Patterns For Parallel Computing
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Transcript of Patterns For Parallel Computing
An architectural conversation
• Concepts• Patterns• Design Principles• Microsoft Platform
> Outline
Why is this interesting?
• Amdahl’s law (1967)• Multi-core processors• Virtualization• High-performance computing• Distributed architecture• Web–scale applications• Cloud computing
Paradigm shift!
> Concepts
Parallel Computing == ??
• Simultaneous multi-threading (Intel HyperThreading, IBM Cell microprocessor for PS3, etc.)
• Operating system multitasking (cooperative, preemptive; symmetric multi-processing, etc.)
• Server load-balancing & clustering (Oracle RAC, Windows HPC Server, etc.)
• Grid computing (SETI@home, Sun Grid, DataSynapse, DigiPede, etc.)
• Asynchronous programming (AJAX, JMS, MQ, event-driven, etc.)
• Multi-threaded & concurrent programming (java.lang.Thread, System.Thread, Click, LabVIEW, etc.)
• Massively parallel processing (MapReduce, Hadoop, Dryad, etc.)
Elements and best practices in all of these
> Concepts
Types of Parallelism
• Bit-level parallelism (microprocessors)
• Instruction-level parallelism (compilers)
• Multiprocessing, multi-tasking (operating systems)
• HPC, clustering (servers)
• Multi-threading (application code)
• Data parallelism (massive distributed databases)
• Task parallelism (concurrent distributed processing)
Focus is moving “up” the technology stack…
> Patterns
Clustering Infrastructure for High Availability
> Patterns > HPC, Clustering
High-Performance Computing
> Patterns > HPC, Clustering
Web/App Server
Browser
A-Z
Web/App Server
Browser
A-Z
Microsoft.com
• Infrastructure and Application Footprint– 7 Internet data centers & 3 CDN partnerships– 120+ Websites, 1000’s apps and 2500 databases – 20-30+ Gbits/sec Web traffic; 500+ Gbits/sec download traffic
• 2007 stats (microsoft.com): – #9 ranked domain in U.S; 54.0M UU for 36.0% reach– #5 site worldwide; reaching 287.3M UU– 15K req/sec, 35K concurrent connections on 80 servers– 600 vroots, 350 IIS Web apps & 12 app pools– Windows Server 2008, SQL Server 2008, IIS7, ASP.NET 3.5
• 2007 stats (Windows Update):– 350M UScans/day, 60K ASP.NET req/sec, 1.5M concurrent connections– 50B downloads for CY 2006– Update Egress – MS, Akamai, Level3 & Limelight (50-500+ Gbits/sec)
> Patterns > HPC, Clustering > Example
Multi-threaded programming
> Patterns > Multi-threading
Execution Time
Execution Time
Sequential Concurrent
Multi-threading
• Typically, functional decomposition into individual threads• But, explicit concurrent programming brings complexities
– Managing threads, semaphores, monitors, dead-locks, race conditions, mutual exclusion, synchronization, etc.
• Moving towards implicit parallelism– Integrating concurrency & coordination into mainstream programming
languages– Developing tools to ease development– Encapsulating parallelism in reusable components – Raising the semantic level: new approaches
> Patterns > Multi-threading
API
PIC
Metadata Membership
Web Browser
Content Pods
Content Pods
Content Pods
Content Pods
Thumbs
Content Pods
Content Pods
Content Pods
Content Pods
Images
Content Pods
Content Pods
Content Pods
Content Pods
Albums
Content Pods
Content Pods
Content Pods
Content Pods
Groups
Photobucket
• 2007 stats:– +30M searches processed / day– 25M UU/month in US, +46M worldwide– +7B images uploaded– +300K unique websites link to content– #31 top 50 sites in US– #41 top 100 sites worldwide– 18th largest ad supported site in US
> Patterns > Multi-threading > Example
• Scaling the performance:– Browser handles
concurrency– Centralized lookup– Horizontal partitioning of
distributed content
Data Parallelism
• Loop-level parallelism• Focuses on distributing the data across different parallel
computing nodes– Denormalization, sharding, horizontal partitioning, etc.
• Each processor performs the same task on different pieces of distributed data
• Emphasizes the distributed (parallelized) nature of the data• Ideal for data that is read more than written (scale vs.
consistency)
> Patterns > Data Parallelism
Parallelizing Data in Distributed Architecture
> Patterns > Data Parallelism
Web/App Server
Browser
A-Z
Web/App Server
Browser
A-M N-Z
Browser
H-M N-S
Web/App Server
A-G T-Z
Index
Web/App Server
Web/App Server
Flickr
• 2007 stats:– Serve 40,000 photos / second– Handle 100,000 cache operations / second– Process 130,000 database queries / second
> Patterns > Data Parallelism > Example
• Scaling the “read” data:– Data denormalization– Database replication and
federation• Vertical partitioning• Central cluster for index
lookups• Large data sets horizontally
partitioned as shards• Grow by binary hashing of
user buckets
MySpace
• 2007 stats:– 115B pageviews/month– 5M concurrent users @ peak– +3B images, mp3, videos– +10M new images/day– 160 Gbit/sec peak bandwidth
> Patterns > Data Parallelism > Example
• Scaling the “write” data:– MyCache: distributed dynamic memory cache– MyRelay: inter-node messaging transport handling +100K req/sec, directs
reads/writes to any node– MySpace Distributed File System: geographically redundant distributed
storage providing massive concurrent access to images, mp3, videos, etc.– MySpace Distributed Transaction Manager: broker for all non-transient
writes to databases/SAN, multi-phase commit across data centers
• 2009 stats:– +200B pageviews/month– >3.9T feed actions/day– +300M active users– >1B chat mesgs/day– 100M search queries/day– >6B minutes spent/day
(ranked #2 on Internet)
– +20B photos, +2B/month growth
– 600,000 photos served / sec
– 25TB log data / day processed thru Scribe
– 120M queries /sec on memcache
> Patterns > Data Parallelism > Example
• Scaling the “relational” data:– Keeps data normalized, randomly
distributed, accessed at high volumes– Uses “shared nothing” architecture
Task Parallelism
• Functional parallelism• Focuses on distributing execution processes (threads) across
different parallel computing nodes• Each processor executes a different thread (or process) on the
same or different data• Communication takes place usually to pass data from one thread
to the next as part of a workflow• Emphasizes the distributed (parallelized) nature of the
processing (i.e. threads)• Need to design how to compose partial output from concurrent
processes
> Patterns > Task Parallelism
• 2007 stats:– +20 petabytes of data processed / day by +100K MapReduce jobs – 1 petabyte sort took ~6 hours on ~4K servers replicated onto ~48K disks– +200 GFS clusters, each at 1-5K nodes, handling +5 petabytes of storage– ~40 GB/sec aggregate read/write throughput across the cluster– +500 servers for each search query < 500ms
> Patterns > Task Parallelism > Example
• Scaling the process:– MapReduce: parallel
processing framework– BigTable: structured
hash database– Google File System:
massively scalable distributed storage
Parallelism for Speedup
• Amdahl’s law (1967): • Amdahl’s speedup: • Gustafson’s law (1988): • Gustafson’s speedup: • Karp-Flatt metric (1990): • Speedup: • Efficiency:
> Design Principles
Parallelism for Scale-out
• Sequential Parallel– Convert sequential and/or single-machine program into a form in which it
can be executed in a concurrent, potentially distributed environment
• Over-decompose for scaling– Structured multi-threading with a data focus
• Relax sequential order to gain more parallelism– Ensure atomicity of unordered interactions
• Consider data as well as control flow– Careful data structure & locking choices to manage contention– User parallel data structures– Minimize shared data and synchronization
• Continuous optimization
> Design Principles
Amazon
• Principles for Scalable Service Design (Werner Vogels, CTO, Amazon)
> Design Principles > Example
– Autonomy– Asynchrony– Controlled concurrency– Controlled parallelism– Decentralize– Decompose into small
well-understood building blocks
– Failure tolerant– Local responsibility– Recovery built-in– Simplicity– Symmetry
Parallel computing on the Microsoft platform
• Concurrent Programming (.NET 4.0 Parallel APIs)
• Distributed Computing (CCR & DSS Runtime, Dryad)
• Cloud Computing (Azure Services Platform)
• Grid Computing (Windows HPC Server 2008)
• Massive Data Processing (SQL Server “Madison”)
Components spanning a spectrum of computing models
> Microsoft Platform
.NET 4.0 Parallel APIs
• Task Parallel Library (TPL)• Parallel LINQ (PLINQ)• Data Structures• Diagnostic Tools
> Microsoft Platform > Concurrent Programming
CCR & DSS Toolkit
• Supporting multi-core and concurrent applications by facilitating asynchronous operations
• Dealing with concurrency, exploiting parallel hardware and handling partial failure
• Supporting robust, distributed applications based on a light-weight state-driven service model
• Providing service composition, event notification, and data isolation
> Microsoft Platform > Distributed Computing
• Concurrency & Coordination Runtime
• Decentralized Software Services
Dryad
• General-purpose execution environment for distributed, data-parallel applications
• Automated management of resources, scheduling, distribution, monitoring, fault tolerance, accounting, etc.
• Concurrency and mutual exclusion semantics transparency• Higher-level and domain-specific language support
> Microsoft Platform > Distributed Computing
28
Windows Server
Cluster Services
Distributed Filesystem
Dryad
Distributed Shell
PSQL
DryadLINQ
PerlSQL
server
C++
Windows Server
Windows Server
Windows Server
C++
CIFS/NTFS
legacycode
sed, awk, grep, etc.
SSISQueries
C#
Vectors
Machine Learning
C#
Job
queu
eing
, mon
itorin
g
Azure Services Platform
• Internet-scale, highly available cloud fabric• Auto-provisioning 64-bit compute nodes on Windows Server VMs• Massively scalable distributed storage (table, blob, queue)• Massively scalable and highly consistent relational database
> Microsoft Platform > Cloud Computing
Table StorageService
Blob StorageService
QueueService
CacheService
ASP.NET(Web Role)
ASP.NET(Web Role)
ASP.NET(Web Role)
ASP.NET(Web Role)
ASP.NET(Web Role)
ASP.NET(Web Role)
ASP.NET(Web Role)
ASP.NET(Web Role)
ASP.NET(Web Role)
Web Svc(Web Role)
ASP.NET(Web Role)
ASP.NET(Web Role)
ASP.NET(Web Role)
ASP.NET(Web Role)
Jobs(Worker
Role)
Service Bus
Access Control Service
WorkflowService
Service Orch.
Identities & Roles
Conn. Bindings
Application Data
SQL Data Services
BIServices
Application Data
Reference Data
Windows HPC Server
• Image multicasting-based parallel deployment of cluster nodes• Fault tolerance with failover clustering of head node• Policy-driven, NUMA-aware, multicore-aware, job scheduler• Inter-process distributed communication via MS-MPI
> Microsoft Platform > Grid Computing
• #10 fastest supercomputer in the world (top500.org)– 30,720 cores– 180.6 teraflops– 77.5% efficiency
SQL Server “Madison”
– IO and CPU affinity within symmetric multi-processing (SMP) nodes– Multiple physical instances of tables w/ dynamic re-distribution
• Distribute / partition large tables across multiple nodes• Replicate small tables• Replicate + distribute medium tables
> Microsoft Platform > Massive Data Processing
• Massively parallel processing (MPP) architecture
• +500TB to PB’s databases• “Ultra Shared Nothing” design
For More Information
• Architect Council Website (blogs.msdn.com/sac)
– This series (blogs.msdn.com/sac/pages/council-2009q2.aspx)
• .NET 4.0 Parallel APIs (msdn.com/concurrency)
• CCR & DSS Toolkit (microsoft.com/ccrdss)
• Dryad (research.microsoft.com/dryad)
• Azure Services Platform (azure.com)
• SQL Server “Madison” (microsoft.com/madison)
• Windows HPC Server 2008 (microsoft.com/hpc)
> Resources
© 2009 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.
Thank you!
[email protected]/dachou