Adaptivity. Luca Schroeder & Thomas Lively
|
|
|
- Martin Hensley
- 5 years ago
- Views:
Transcription
1 Adaptivity Luca Schroeder & Thomas Lively H2O: A Hands-free Adaptive Store. Ioannis Alagiannis, Stratos Idreos and Anastassia Ailamaki ACM SIGMOD International Conference on Data Management, 2014
2 Three things are important in the database world: performance, performance, and performance. Bruce Lindsay
3 It all starts with how we how we store data. Stratos
4 physical data layout defines access patterns, & therefore performance
5 The Fixed Storage Layout Problem
6 Row-store (NSM) A B C D insert (1,2,3,4) select A select A,B,C,D
7 Column-store (DSM) A B C D insert (1,2,3,4) select A select A,B,C,D
8
9 one size does not fit all as you change the query, optimal layout changes as well
10 but what if you knew workload perfectly beforehand?
11 A B C D select A select B,C select D
12 Column-groups A B C D select A select B,C select D
13 Column-groups A B C D select A select B,C select D
14 Column-groups A B C D select A select B,C select D
15 Column-groups A B C D select A select B,C select D
16 Column-groups A B C D select A,B select C,D... select B,C select A
17 Column-groups A B C D select A,B select C,D... select B,C select A
18 Column-groups A B C D select A,B select C,D... select B,C select A
19 Column-groups A B C D select A,B select C,D... select B,C select A
20 Column-groups A B C D select A,B select C,D... select B,C select A
21 Column-groups A B C D select A,B select C,D... select B,C select A
22 Column-groups A B C D select A,B select C,D... select B,C select A
23 Column-groups A B C D select A,B select C,D... select B,C select A
24 Column-groups A B C D select A,B select C,D... select B,C select A
25 Column-groups A B C D select A,B select C,D... select B,C select A
26 Column-groups A B C D select A,B select C,D... select B,C select A
27 Column-groups A B C D select A,B select C,D... select B,C select A
28 Column-groups A B C D select A,B select C,D... select B,C select A
29 Column-groups A B C D select A,B select C,D... select B,C select A
30 Column-groups A B C D select A,B select C,D... select B,C select A
31 Column-groups A B C D select A,B select C,D... select B,C select A
32 Column-groups A B C D select A,B select C,D... select B,C select A
33 Column-groups A B C D select A,B select C,D... select B,C select A
34 Column-groups A B C D select A,B select C,D... select B,C select A
35 even with perfect knowledge of workload finding optimal layout is hard (NP-hard!) and one layout will not be optimal for everything
36 different partitions for table with 10 attributes!
37 Fractured mirrors optimizer A B C D A B C D
38 PAX partition attributes across : all data in a row in a single page, but pages are organized column-by-column
39 can we be adaptive?
40
41 Query Processor Layout 1 Layout 2 A B C D A B C D
42 Query Processor Layout 1 Layout 2 A B C D A B C D
43 Query Processor select(a, B) where C<50 Layout 1 Layout 2 A B C D A B C D
44 Query Processor select(a, B) where C<50 Layout 1 Layout 2 A B C D A B C D
45 Query Processor select(a, B) where C<50 Layout 1 Layout 2 A B C D A B C D
46 Query Processor select(a, B) where C<50 Layout 1 Layout 2 A B C D A B C D
47 Query Processor select(a, B) where C<50 Layout 1 Layout 2 A B C D A B C D q(l1)
48 Query Processor select(a, B) where C<50 Layout 1 Layout 2 A B C D A B C D q(l1)
49 Query Processor select(a, B) where C<50 Layout 1 Layout 2 A B C D A B C D q(l1) q(l2)
50 Query Processor select(a, B) where C<50 Layout 1 Layout 2 A B C D A B C D q(l1) < q(l2)
51 Query Processor select(a, B) where C<50 Layout 1 Layout 2 A B C D A B C D q(l1) < q(l2)
52 Operator Generator select(a, B) where C<50 Using Layout 1
53 Operator Generator select(a, B) where C<50 Using Layout 1
54 Operator Generator select(a, B) where C<50 Using Layout 1
55 Operator Generator select(a, B) where C<50 Using Layout 1 int q_sel_vector(const int n, const T* sel, T* colc, T* val1) { int i, j = 0; const T* ptr = colc; for (i = 0; i < n; i++) { if (*ptr < *val1) sel[j++] = i; ptr++; } }...
56 Adaptation Mechanism
57 Adaptation Mechanism
58 select(a, B) where C<50 Adaptation Mechanism
59 select(a, B) where C<50 Adaptation Mechanism
60 select(a, B) where C<50 select(a, B) where C<50 Adaptation Mechanism
61 select(a, B) where C<50 select(a, B) where C<50 Adaptation Mechanism
62 select(a, B) where C<50 select(a, B) where C<50 Adaptation Mechanism L3
63 select(a, B) where C<50 select(a, B) where C<50 L3 A B C D Adaptation Mechanism
64 select(a, B) where C<50 select(a, B) where C<50 Adaptation Mechanism L3 A B C D cost(w, L2) > cost(w, L1) > cost(w, L3)
65 select(a, B) where C<50 select(a, B) where C<50 Adaptation Mechanism L3 A B C D cost(w, L2) > cost(w, L1) > cost(w, L3)
66 select(a, B) where C<50 select(a, B) where C<50 Adaptation Mechanism L3 A B C D cost(w, L2) > cost(w, L1) > cost(w, L3) Propose L3 to Layout Manager
67 select(a, B) where C<50 A B C D
68 select(a, B) where C<50 A B C D
69 select(a, B) where C<50 A B C D
70 select(a, B) where C<50
71 select(a, B) where C<50
72 window size - 1??
73 Large window A A A A B B B B B B Layout A
74 Large window A A A A B B B B B B Layout A
75 Large window A A A A B B B B B B Layout A
76 Large window A A A A B B B B B B cost(w, A) > cost(w, B) Layout B
77 slow adaptation leads to suboptimal performance
78 Small window A B A B A B A B A B Layout A
79 Small window A B A B A B A B A B Layout B
80 Small window A B A B A B A B A B Layout A
81 tradeoff between adapting quickly and overreacting
82
83 online vs. offline reorganization
84
85 generic vs. generated operator
86
87 Generating Code compiler process icc
88 Generating Code query plan compiler process icc
89 Generating Code query plan compiler process icc
90 Generating Code query plan code template compiler process icc
91 Generating Code query plan code template compiler process icc
92 Generating Code query plan code template compiler process icc
93 Generating Code query plan code template compiler process icc
94 Generating Code query plan code template compiler process dynamically linked lib icc
95 Generating Code query plan code template compiler process dynamically linked lib icc
96 Generating Code query plan code template compiler process dynamically linked lib icc
97 Generating Code query plan code template compiler process dynamically linked lib icc query functions
98 Generating Code query plan code template compiler process dynamically linked lib icc query functions
99 Generating Code query plan code template compiler process dynamically linked lib icc query functions RUN
100
101 experiments
102 reorganization
103
104 next steps
105 column store optimizations, indexing: compatible with adaptive storage?
106 can we use machine learning (e.g. neural nets) to forecast future workloads? how far in the future do we want?
107 The End
H2O: A Hands-free Adaptive Store
H2O: A Hands-free Adaptive Store Ioannis Alagiannis Stratos Idreos Anastasia Ailamaki Ecole Polytechnique Fédérale de Lausanne {ioannis.alagiannis, anastasia.ailamaki}@epfl.ch Harvard University stratos@seas.harvard.edu
complex plans and hybrid layouts
class 7 complex plans and hybrid layouts prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ essential column-stores features virtual ids late tuple reconstruction (if ever) vectorized execution
class 6 more about column-store plans and compression prof. Stratos Idreos
class 6 more about column-store plans and compression prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ query compilation an ancient yet new topic/research challenge query->sql->interpet
class 17 updates prof. Stratos Idreos
class 17 updates prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ early/late tuple reconstruction, tuple-at-a-time, vectorized or bulk processing, intermediates format, pushing selects
Overview of Data Exploration Techniques. Stratos Idreos, Olga Papaemmanouil, Surajit Chaudhuri
Overview of Data Exploration Techniques Stratos Idreos, Olga Papaemmanouil, Surajit Chaudhuri data exploration not always sure what we are looking for (until we find it) data has always been big volume
NoDB: Querying Raw Data. --Mrutyunjay
NoDB: Querying Raw Data --Mrutyunjay Overview Introduction Motivation NoDB Philosophy: PostgreSQL Results Opportunities NoDB in Action: Adaptive Query Processing on Raw Data Ioannis Alagiannis, Renata
Weaving Relations for Cache Performance
VLDB 2001, Rome, Italy Best Paper Award Weaving Relations for Cache Performance Anastassia Ailamaki David J. DeWitt Mark D. Hill Marios Skounakis Presented by: Ippokratis Pandis Bottleneck in DBMSs Processor
class 20 updates 2.0 prof. Stratos Idreos
class 20 updates 2.0 prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ UPDATE table_name SET column1=value1,column2=value2,... WHERE some_column=some_value INSERT INTO table_name VALUES
Architecture-Conscious Database Systems
Architecture-Conscious Database Systems 2009 VLDB Summer School Shanghai Peter Boncz (CWI) Sources Thank You! l l l l Database Architectures for New Hardware VLDB 2004 tutorial, Anastassia Ailamaki Query
Architecture-Conscious Database Systems
Architecture-Conscious Database Systems Anastassia Ailamaki Ph.D. Examination November 30, 2000 A DBMS on a 1980 Computer DBMS Execution PROCESSOR 10 cycles/instruction DBMS Data and Instructions 6 cycles
Bridging the Processor/Memory Performance Gap in Database Applications
Bridging the Processor/Memory Performance Gap in Database Applications Anastassia Ailamaki Carnegie Mellon http://www.cs.cmu.edu/~natassa Memory Hierarchies PROCESSOR EXECUTION PIPELINE L1 I-CACHE L1 D-CACHE
class 9 fast scans 1.0 prof. Stratos Idreos
class 9 fast scans 1.0 prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ 1 pass to merge into 8 sorted pages (2N pages) 1 pass to merge into 4 sorted pages (2N pages) 1 pass to merge into
class 12 b-trees 2.0 prof. Stratos Idreos
class 12 b-trees 2.0 prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ A B C A B C clustered/primary index on A Stratos Idreos /26 2 A B C A B C clustered/primary index on A pos C pos
basic db architectures & layouts
class 4 basic db architectures & layouts prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ videos for sections 3 & 4 are online check back every week (1-2 sections weekly) there is a schedule
Data Systems that are Easy to Design, Tune and Use. Stratos Idreos
Data Systems that are Easy to Design, Tune and Use data systems that are easy to: (years) (months) design & build set-up & tune (hours/days) use e.g., adapt to new applications, new hardware, spin off
Weaving Relations for Cache Performance
Weaving Relations for Cache Performance Anastassia Ailamaki Carnegie Mellon Computer Platforms in 198 Execution PROCESSOR 1 cycles/instruction Data and Instructions cycles
Spanner A distributed database system
Presented by Yue Xia Spanner A distributed database system Background - Developed by Google initially as a key-value storage system - Developers want traditional database features like query language -
class 13 scans vs indexes prof. Stratos Idreos
class 13 scans vs indexes prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ b-tree - dynamic tree - always balanced 35,50 35, 12,20 50, 1,2,3 12,15,17 20, Stratos Idreos 2 /24 select from
Query processing on raw files. Vítor Uwe Reus
Query processing on raw files Vítor Uwe Reus Outline 1. Introduction 2. Adaptive Indexing 3. Hybrid MapReduce 4. NoDB 5. Summary Outline 1. Introduction 2. Adaptive Indexing 3. Hybrid MapReduce 4. NoDB
class 10 b-trees 2.0 prof. Stratos Idreos
class 10 b-trees 2.0 prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ CS Colloquium HV Jagadish Prof University of Michigan 10/6 Stratos Idreos /29 2 CS Colloquium Magdalena Balazinska
class 5 column stores 2.0 prof. Stratos Idreos
class 5 column stores 2.0 prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ worth thinking about what just happened? where is my data? email, cloud, social media, can we design systems
class 17 updates prof. Stratos Idreos
class 17 updates prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ UPDATE table_name SET column1=value1,column2=value2,... WHERE some_column=some_value INSERT INTO table_name VALUES (value1,value2,value3,...)
Column Stores - The solution to TB disk drives? David J. DeWitt Computer Sciences Dept. University of Wisconsin
Column Stores - The solution to TB disk drives? David J. DeWitt Computer Sciences Dept. University of Wisconsin Problem Statement TB disks are coming! Superwide, frequently sparse tables are common DB
Physical Data Organization. Introduction to Databases CompSci 316 Fall 2018
Physical Data Organization Introduction to Databases CompSci 316 Fall 2018 2 Announcements (Tue., Nov. 6) Homework #3 due today Project milestone #2 due Thursday No separate progress update this week Use
Plan (next 4 weeks) 1. Fast forward. 2. Rewind. 3. Slow motion. Rapid introduction to what s in OCaml. Go over the pieces individually
Plan (next 4 weeks) 1. Fast forward Rapid introduction to what s in OCaml 2. Rewind 3. Slow motion Go over the pieces individually History, Variants Meta Language Designed by Robin Milner @ Edinburgh Language
I am: Rana Faisal Munir
Self-tuning BI Systems Home University (UPC): Alberto Abelló and Oscar Romero Host University (TUD): Maik Thiele and Wolfgang Lehner I am: Rana Faisal Munir Research Progress Report (RPR) [1 / 44] Introduction
Weaving Relations for Cache Performance
Weaving Relations for Cache Performance Anastassia Ailamaki Carnegie Mellon David DeWitt, Mark Hill, and Marios Skounakis University of Wisconsin-Madison Memory Hierarchies PROCESSOR EXECUTION PIPELINE
The Case for Heterogeneous HTAP
The Case for Heterogeneous HTAP Raja Appuswamy, Manos Karpathiotakis, Danica Porobic, and Anastasia Ailamaki Data-Intensive Applications and Systems Lab EPFL 1 HTAP the contract with the hardware Hybrid
class 10 fast scans 2.0 prof. Stratos Idreos
class 10 fast scans 2.0 prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ always want to minimize data movement - computation & utilize all resources! registers on chip cache on board
HOW INDEX TO STORE DATA DATA
Stratos Idreos HOW INDEX DATA TO STORE DATA ALGORITHMS data structure decisions define the algorithms that access data INDEX DATA ALGORITHMS unordered [7,4,2,6,1,3,9,10,5,8] INDEX DATA ALGORITHMS unordered
OCaml. ML Flow. Complex types: Lists. Complex types: Lists. The PL for the discerning hacker. All elements must have same type.
OCaml The PL for the discerning hacker. ML Flow Expressions (Syntax) Compile-time Static 1. Enter expression 2. ML infers a type Exec-time Dynamic Types 3. ML crunches expression down to a value 4. Value
Team SimpleMind. Ismail Oukid (TU Dresden), Ingo Müller (KIT), Iraklis Psaroudakis (EPFL) ACM SIGMOD 2015 Programming SIGMOD 2015 (June 2)
Team SimpleMind Ismail Oukid (TU Dresden), Ingo Müller (KIT), Iraklis Psaroudakis (EPFL) ACM SIGMOD 2015 Programming Contest @ SIGMOD 2015 (June 2) Public Agenda Programming Contest Overview Transaction
A Self-Designing Key-Value Store
A Self-Designing Key-Value Store Niv Dayan, Wilson Qin, Manos Athanassoulis, Stratos Idreos http://daslab.seas.harvard.edu/crimsondb/ storage is cheaper inserts & updates price per GB workload time storage
Advanced optimizations of cache performance ( 2.2)
Advanced optimizations of cache performance ( 2.2) 30 1. Small and Simple Caches to reduce hit time Critical timing path: address tag memory, then compare tags, then select set Lower associativity Direct-mapped
systems & research project
class 4 systems & research project prof. HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS265/ index index knows order about the data data filtering data: point/range queries index data A B C sorted A B C initial
Smooth Scan: Statistics-Oblivious Access Paths. Renata Borovica-Gajic Stratos Idreos Anastasia Ailamaki Marcin Zukowski Campbell Fraser
Smooth Scan: Statistics-Oblivious Access Paths Renata Borovica-Gajic Stratos Idreos Anastasia Ailamaki Marcin Zukowski Campbell Fraser Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q16 Q18 Q19 Q21 Q22
SQL Server 2014 Performance Tuning and Optimization
SQL Server 2014 Performance Tuning and Optimization 55144B; 5 Days, Instructor-led Course Description This course is designed to give the right amount of Internals knowledge, and wealth of practical tuning
CS31 Discussion 1E Spring 17 : week 08
CS31 Discussion 1E Spring 17 : week 08 TA: Bo-Jhang Ho bojhang@cs.ucla.edu Credit to former TA Chelsea Ju Project 5 - Map cipher to crib Approach 1: For each pair of positions, check two letters in cipher
So what does studying PL buy me?
So what does studying PL buy me? Enables you to better choose the right language but isn t that decided by libraries, standards, and my boss? Yes. Chicken-and-egg. My goal: educate tomorrow s tech leaders
55144-SQL Server 2014 Performance Tuning and Optimization
55144-SQL Server 2014 Performance Tuning and Optimization Course Number: M55144 Category: Technical - Microsoft Duration: 5 day Overview This course is designed to give the right amount of Internals knowledge,
Efficient Bulk Deletes for Multi Dimensional Clustered Tables in DB2
Efficient Bulk Deletes for Multi Dimensional Clustered Tables in DB2 Bishwaranjan Bhattacharjee, Timothy Malkemus IBM T.J. Watson Research Center Sherman Lau, Sean McKeough, Jo-anne Kirton Robin Von Boeschoten,
The University Of Michigan. EECS402 Lecture 05. Andrew M. Morgan. Savitch Ch. 5 Arrays Multi-Dimensional Arrays. Consider This Program
The University Of Michigan Lecture 05 Andrew M. Morgan Savitch Ch. 5 Arrays Multi-Dimensional Arrays Consider This Program Write a program to input 3 ints and output each value and their sum, formatted
News. Programming Languages. Complex types: Lists. Recap: ML s Holy Trinity. CSE 130: Spring 2012
News CSE 130: Spring 2012 Programming Languages On webpage: Suggested HW #1 PA #1 (due next Fri 4/13) Lecture 2: A Crash Course in ML Please post questions to Piazza Ranjit Jhala UC San Diego Today: A
Object-Based Storage Model for Object-Oriented Database*
Object-Based Storage Model for Object-Oriented Database* Zhongmin Li 1 and Zhanwu Yu 2, ** 1 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University,
OCaml. History, Variants. ML s holy trinity. Interacting with ML. Base type: Integers. Base type: Strings. *Notes from Sorin Lerner at UCSD*
OCaml 1. Introduction Rapid introduction to what s in OCaml 2. Focus on Features Individually as Needed as Semester Progresses *Notes from Sorin Lerner at UCSD* History, Variants Meta Language Designed
DBMS Data Loading: An Analysis on Modern Hardware. Adam Dziedzic, Manos Karpathiotakis*, Ioannis Alagiannis, Raja Appuswamy, Anastasia Ailamaki
DBMS Data Loading: An Analysis on Modern Hardware Adam Dziedzic, Manos Karpathiotakis*, Ioannis Alagiannis, Raja Appuswamy, Anastasia Ailamaki Data loading: A necessary evil Volume => Expensive 4 zettabytes
Modern RAID Technology. RAID Primer A Configuration Guide
Modern RAID Technology RAID Primer A Configuration Guide E x c e l l e n c e i n C o n t r o l l e r s Modern RAID Technology RAID Primer A Configuration Guide 6th Edition Copyright 1997-2003 ICP vortex
Any questions. Say hello to OCaml. Say hello to OCaml. Why readability matters. History, Variants. Plan (next 4 weeks)
Any questions Say hello to OCaml? void sort(int arr[], int beg, int end){ if (end > beg + 1){ int piv = arr[beg]; int l = beg + 1; int r = end; while (l!= r-1){ if(arr[l]
Column Store Internals
Column Store Internals Sebastian Meine SQL Stylist with sqlity.net sebastian@sqlity.net Outline Outline Column Store Storage Aggregates Batch Processing History 1 History First mention of idea to cluster
Polymorphism. Zimmer CSCI 330
Polymorphism Polymorphism - is the property of OOP that allows the run-time binding of a function's name to the code that implements the function. (Run-time binding to the starting address of the code.)
Efficient Computation of Data Cubes. Network Database Lab
Efficient Computation of Data Cubes Network Database Lab Outlines Introduction Some CUBE Algorithms ArrayCube PartitionedCube and MemoryCube Bottom-Up Cube (BUC) Conclusions References Network Database
from bits to systems
class 2 from bits to systems prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ today logistics, goals, etc big data & systems (cont d) designing a data system algorithm: what can go wrong
column-stores basics
class 3 column-stores basics prof. HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS265/ Goetz Graefe Google Research guest lecture Justin Levandoski Microsoft Research projects option 1: systems project (now
Programming Languages
CSE 130 : Fall 2008 Programming Languages Lecture 2: A Crash Course in ML Ranjit Jhala UC San Diego News On webpage: Suggested HW #1, sample for Quiz #1 on Thu PA #1 (due next Fri 10/10) No make-up quizzes
Programming Languages
CSE 130: Spring 2010 Programming Languages Lecture 2: A Crash Course in ML Ranjit Jhala UC San Diego News On webpage: Suggested HW #1 PA #1 (due next Wed 4/9) Please post questions to WebCT Today: A crash
Towards Model-based Management of Database Fragmentation
Towards Model-based Management of Database Fragmentation Asim Ali, Abdelkarim Erradi, Rashid Hadjidj Qatar University Rui Jia, Sherif Abdelwahed Mississippi State University Outline p Introduction p Model-based
Operator overloading. Conversions. friend. inline
Operator overloading Conversions friend inline. Operator Overloading Operators like +, -, *, are actually methods, and can be overloaded. Syntactic sugar. What is it good for - 1 Natural usage. compare:
Memory Hierarchies && The New Bottleneck == Cache Conscious Data Access. Martin Grund
Memory Hierarchies && The New Bottleneck == Cache Conscious Data Access Martin Grund Agenda Key Question: What is the memory hierarchy and how to exploit it? What to take home How is computer memory organized.
class 11 b-trees prof. Stratos Idreos
class 11 b-trees prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ Midway check-in: Two design docs tmr (Canvas) & tests on Sunday Next weekend: Lab marathon for midway check-in & tests
Benchmarking Adaptive Indexing
Benchmarking Adaptive Indexing Goetz Graefe 2, Stratos Idreos 1, Harumi Kuno 2, and Stefan Manegold 1 1 CWI Amsterdam The Netherlands first.last@cwi.nl 2 Hewlett-Packard Laboratories Palo Alto, CA first.last@hp.com
Title: How Do I Use Keypads in EZware- 500?
Title: How Do I Use Keypads in EZware- 500? Article Number: TN1071 Date: 07/18/05 Information in this article applies to: EZware-500 HMI Product(s) Silver/HMI500 Series Controller (PLC) Product(s) All
Programming Languages
CSE 130: Fall 2009 Programming Languages Lecture 2: A Crash Course in ML News On webpage: Suggested HW #1 PA #1 (due next Fri 10/9) Technical issues installing Ocaml - should be resolved soon! Ranjit Jhala
CSCI-UA /002, Fall 2012 RK Lab: Approximiate document matching Due: Sep 26 11:59PM
CSCI-UA.0201-001/002, Fall 2012 RK Lab: Approximiate document matching Due: Sep 26 11:59PM 1 Introduction In many scenarios, we would like to know how similar two documents are are to each other. For example,
DRepl. Optimizing Access to Application Data for Analysis and Visualization. Latchesar Ionkov Michael Lang LANL. Carlos Maltzahn UCSC
DRepl Optimizing Access to Application Data for Analysis and Visualization Latchesar Ionkov Michael Lang LANL Carlos Maltzahn UCSC HPC Cluster Desktop Desktop Desktop Desktop Head Node FS CN1 CN2 CNk IO1
class 8 b-trees prof. Stratos Idreos
class 8 b-trees prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS165/ I spend a lot of time debugging am I doing something wrong? maybe but probably not 1. learn to use gdb 2. after spending
Advanced C++ Topics. Alexander Warg, 2017
www.kernkonzept.com Advanced C++ Topics Alexander Warg, 2017 M I C R O K E R N E L M A D E I N G E R M A N Y Overview WHAT IS BEHIND C++ Language Magics Object Life Time Object Memory Layout INTRODUCTION
Simple Low Concentrating Module Design Increases Solar Cell Output 25%
Simple Low Concentrating Module Design Increases Solar Cell Output 25% 3D Solar: The Next Dimension In Solar Power May 16, 2012 to WREF 2012 Denver, CO by Daniel Simon Overview of WREF 2012 Paper 3D Solar
DKT 122/3 DIGITAL SYSTEM 1
Company LOGO DKT 122/3 DIGITAL SYSTEM 1 BOOLEAN ALGEBRA (PART 2) Boolean Algebra Contents Boolean Operations & Expression Laws & Rules of Boolean algebra DeMorgan s Theorems Boolean analysis of logic circuits
CSE 344 Final Review. August 16 th
CSE 344 Final Review August 16 th Final In class on Friday One sheet of notes, front and back cost formulas also provided Practice exam on web site Good luck! Primary Topics Parallel DBs parallel join
data systems 101 prof. Stratos Idreos class 2
class 2 data systems 101 prof. Stratos Idreos HTTP://DASLAB.SEAS.HARVARD.EDU/CLASSES/CS265/ 2 classes per week - OH/Labs every day 1 presentation/discussion lead - 2 reviews each week research (or systems)
Accelerating Analytical Workloads
Accelerating Analytical Workloads Thomas Neumann Technische Universität München April 15, 2014 Scale Out in Big Data Analytics Big Data usually means data is distributed Scale out to process very large
Data Page Layouts for Relational Databases on Deep Memory Hierarchies
Data Page Layouts for Relational Databases on Deep Memory Hierarchies Anastassia Ailamaki David J. DeWitt Mark D. Hill Carnegie Mellon University natassa@cmu.edu University of Wisconsin - Madison {dewitt,
Shopping Cart: Queries, Personalizations, Filters, and Settings
Shopping Cart: Queries, Personalizations, Filters, and Settings on the Shopping Cart Home Page Use this Job Aid to: Learn how to organize the Shopping Cart home page so that it is easier to use. BEFORE
MDF4 Lib. Product Information
Product Information Table of Contents 1 Overview...3 1.1 Introduction...3 1.2 Application Areas...3 1.3 Overview of Advantages...3 2 Features and Advantages...4 2.1 Supported MDF Versions...4 3 Functional
CS 265. Computer Architecture. Wei Lu, Ph.D., P.Eng.
CS 265 Computer Architecture Wei Lu, Ph.D., P.Eng. 1 Part 1: Data Representation Our goal: revisit and re-establish fundamental of mathematics for the computer architecture course Overview: what are bits
Sung-Eun Choi and Steve Deitz Cray Inc.
Sung-Eun Choi and Steve Deitz Cray Inc. Domains and Arrays Overview Arithmetic Other Domain Types Data Parallel Operations Examples Chapel: Data Parallelism 2 A first-class index set Specifies size and
55144 SQL Server 2014 Performance Tuning and Optimization Microsoft Official Curriculum (MOC 55144)
55144 SQL Server 2014 Performance Tuning and Optimization Microsoft Official Curriculum (MOC 55144) Course Length: 5 days Course Delivery: Traditional Classroom Online Live Course Overview This five day
Meta-Programming and JIT Compilation
Meta-Programming and JIT Compilation Sean Treichler 1 Portability vs. Performance Many scientific codes sp ~100% of their cycles in a tiny fraction of the code base We want these kernels to be as fast
Evolution of Implementation Technologies. ECE 4211/5211 Rapid Prototyping with FPGAs. Gate Array Technology (IBM s) Programmable Logic
ECE 42/52 Rapid Prototyping with FPGAs Dr. Charlie Wang Department of Electrical and Computer Engineering University of Colorado at Colorado Springs Evolution of Implementation Technologies Discrete devices:
L2 cache provides additional on-chip caching space. L2 cache captures misses from L1 cache. Summary
HY425 Lecture 13: Improving Cache Performance Dimitrios S. Nikolopoulos University of Crete and FORTH-ICS November 25, 2011 Dimitrios S. Nikolopoulos HY425 Lecture 13: Improving Cache Performance 1 / 40
An application: foreign function bindings
1/ 19 An application: foreign function bindings C int puts ( const char *s); 2/ 19 C in two minutes object types numeric types int, char, float,... pointers int *, char *, int **,... structures and unions
Blanket Execution: Dynamic Similarity Testing for Program Binaries and Components
Blanket Execution: Dynamic Similarity Testing for Program Binaries and Components Manuel Egele, Maverick Woo, Peter Chapman, and David Brumley Carnegie Mellon University 1 Picture Yourself as an Analyst
Implementing Higher-Level Languages. Quick tour of programming language implementation techniques. From the Java level to the C level.
Implementing Higher-Level Languages Quick tour of programming language implementation techniques. From the Java level to the C level. Ahead-of-time compiler compile time C source code C compiler x86 assembly
DASlab: The Data Systems Laboratory
DASlab: The Data Systems Laboratory at Harvard SEAS Stratos Idreos Harvard University http://daslab.seas.harvard.edu ABSTRACT DASlab is a new laboratory at the Harvard School of Engineering and Applied
int x = 15; int &reftox = x; reftox = 3; // reftox is a synonym for x cout 1.4 Euler Diagram Layout Techniques
1.4 Euler Diagram Layout Techniques Euler Diagram Layout Techniques: Overview Dual graph based methods Inductive methods Drawing with circles Including software demos. How is the drawing problem stated?
Chapter 6 Caches. Computer System. Alpha Chip Photo. Topics. Memory Hierarchy Locality of Reference SRAM Caches Direct Mapped Associative
Chapter 6 s Topics Memory Hierarchy Locality of Reference SRAM s Direct Mapped Associative Computer System Processor interrupt On-chip cache s s Memory-I/O bus bus Net cache Row cache Disk cache Memory
C++ (Non for C Programmer) (BT307) 40 Hours
C++ (Non for C Programmer) (BT307) 40 Hours Overview C++ is undoubtedly one of the most widely used programming language for implementing object-oriented systems. The C++ language is based on the popular
CISC-124. Casting. // this would fail because we can t assign a double value to an int // variable
CISC-124 20180122 Today we looked at casting, conditionals and loops. Casting Casting is a simple method for converting one type of number to another, when the original type cannot be simply assigned to
Databricks, an Introduction
Databricks, an Introduction Chuck Connell, Insight Digital Innovation Insight Presentation Speaker Bio Senior Data Architect at Insight Digital Innovation Focus on Azure big data services HDInsight/Hadoop,
PREPROCESSING FOR ADVANCED DATA ANALYSIS
PREPROCESSING FOR ADVANCED DATA ANALYSIS PSYC696B CHAP 7 JL SANGUINETTI Preprocessing Reorganization Transformation Data analysis Collecting EEG Organize Extracting epochs Adjusting event codes Removing
Andrew Pavlo, Erik Paulson, Alexander Rasin, Daniel Abadi, David DeWitt, Samuel Madden, and Michael Stonebraker SIGMOD'09. Presented by: Daniel Isaacs
Andrew Pavlo, Erik Paulson, Alexander Rasin, Daniel Abadi, David DeWitt, Samuel Madden, and Michael Stonebraker SIGMOD'09 Presented by: Daniel Isaacs It all starts with cluster computing. MapReduce Why
names names identifiers variables subroutines constants
names (source: Louden, "Programming Languages, Principles and Practices", 2nd Edition, Ch. 5, pp. 125-134)!p. 126: "A fundamental abstraction mechanism in a programming language is the use of names, or
Data Warehousing and Data Mining
Data Warehousing and Data Mining Lecture 3 Efficient Cube Computation CITS3401 CITS5504 Wei Liu School of Computer Science and Software Engineering Faculty of Engineering, Computing and Mathematics Acknowledgement:
Semantic Analysis. Compiler Architecture
Processing Systems Prof. Mohamed Hamada Software Engineering Lab. The University of Aizu Japan Source Compiler Architecture Front End Scanner (lexical tokens Parser (syntax Parse tree Semantic Analysis
DATABASE Management Systems (DBMSs) have been
EDIC RESEARCH PROPOSAL 1 Towards Adaptive, Flexible, and Self-tuned Database Systems Ioannis Alagiannis DIAS, I&C, EPFL Abstract Modern database management systems (DBMSs) are used to answer complex queries
Efficient in-network evaluation of multiple queries
Efficient in-network evaluation of multiple queries Vinayaka Pandit 1 and Hui-bo Ji 2 1 IBM India Research Laboratory, email:pvinayak@in.ibm.com 2 Australian National University, email: hui-bo.ji@anu.edu.au
BITG 1113: POINTER LECTURE 12
BITG 1113: POINTER LECTURE 12 1 LEARNING OUTCOMES At the end of this lecture, you should be able to: 1. Describe the concept of pointer. 2. Write declaration and initialization of a pointer. 3. Do arithmetic
University of Waterloo Department of Electrical and Computer Engineering ECE 250 Data Structures and Algorithms. Final Examination
University of Waterloo Department of Electrical and Computer Engineering ECE 250 Data Structures and Algorithms Instructor: Douglas Wilhelm Harder Time: 2.5 hours Aides: none 14 pages Final Examination
NAMESPACES IN C++ You can refer the Programming with ANSI C++ by Bhushan Trivedi for Understanding Namespaces Better(Chapter 14)
NAMESPACES IN C++ You can refer the Programming with ANSI C++ by Bhushan Trivedi for Understanding Namespaces Better(Chapter 14) Some Material for your reference: Consider following C++ program. // A program