-down approach to accelerating decision support systems (DSS) on x86-64 microprocessors using vector ISA extensions. In the first step, a leading DSS DBMS is analysed for potential data-level parallelism. We discuss why the existing multimedia SIMD extensions (SSE/AVX) are not suitable for capturing this parallelism and propose a complementary instruction set reminiscent of classical vector architectures. The instruction set is implemented using unintrusive modifications to a modern x86-64 micro architecture tailored for DSS DBMS. The ISA and micro architecture are evaluated using a cycle-accurate x86-64 micro architectural simulator coupled with a highly-detailed memory simulator. We have found a single operator is responsible for 41% of total execution time for the TPC-H DSS benchmark. Our results show performance speedups between 1.94x and 4.56x for an implementation of this operator run with our proposed hardware modifications.
AbstractList Database management systems (DBMS) have become an essential tool for industry and research and are often a significant component of data centres. As a result of this criticality, efficient execution of DBMS engines has become an important area of investigation. This work takes a top-down approach to accelerating decision support systems (DSS) on x86-64 microprocessors using vector ISA extensions. In the first step, a leading DSS DBMS is analysed for potential data-level parallelism. We discuss why the existing multimedia SIMD extensions (SSE/AVX) are not suitable for capturing this parallelism and propose a complementary instruction set reminiscent of classical vector architectures. The instruction set is implemented using unintrusive modifications to a modern x86-64 micro architecture tailored for DSS DBMS. The ISA and micro architecture are evaluated using a cycle-accurate x86-64 micro architectural simulator coupled with a highly-detailed memory simulator. We have found a single operator is responsible for 41% of total execution time for the TPC-H DSS benchmark. Our results show performance speedups between 1.94x and 4.56x for an implementation of this operator run with our proposed hardware modifications.
Author Cristal, A.
Palomar, O.
Valero, M.
Hayes, T.
Unsal, O.
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  fullname: Valero, M.
  email: Mateo.Valero@bsc.es
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Snippet Database management systems (DBMS) have become an essential tool for industry and research and are often a significant component of data centres. As a result...
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SubjectTerms dbms
decision
dlp
microarchitecture
parallelism
simd
support
vector
Title Vector Extensions for Decision Support DBMS Acceleration
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