DB system design for new hardware and sciences

Anastasia Ailamaki

École Polytechnique Fédérale de Lausanneand

Carnegie Mellon University

introspective

parallelism

shar

ing

Uni-processor Multi-core

Multi-processor Cluster

Exploit max parallelism and sharing simultaneously

Moore’s Law = cores

Performance burden shifts to software

0

50

100

150

200

1985 1990 1995 2000 2005 2010

Year of Introduction

Nu

mb

er o

f H

W T

hre

ads

UltraSparc T2

Power4

80486

CRS-1 (Tensilica)

Multi-core challenges for DBMS

• CMP-aware parallelism in OLTP – Efficient synchronization– Highly concurrent algorithms

• CMP-aware sharing in BI– Eliminate redundancy with work sharing– Improve locality in query operators

• …

• But programmers are not multithreaded

sciences

Challenges: Complexity AND size

Alliez et al, INRIA, SIGGRAPH’05

Brain Mind Institute, EPFL

• Automate DB Design

• Computational Support

• Understanding Data

Summary

• Challenge #1: exploit hardware– Parallelism, sharing maximized simultaneously– Infrastructure to parallel thinking&programming

• Challenge #2: serve sciences– Reduce complexity through abstraction– Manage large datasets on large computers

“Multicore: This is the one which will have the biggest impact on us. We have never had a problem to solve like this. A breakthrough is needed in how applications are done on multicore devices.”

– Bill Gates

“It’s time we rethought some of the basics of computing. It’s scary and lots of fun at the same time.”

– Burton Smith