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High Performance Cloud Computing

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Amazon Web Services
Amazon Web Services

A discussion of high performance computing, including high throughput, tightly coupled, parallel jobs and map/reduce, on Amazon EC2.

TecnologiaNegócios
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High Performance Cloud Computing Matt Wood T E C H N O L O G Y E VA N G E L I S T
Hello.
Thank you.
HPC with AMAZON WEB SERVICES
Tightly coup Hi gh throughput led, parallel HPC with AMAZON WEB SERVICES Data intensive, G etting star ted, map/reduce pr icing & access
Prelude
Infrastructure services
Idea Results
Idea Results Heavy lifting
Scale Redundancy Orchestration 70% Idea Results Heavy lifting Capacity Management Procurement
30% Idea Results Infrastructure
Idea Results AWS
Idea Results AWS
Compute
Compute Storage
Compute Storage Databases
Compute Storage Services Databases & Support
Dependable
API Compute Storage Services Databases & Support Fault tolerant infrastructure
API Compute Storage Services Databases & Support Fault tolerant infrastructure
Flexible
Compute Storage Services Databases & Support
Compute Storage Services Databases & Support
CentOS Ubuntu RHEL Windows Compute Storage Services Databases & Support
Low cost
PAYG
Gb/month
Compute/hour
Economies of scale
1 High Throughput Computing
Embarrassingly parallel
Independent tasks
Rapid data generation
Batch processing
Constraints
Constrained by capacity
More molecules Bigger systems Constrained by capacity More simulations More dimensions
Constrained by time
Upcoming conference Grant submissions Constrained by time Impatience! Exploratory “spike” run
Elastic capacity
EC2 Elastic Compute Cloud
Instances
Virtualised
Linux 20 seconds
Windows A few minutes
ec2-run-instances
mza$ ssh -i web/us-east/aws-web.pem root@ec2-204-236-247-169.compute-1.amazonaws.com Last login: Wed Jun 22 11:15:20 2011 from 82.26.6.99 __| __|_ ) CentOS _| ( / v5.4 ___|___|___| HVMx64 Welcome to an EC2 Public Image :-) [root@ip-10-17-135-244 ~]#
ec2-terminate-instances
Sandbox
Custom virtual machines
Instance sizes
Small (and micro)
1.7Gb 1. 0GHz RAM Small (and micro)
Large & Extra large
15Gb 4 cores RAM Large & Extra large
68Gb 8 cores RAM High memory & High CPU
Cluster compute
Duel Intel 23Gb R i7 AM gpGPU “Neha lem” Hardware virtualisati on 1.7Tb Fast scratch interconne cts Cluster compute
Rapid provisioning
10k in 45 minutes
High scale
Elastic capacity
Capacity Estimated demand Time
Capacity Infrastructure Investment Estimated demand Time
Capacity Infrastructure Real demand Time
Capacity Elastic capacity Real demand Time
Optimise for throughput
Tasks Instances
Tasks Queue Instances
Tasks Queue Instances
Vertical scale
Tasks Queue Instances Increase instance s ize
Tasks Queue Instances Increase instance s ize
Horizontal scale
Tasks Queue Instances Increase instance count
Tasks Queue Instances Results Store
Tasks Queue On-premise Instances Results Store
Tasks Queue On-premise Instances Results Store
Tasks Queue On-premise Instances Results Store
Optimise for cost Maximise bang for buck
Bang for buck: Instance size
Bang for buck: Monitoring & metrics
Bang for buck: Cost options
On-demand
Reserved Instances
Spot Instances
Built for batch
Persistent requests
All or nothing
Mix and match
Tools
Wide ecosystem
Oracle Grid Engine
LSF
Condor
Rocks+
MIT StarCluster
Slurm
Use existing tools
Reuse existing tools
Reproducibility
http://www.cloudbiolinux.com/
http://usegalaxy.org/cloud
2 Data Intensive Computing
Big data is a big opportunity
Idea Results
Enter Hadoop
map/reduce
Independent computation
Distributed platform
HDFS
High scale > 1000 nodes
Painful Set up. Configure. Optimise. Run.
Elastic MapReduce
Undifferentiated heavy lifting
S3 Input data
S3 Input data Code Elastic MapReduce
S3 Input data Code Elastic Name MapReduce node
S3 Input data Code Elastic Name MapReduce node Elastic cluster
S3 Input data Code Elastic Name MapReduce node HDFS Elastic cluster
S3 Input data Code Elastic Name MapReduce node Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster
S3 Input data Code Elastic Name Output MapReduce node S3 + SimpleDB Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster
S3 Input data Elastic Output MapReduce S3 + SimpleDB
Crossbow: Rapid whole genome SNP analysis Preprocessed reads Map: Bowtie Sort: Bin and partition Reduce: SoapSNP Langmead B, Schatz MC, Lin, J, Pop M, Salzberg SL. Genome Biol 10(11): R134.
CloudBurst Catalog k-mers Collect seeds End-to-end alignment http://cloudburst-bio.sourceforge.net; Bioinformatics 2009 25: 1363-1369
Data location
Generate in the cloud
Upload
Free inbound bandwidth
Import/export
Maximise value from the upload
Public Hosted Datasets
Ensembl
1000 Genomes
Genbank
3 Parallel Computing
Tightly coupled
Duel Intel i7 23Gb R AM “Ne halem” gpGPU Hardware vir tualisation 1.7Tb scratch Cluster compute
10 gig E Cluster compute
Placement group Cluster compute
250th
450th
Cores 7040 R max 41.82 R peak 82.51
GPU
2 x Tesla 800+ cores
4 Getting Started
HPC
Accessible
Flexible
Team access
Identity and access
API level rights management
Account
Billing Account credentials Account MFA
Account Student Admin Faculty Post-doc Roles
Account Student Admin Faculty Post-doc Roles Sally Robert Users Chris
Security credentials Multifactor authentication Management console access Data read/write access API level access
Account Student Admin Faculty Post-doc Roles Sally Robert Users Chris
Detailed accounting
Education grants
JISC
Free tier
HPC primer
HPC primer aws.amazon.com/hpc
Security
Shared responsibility
Certified + audited ISO 27001 SAS 70 Type II HIPPA and FISMA
Data stays local
aws.amazon.com/security
aws.amazon.com
Thank you!
Q U E S T I O N S + C O M M E N T S matthew@amazon.com @mza O N T W I T T E R

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High Performance Cloud Computing

Notas do Editor

  1. Good morning, my name is X, I'm Y for Amazon Web Services, based in Singapore.\nToday we will talk about Cloud Computing, and explain to you why it's important to know about it.\n
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  31. There has been a lot of interest in cloud computing from the life science community lately. Here are examples of two papers. There have also been articles in Nature\n
  32. If you use Facebook, you surely know Farmville, the most successful game so far.\nThe company behind Farmville is Zynga.com.\n
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  79. Another example, also a hedge fund. Lot more spiky since they do High Frequency Trading\n
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  150. Map: Catalog K-mers\nEmit k-mers in the genome and reads\n\nShuffle: Collect Seeds\nConceptually build a hash table of k-mers and their occurrences\n\nReduce: End-to-end alignment\nIf read aligns end-to-end with ≤ k errors, record the alignment\n
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