www.OASUS.caGrid Grid

The Evolution from The Evolution from Parallel Processing to Modern Parallel Processing to Modern

Day ComputingDay Computing

Greg McLeanGreg McLeanVecdet Mehmet-AliVecdet Mehmet-Ali

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AgendaAgenda

Grid Computing

Introduction to Parallel Processing

Type of GridsWhy and When to Use GridEarly FindingsGrid Components

Considerations When using SAS Grid

SAS/CONNECTMP/CONNECT (example)

Questions / Comments

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Introduction to Introduction to Parallel ProcessingParallel Processing

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Sorted Deck

IllustrationIllustration

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Introduction to Introduction to Parallel ProcessingParallel Processing

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Sorted Deck

IllustrationIllustration

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Introduction to Introduction to Parallel ProcessingParallel Processing

1 Minute 30 Seconds

Standard Approach

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Sorted Deck

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45 Seconds

Parallel Approach

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Sorted Deck

Introduction to Introduction to Parallel ProcessingParallel Processing

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Introduction to Introduction to Parallel ProcessingParallel Processing

Parallel Processing Can Reduce Elapsed Time

“Pipeline Parallelism” Can Reduce Elapsed Time Even Further

Card Experiment vs. Parallel / Grid Computing

Optimal Number of Processes Can Reduce Elapsed Time

Some “Processors” Are Faster Than Others

Data / Software Preparation Is Almost Always Required

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i M a c

Machine XMachine X

Data

SAS/CONNECTSAS/CONNECT

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SAS/CONNECTSAS/CONNECT

%LET server=F8DEV01;OPTIONS REMOTE=server;SIGNON;RSUBMIT; data work.test; A = 10; run;ENDRSUBMIT;SIGNOFF;

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SAS/CONNECTSAS/CONNECT(Pre SAS Version 8)(Pre SAS Version 8)

Synchronous ProcessingSynchronous Processing

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LIBNAME IN ‘\\Server1\Input’;LIBNAME OUT ‘\\Server1\Output’;PROC SORT DATA=IN.DATA1; BY KEY;RUN;PROC SORT DATA=IN.DATA2; BY KEY;RUN;DATA OUT.FINAL; MERGE IN.DATA1 IN.DATA2; BY KEY;RUN;

SAS/CONNECTSAS/CONNECT(Pre SAS Version 8)(Pre SAS Version 8)

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SAS/CONNECTSAS/CONNECT(Pre SAS Version 8)(Pre SAS Version 8)

Sort Data1

Sort Data2

MergeBoth

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Sort Data1

SortData2

MergeBoth

Results

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iM ac

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SAS/CONNECTSAS/CONNECT(Starting In SAS Version 8)(Starting In SAS Version 8)

MP/CONNECTMP/CONNECT

Asynchronous ProcessingAsynchronous Processing

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14

LIBNAME IN ‘\\Server1\Input’;LIBNAME OUT ‘\\Server1\Output’;PROC SORT DATA=IN.DATA1; BY KEY;RUN;PROC SORT DATA=IN.DATA2; BY KEY;RUN;DATA OUT.FINAL; MERGE IN.DATA1 IN.DATA2; BY KEY;RUN;

LIBNAME IN ‘\\Server1\Input’;PROC SORT DATA=IN.DATA1; BY KEY;RUN;

LIBNAME IN ‘\\Server1\Input’;PROC SORT DATA=IN.DATA2; BY KEY;RUN;

LIBNAME IN ‘\\Server1\Input’;LIBNAME OUT ‘\\Server1\Output’;DATA OUT.FINAL; MERGE IN.DATA1 IN.DATA2; BY KEY;RUN;

SAS/CONNECTSAS/CONNECT(Starting In SAS Version 8)(Starting In SAS Version 8)

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Sort Data1

Sort Data2

MergeBoth

MP/CONNECTMP/CONNECT

SortData2

MergeBoth

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SortData1

SortData2

SortData1

SortResults

SortResults

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MergeBoth Results

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MP/CONNECTMP/CONNECT

/****** SORT DATA1 ******/%LET remote1=F8DEV01;OPTIONS AUTOSIGNON=YES;RSUBMIT PROCESS=remote1 WAIT=NO; LIBNAME data1 "\\F8DEV01\PFM-System\Tools"; proc sort data=data1.data1; by city; run;ENDRSUBMIT;

/****** SORT DATA2 ******/%LET remote2=F8TEST01;OPTIONS AUTOSIGNON=YES;RSUBMIT PROCESS=remote2 WAIT=NO; LIBNAME data2 "\\F8DEV01\PFM-System\Tools"; proc sort data=data2.data2; by city; run;ENDRSUBMIT;

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MP/CONNECTMP/CONNECT

WAITFOR _all_ remote1 remote2

/****** MERGE DATA1 & DATA2 ******/%LET remote3=F8PROD01;OPTIONS AUTOSIGNON=YES;RSUBMIT PROCESS=remote3; LIBNAME both "\\F8DEV01\PFM-System\Tools"; data both.sorted; merge both.data1 both.data2; by city; run;ENDRSUBMIT;

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“A parallel processing architecture in which computer resources are shared across a network and all machines function as one large

supercomputer.”

Grid ComputingGrid Computing

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Utility Grid

Compute Grid

Multiple users that require processing

Multiple machines available to process

Dynamic allocation of process to available machine

Task that can be decomposed into sub-units

Sub-units dynamically allocated to available machines

Sub-units able to run in parallel

Grid ComputingGrid Computing

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Grid ComputingGrid ComputingWhy Use

Budget constraints

Higher volume of Data

Tighter processing schedules

Idle processing power of existing hardware

Centrally Managed Hardware & Infrastructure

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Grid ComputingGrid ComputingWhen To Use

Applications requiring hours / days to process

Applications that are more processing intensive

Applications that can be decomposed into sub-tasks

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Optimization in a grid of PC Laptops

Case 1

60 laptops (266 - 400 Mhz)

600 Sales Territories

87% Improvement

92% Improvement

Total Elapsed Time

Grid ComputingGrid ComputingEarly Findings

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Grid ComputingGrid ComputingEarly Findings

Case 2 – NIEHS - Heterogeneous Grid

99% Improvement

Total Elapsed Time

100 nodes running mixture of W2K, WXP, variety of Unix OS’s Combination of SAS v8 and SAS v9 on nodes

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Grid Infrastructure SAS® P

rogr

ams\

Data

Grid

Co

ntro

ller / Man

ager

SASSAS® Grid Solution Grid Solution

Grid ComputingGrid Computing

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Grid ComputingGrid ComputingGrid Infrastructure

SAS\CONNECT®

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Asynchronous

Connections

SAS\MPCONNECT®

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Grid ComputingGrid ComputingGrid Controller / Manager (Then)

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Grid ComputingGrid ComputingGrid Controller / Manager (Then)

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Grid ComputingGrid ComputingGrid Controller / Manager (Now)

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Grid ComputingGrid ComputingSAS® Programs\Data (Then & Now)

LIBNAME IN ‘\\Server1\Input’;LIBNAME OUT ‘\\Server1\Output’;PROC SORT DATA=IN.DATA1; BY KEY;RUN;PROC SORT DATA=IN.DATA2; BY KEY;RUN;DATA OUT.FINAL; MERGE IN.DATA1 IN.DATA2; BY KEY;RUN;

LIBNAME IN ‘\\Server1\Input’;PROC SORT DATA=IN.DATA1; BY KEY;RUN;

LIBNAME IN ‘\\Server1\Input’;PROC SORT DATA=IN.DATA2; BY KEY;RUN;

LIBNAME IN ‘\\Server1\Input’;LIBNAME OUT ‘\\Server1\Output’;DATA OUT.FINAL; MERGE IN.DATA1 IN.DATA2; BY KEY;RUN;

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Considerations When UsingConsiderations When UsingSAS GridSAS Grid

Vecdet Mehmet-Ali

SAS Grid Now @

Statistics Canada!

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From Dream to Reality – From Dream to Reality – Introducing the SAS GridIntroducing the SAS Grid

Presented to: Informatics Branch May 6, 2014

Yves DeGuireSection ChiefSAS Technology CenterSystem Engineering DivisionStatistics Canada

www.OASUS.caWhat is Grid Computing?What is Grid Computing?

• Emerged in the academic research community with 2 primary goals:

• Reduce overall elapsed processing time• Leverage commodity hardware

• Became mainstream with the SETI@Home project • Today: a sophisticated computer infrastructure for the

Enterprise with scalability, load balancing and high availability.

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Use Case #3: Use Case #3: Parallel ProcessingParallel Processing

Long running jobs broken into smaller tasks and dispatched to the grid.

Likely submitted as a batch job. SAS programs must be modified first using MP Connect directives:

Manually or Using SAS SCAPROC

Another option: the SAS Data Integration loop transformation The easiest: directly from EG process flow!

Myth: a SAS program will execute in parallel without any modifications!

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Parallel Processing & Grid Parallel Processing & Grid Computing with SASComputing with SAS

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G-TabG-Tab (Generalized Tabulation System) (Generalized Tabulation System)

Input:– Table specifications(xml)– Micro Data

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G-TabG-Tab (Generalized Tabulation System) (Generalized Tabulation System)

G-Tab

Inputdata

Xml file

TabulatedOutput

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G-TabG-Tab (Generalized Tabulation System) (Generalized Tabulation System)

Table specifications(xml) Domain variable list (Ex: Region, Province, AgeGroup, Sex, etc.) Analysis variable list (Ex: (Income, Expense, etc.) Weight variable (Ex: SWeight) Bootstrap weight variable specification (Ex: BSW1-BSW1000) Statistics:

• Level-1: (MEAN,MAX,MIN,SUM,N,SUMWGT,MEDIAN,P1,P5,..,P99)– Calculated by PROC MEANS on Micro Data

• Level-2: (GINI,GEOMEAN)– Calculated by special algorithm on Micro Data

• Level-3: (DISTRIBUTION,PROPORTION,RATIO)– Calculated by using the results of Level-1 statistics

– Example (RATIO): MEAN(Income) / MEAN(Expense)

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G-TabG-Tab (Generalized Tabulation System) (Generalized Tabulation System)

Precision Measures (Bootstrap Variance Method) VAR (Variance) STD (Standard Deviation) CV (Coefficient of Variation) CILB (Confidence Interval Lower Bound) CIUB (Confidence Interval Upper Bound) QI (Quality Indicator)

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G-TabG-Tab(Sequential Processing)(Sequential Processing)

Process Flow

Level-1 Level-2 Level-3 PrecisionMeasures

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G-TabG-Tab(Sequential Processing)(Sequential Processing)

Data Flow

Level-1Statistics

Level-2GINI

Level-3Statistics

PrecisionMeasures

Inputdata

Level-2GEOMEAN

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Considerations forConsiderations forParallel ProcessingParallel Processing

Can your job be divided into independent tasks? Many SAS programs contain modules that are independent. On a single server these tasks are performed sequentially. On the Grid they can be processed in parallel sessions.

Identify dependent and independent tasks A task is dependent if it requires output from another task

Finally consider the length of time required to process each task. If the tasks are short and take little time to process, you might

not be able to offset the time required to start up multiple Grid sessions.

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G-TabG-Tab(Task Dependency)(Task Dependency)

Data Flow

Level-1Statistics

Level-2GINI

Level-3Statistics

PrecisionMeasures

Inputdata

Level-2GEOMEAN

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G-TabG-TabProcessing on the GridProcessing on the Grid

PrecisionMeasures

InputData

split

Level-1Statistics

Level-2Gini

Level-2GeoMean

Level-3Statistics

G-TabGrid node

Grid node

Grid node

Grid node

Partialresult

Partialresult

Partialresult

Partialresult

FinalResult

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G-TabG-TabLevel-1 StatisticsLevel-1 Statistics

PrecisionMeasures

InputData

split

Level-1Statistics

G-TabGrid node

Partialresult

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Table specifications(xml) Domain variable list (Ex: Region, Province, AgeGroup, Sex, etc.) Analysis variable list (Ex: (Income, Expense, etc.) Weight variable (Ex: SWeight) Bootstrap weight variable specification (Ex: BSW1-BSW1000) Statistics:

• Level-1: (MEAN,MAX,MIN,SUM,N,SUMWGT,MEDIAN,P1,P5,..,P99)– Calculated by PROC MEANS on Micro Data

• Level-2: (GINI,GEOMEAN)– Calculated by special algorithm on Micro Data

• Level-3: (DISTRIBUTION,PROPORTION,RATIO)– Calculated from the results of Level-1 statistics – Example (RATIO): MEAN(Income) / MEAN(Expense)

G-TabG-Tab (Generalized Tabulation System) (Generalized Tabulation System)

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G-TabG-TabSample Input DataSample Input Data

Province AgeGroup Sex Income SWeight BSW1 BSW2 … … BSW1000

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Proc means data=.. noprint;Class province agegroup sex;Var income / sweight; /* (BSW1 – BSW1000) */Output out=.. Mean= ;

Run;

Repetitive task Split data for parallel processing

G-TabG-TabLevel-1 StatisticsLevel-1 Statistics

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Level-1 StatisticsLevel-1 StatisticsSub-task(1) Input DataSub-task(1) Input Data

Province AgeGroup Sex Income SWeight BSW1 BSW2 … … BSW250

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Level-1 StatisticsLevel-1 StatisticsSub-task(2) Input DataSub-task(2) Input Data

Province AgeGroup Sex Income SWeight BSW251 BSW252 … … BSW500

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Level-1 StatisticsLevel-1 StatisticsSub-task(3) Input DataSub-task(3) Input Data

Province AgeGroup Sex Income SWeight BSW501 BSW502 … … BSW750

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Level-1 StatisticsLevel-1 StatisticsSub-task(4) Input DataSub-task(4) Input Data

Province AgeGroup Sex Income SWeight BSW751 BSW752 … … BSW1000

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Level-1 StatisticsLevel-1 StatisticsSub-task(1) ResultsSub-task(1) Results

Province AgeGroup Sex Income_Mean Income1_Mean Income2_Mean … Income250_Mean

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Level-1 StatisticsLevel-1 StatisticsSub-task(2) ResultsSub-task(2) Results

Province AgeGroup Sex Income251_Mean Income252_Mean … Income500_Mean

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Level-1 StatisticsLevel-1 StatisticsSub-task(3) ResultsSub-task(3) Results

Province AgeGroup Sex Income501_Mean Income502_Mean … Income750_Mean

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Level-1 StatisticsLevel-1 StatisticsSub-task(4) ResultsSub-task(4) Results

Province AgeGroup Sex Income751_Mean Income752_Mean … Income1000_Mean

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Level-1 StatisticsLevel-1 StatisticsResultsResults

Province AgeGroup Sex Income_Mean Income1_Mean Income2_Mean … Income1000_Mean

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G-TabG-TabParallel ProcessingParallel Processing

Level-1Results

InputData

split

Level-1SWeight

BSW1-BSW250

Level-1BSW251-BSW500

Level-1BSW501-BSW750

Level-1BSW751-BSW1000

G-TabGrid node

Grid node

Grid node

Grid node

Partialresult

Partialresult

Partialresult

Partialresult

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G-TabG-TabPrecision MeasuresPrecision Measures

Let Y be the statistic to be considered. For example Ŷ can be a mean, a median, a sum, etc. The variance of Ŷ is given by:

𝑽൫𝒀 ൯= (𝒀 𝒋− 𝒀 )𝟐𝑩𝒋=𝟏

𝑺𝑻𝑫൫𝒀 ൯= ට𝑽(𝒀 ) 𝑪𝑽൫𝒀 ൯= 𝑺𝑻𝑫(𝒀 )

ห𝒀 ห

Where Ŷj is the statistic calculated using the jth Bootstrap weight, B is the number of Bootstrap weights, Ŷ is the estimate produced using the Survey weight.

Quality Indicator of the statistic is set based on the above calculations.

Standard Deviation:

Coefficient of Variation:

www.OASUS.caNotesNotes

In the example: The input data was sliced vertically into 4. This gave the BEST elapsed processing time for average surveys. Slicing it into 5 sessions(200 BSW each) took longer to run. For bigger volume, 5 sessions could give better results.

Other Considerations: Slice the input data horizontally

Time cycles

Warning: Maintain data integrity

www.OASUS.caConclusionConclusion

Grid is a sophisticated computer infrastructure for the Enterprise with scalability, load balancing and high availability.

A SAS program will NOT execute in parallel without any modifications! It must be modified first using MP Connect directives to run in parallel.

Long running jobs should be broken into smaller tasks and dispatched to the grid.

Parallel processing will reduce the overall elapsed processing time.

The Future Of Grid Computing Is Now Here!The Future Of Grid Computing Is Now Here!

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Questions / CommentsQuestions / Comments

Greg McLeanProject LeaderSystem Engineering DivisionStatistics CanadaJean Talon Building 5th Floor Section A6170, Tunney’s Pasture drivewayOttawa, Ont., K1A 0T6

(613) 951-2396

Greg.McLean@statcan.gc.ca

Vecdet Mehmet-AliProject LeaderSystem Engineering DivisionStatistics CanadaJean Talon Building 5th Floor Section A2 170, Tunney’s Pasture drivewayOttawa, Ont., K1A 0T6

(613) 951-2390

Vecdet.Mehmet-Ali@statcan.gc.ca