Education for all..

1. Data WarehousingData Warehousing
11
Data WarehousingData Warehousing
Lecture-24Lecture-24
Need for Speed: ParallelismNeed for Speed: Parallelism
Virtual University of PakistanVirtual University of Pakistan
Ahsan Abdullah
Assoc. Prof. & Head
Center for Agro-Informatics Research
www.nu.edu.pk/cairindex.asp
National University of Computers & Emerging Sciences, Islamabad
Email: ahsan101@yahoo.com

2. Data Warehousing
2
BackgroundBackground

3. Data Warehousing
3
When to parallelize?When to parallelize?
Useful for operations that access significant amountsUseful for operations that access significant amounts
of data.of data.
Useful for operations that can be implementedUseful for operations that can be implemented
independent of each other “Divide-&-Conquer”independent of each other “Divide-&-Conquer”
Parallel execution improves processing for:Parallel execution improves processing for:
 Large table scans and joinsLarge table scans and joins
 Creation of large indexesCreation of large indexes
 Partitioned index scansPartitioned index scans
 Bulk inserts, updates, and deletesBulk inserts, updates, and deletes
 Aggregations and copyingAggregations and copying
SizeSize
SizeSize
D&CD&C
SizeSize
D&CD&C

4. Data Warehousing
4
Are you ready to parallelize?Are you ready to parallelize?
Parallelism can be exploited, if there is…Parallelism can be exploited, if there is…
 Symmetric multi-processors (SMP), clusters, or MassivelySymmetric multi-processors (SMP), clusters, or Massively
Parallel (MPP) systemsParallel (MPP) systems ANDAND
 Sufficient I/O bandwidthSufficient I/O bandwidth ANDAND
 Underutilized or intermittently used CPUs (for example,Underutilized or intermittently used CPUs (for example,
systems where CPU usage is typically less than 30%)systems where CPU usage is typically less than 30%) ANDAND
 Sufficient memory to support additional memory-intensiveSufficient memory to support additional memory-intensive
processes such as sorts, hashing, and I/O buffersprocesses such as sorts, hashing, and I/O buffers
Word of cautionWord of caution
Parallelism can reduce system performance on over-utilized
systems or systems with small I/O bandwidth.

5. Data Warehousing
5
Scalability – Size is NOT everythingScalability – Size is NOT everything
Number of ConcurrentNumber of Concurrent
UsersUsers
• Simple table retrieval
• Moderate complexity Join
• Propensity analysis
• Clustering
Complexity of TechniqueComplexity of Technique
• Hash based
• B-Tree
• Multiple
• Bitmapped
Index usageIndex usage
Amount of detailed dataAmount of detailed data
Complexity of Data ModelComplexity of Data Model



 



6. Data Warehousing
6
Scalability- Speed-Up & Scale-UpScalability- Speed-Up & Scale-Up
Speed-UpSpeed-Up
More resources means
proportionally less time
for given amount of data.
Scale-UpScale-Up
If resources increased in
proportion to increase in
data size, time is constant.
Degree of Parallelism
Transactions/Sec
Degree of Parallelism
Secs/Transaction
Ideal
Ideal
Real

7. Data Warehousing
7
Quantifying Speed-upQuantifying Speed-up
Sequential Execution IdealIdeal Parallel Execution
18 time units 6 time units
Task-1 Task-2 Task-3
Control work (“overhead”)
Speedup = 18 = 300%
6
Ts: Time on serial processor
Tm: Time on multiple processors
Speedup =
Ts
Tm
Task-1
Task-2
Task-3

8. Data Warehousing
8
Speed-Up & Amdahl’s LawSpeed-Up & Amdahl’s Law
Reveals maximum expected speedup from parallel
algorithms given the proportion of task that must be
computed sequentially. It gives the speedup S as
ff is the fraction of the problem that must be computed sequentially
NN is the number of processors
As ff approaches 0, SS approaches NN
Example-1: ff = 5% and NN = 100 then SS = 16.8
Example-2: ff = 10% and NN = 200 then SS = 9.56
Not
1:1
Ratio
Only formula and explanation will go to graphics

9. Data Warehousing
9
Amdahl’s Law: Limits of parallelizationAmdahl’s Law: Limits of parallelization
For less than 80% parallelism, the speedup drastically drops.
At 90% parallelism, 128128 processors give performance of less than 1010
processors.
1
2
3
4
5
6
7
8
9
10
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
% sequential code (f)
Speedup(S)
N=2 N=4 N=8 N=16 N=32 N=64 N=128

10. Data Warehousing
10
Parallelization OLTP Vs. DSSParallelization OLTP Vs. DSS
There is a big difference.
DSS
Parallelization of a SINGLE query
OLTP
Parallelization of MULTIPLE queries
Or Batch updates in parallel

11. Data Warehousing
11
Brief Intro to Parallel ProcessingBrief Intro to Parallel Processing
 Parallel Hardware ArchitecturesParallel Hardware Architectures
 Symmetric Multi Processing (SMP)Symmetric Multi Processing (SMP)
 Distributed Memory or Massively Parallel ProcessingDistributed Memory or Massively Parallel Processing
(MPP)(MPP)
 Non-uniform Memory Access (NUMA)Non-uniform Memory Access (NUMA)
 Parallel Software ArchitecturesParallel Software Architectures
 Shared MemoryShared Memory
 Shard DiskShard Disk
 Shared NothingShared Nothing
 Types of parallelismTypes of parallelism
 Data ParallelismData Parallelism
 Spatial ParallelismSpatial Parallelism
Shared Everything

12. Data Warehousing
12
Symmetrical Multi Processing (SMP)Symmetrical Multi Processing (SMP)
 A number of independent I/O and number ofA number of independent I/O and number of
processors all sharing access to a single large memoryprocessors all sharing access to a single large memory
space.space.
Main Memory
I/O I/O I/O P1 P2 P3 P4
 Typically each CPU executes its job independently.Typically each CPU executes its job independently.
 Supports both Multi-Tasking and Parallel Processing.Supports both Multi-Tasking and Parallel Processing.
 Have to deal with issues such as Cache Coherence, Processor AffinityHave to deal with issues such as Cache Coherence, Processor Affinity
and Hot Spots.and Hot Spots.
Yellow will not go to graphics

13. Data Warehousing
13
 Composed of a number of self-contained, self-controlled nodesComposed of a number of self-contained, self-controlled nodes
connected through a network interface.connected through a network interface.
 Each node contains its own CPU, processor, memory and I/O.Each node contains its own CPU, processor, memory and I/O.
 Architecture better known as Massively Parallel Processing (MPP)Architecture better known as Massively Parallel Processing (MPP)
or cluster computing.or cluster computing.
 Memory is distributed across all nodes.Memory is distributed across all nodes.
Distributed Memory MachinesDistributed Memory Machines
Bus, Switch or Network
I/O P
Memory
I/O P
Memory
I/O P
Memory
 Network has the tendency to become the bottleneck.Network has the tendency to become the bottleneck.
 Issues fundamentally different from those in SMP.Issues fundamentally different from those in SMP.
Yellow will not go to graphics
Node

14. Data Warehousing
14
A little bit of both worlds !A little bit of both worlds !
Distributed Shared Memory MachinesDistributed Shared Memory Machines
Interconnection
Network
Main Memory
I/O I/O I/O P1 P2 P3 P4
Main Memory
I/O I/O I/O P1 P2 P3 P4
Main Memory
I/O I/O I/O P1 P2 P3 P4
Main Memory
I/O I/O I/O P1 P2 P3 P4

15. Data Warehousing
15
Shared disk RDBMS ArchitectureShared disk RDBMS Architecture
Clients/Users
Shared Disk
Interconnect
Adv
High level of fault tolerance
Dis Adv
Serialization due to locking
Interconnect can become a bottleneck
Yellow ill not go to graphics

16. Data Warehousing
16
Shared Nothing RDBMS ArchitectureShared Nothing RDBMS Architecture
Clients/Users
Adv
Data ownership changes infrequently
There is no locking
Dis Adv
Data availability low on failure
Very careful with data distribution
Redistribution is expensive
Yellow ill not go to graphics

17. Data Warehousing
17
Shared disk Vs. Shared Nothing RDBMSShared disk Vs. Shared Nothing RDBMS
 Important note:Important note: Do not confuse RDBMSDo not confuse RDBMS
architecture with hardware architecture.architecture with hardware architecture.
 Shared nothing databases can run on sharedShared nothing databases can run on shared
everything (SMP or NUMA) hardware.everything (SMP or NUMA) hardware.
 Shared disk databases can run on sharedShared disk databases can run on shared
nothing (MPP) hardware.nothing (MPP) hardware.
This slide will not go to graphics