hpc on aws

HPC on Amazon Web Services

Deepak SinghAmazon Web Services

Dec 17, 2010

Image: Simon Cockell under CC-BY

the new reality

lots and lots and lots and lots and lots of data

lots and lots and lots and lots and lots of

compute

lots and lots and lots and lots and lots of

people

lots and lots and lots and lots and lots of

places

constant change

goal

innovate

innovate in a new reality

optimize the most valuable resource

compute, storage, workflows, memory,

transmission, algorithms, cost, …

people drive innovation

Credit: Pieter Musterd a CC-BY-NC-ND license

make people productive

Credit: Pieter Musterd a CC-BY-NC-ND license

challenges

Your Idea SuccessfulProduct

Your Idea SuccessfulProduct

Your Idea SuccessfulProduct

Great Idea Not Prioritized

Your Idea SuccessfulProduct

Great Idea Not Prioritized

Resource Contention

Tight Budgets

Shared Resources

enter the cloud

infrastructure services

building blocks

Undifferentiated Heavy Lifting

pay as you go

pay for what you use

on demand

programmable

import botoimport boto.emrfrom boto.emr.step import StreamingStepfrom boto.emr.bootstrap_action import BootstrapActionimport time

# set your aws keys and S3 bucket, e.g. from environment or .botoAWSKEY= SECRETKEY= S3_BUCKET=NUM_INSTANCES = 1

conn = boto.connect_emr(AWSKEY,SECRETKEY)

bootstrap_step = BootstrapAction("download.tst", "s3://elasticmapreduce/bootstrap-actions/download.sh",None)

step = StreamingStep(name='Wordcount',                     mapper='s3n://elasticmapreduce/samples/wordcount/wordSplitter.py',                     cache_files = ["s3n://" + S3_BUCKET + "/boto.mod#boto.mod"],                     reducer='aggregate',                     input='s3n://elasticmapreduce/samples/wordcount/input',                     output='s3n://' + S3_BUCKET + '/output/wordcount_output')

jobid = conn.run_jobflow(    name="testbootstrap",     log_uri="s3://" + S3_BUCKET + "/logs",     steps = [step],    bootstrap_actions=[bootstrap_step],    num_instances=NUM_INSTANCES)

print "finished spawning job (note: starting still takes time)"

state = conn.describe_jobflow(jobid).stateprint "job state = ", stateprint "job id = ", jobidwhile state != u'COMPLETED':    print time.localtime()    time.sleep(30)    state = conn.describe_jobflow(jobid).state    print "job state = ", state    print "job id = ", jobid

print "final output can be found in s3://" + S3_BUCKET + "/output" + TIMESTAMPprint "try: $ s3cmd sync s3://" + S3_BUCKET + "/output" + TIMESTAMP + " ."

Connect to Elastic MapReduce

Install packages

Set up mappers &reduces

job state

elastic

Capacity

Time

Realdemand

Elasticcapacity

On demand Faster to market

Pay as you go Maintain focus

Pay to play Efficiency

Elastic resources Capacity planning

Computing with Amazon EC2

Credit: Angel Pizzaro, U. Penn

Credit: Tom Fifield: U. Melbourne

standard “m1”high cpu “c1”

high memory “m2”

http://aws.amazon.com/ec2/instance-types/

EC2

inst

ance

type

s

listening to customers

new EC2 instance type

text

cluster compute instances

http://aws.amazon.com/ec2/instance-types/

2 * Xeon 5570 (“Intel Nehalem”)23 GB RAM

10 gbps Ethernet

1690 TB local disk

HVM-based virtualization

$1.60 / hr

10gbps

PlacementGroup

full bisection bandwidth

HPC on EC2 =

EC2 instance+

high bandwidth, low latency networking

http://aws.amazon.com/ec2/hpc-applications/

Linpack benchmark

880-instance CC1 clusterPerformance: 41.82 TFlops*

*#231 in the most recent Top 500 rankings

CFDMolecular ModelingSequence AnalysisEngineering Design

Energy Trading…

high I/O applications

standard “m1”high cpu “c1”

high memory “m2”

http://aws.amazon.com/ec2/instance-types/

cluster compute “cc1”

EC2

inst

ance

type

s

HPC is evolving

cluster GPU instances

http://aws.amazon.com/ec2/instance-types/

HPC on EC2 =

EC2 instance+

high bandwidth, low latency networking

+GPU

http://aws.amazon.com/ec2/hpc-applications/

2 * Xeon 5570 (“Intel Nehalem”)

22 GB RAM

10 gbps Ethernet

1690 TB local disk

HVM-based virtualization

$2.10 / hr

2 * Tesla M2050 GPU

standard “m1”high cpu “c1”

high memory “m2”

http://aws.amazon.com/ec2/instance-types/

cluster compute “cc1”

EC2

inst

ance

type

s

cluster GPU “cg1”

CFDMolecular DynamicsFinancial Modeling

RenderingVideo Processing

…What is your interest?

“90 percent scaling efficiency on clusters of up to 128 GPUs”

-- Mental Images iRay

Getting Started

http://aws.amazon.com/hpc-applications

4 steps

15 minutes

http://aws.amazon.com/ec2

ecosystem

ISV ecosystem

Mathworksmental imagesRevup Render

Elemental Technologies...

HPC with AWS

E2 instance+

high bandwidth, low latency networking

+Tesla GPU*

*optional

On demand Faster to market

Pay as you go Maintain focus

Pay to play Efficiency

Elastic resources Capacity planning

make people productive

Credit: Pieter Musterd a CC-BY-NC-ND license

Your Idea SuccessfulProduct

Great Idea Not Prioritized

Your Idea SuccessfulProduct

http://aws.amazon.com/hpc-applications

deesingh@amazon.com Twitter:@mndoci

http://slideshare.net/mndocihttp://mndoci.com

Inspiration and ideas from Matt Wood, James Hamilton

& Larry Lessig

Credit” Oberazzi under a CC-BY-NC-SA license