Programming Models
Cloud Computing
Garth Gibson
Greg Ganger
Majd Sakr
Raja Sambasivan
G. Gibson, Mar 3, 2014
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Recall the SaaS, PaaS, IaaS Taxonomy
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Service, Platform or Infrastructure as a Service
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SaaS: service is a complete application (client-server computing)
• Not usually a programming abstraction
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PaaS: high level (language) programming model for cloud computer
• Eg. Rapid prototyping languages
• Turing complete but resource management hidden
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IaaS: low level (language) computing model for cloud computer
• Eg. Assembler as a language
• Basic hardware model with all (virtual) resources exposed
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For PaaS and IaaS, cloud programming is needed
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How is this different from CS 101? Scale, fault tolerance, elasticity, ….
G. Gibson, Mar 3, 2014
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Embarrassingly parallel “Killer app:” Web servers
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Online retail stores (like the ice.com example)
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Most of the computational demand is for browsing product marketing,
forming and rendering web pages, managing customer session state
• Actual order taking and billing not as demanding, have separate specialized
services (Amazon bookseller backend)
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One customer session needs a small fraction of one server
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No interaction between customers (unless inventory near exhaustion)
Parallelism is more cores running identical copy of web server
Load balancing, maybe in name service, is parallel programming
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Elasticity needs template service, load monitoring, cluster allocation
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These need not require user programming, just configuration
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Eg., Obama for America Elastic Load Balancer
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What about larger apps?
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Parallel programming is hard – how can cloud frameworks help?
Collection-oriented languages (Sipelstein&Blelloch, Proc IEEE v79, n4, 1991)
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Also known as Data-parallel
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Specify a computation on an element; apply to each in collection
• Analogy to SIMD: single instruction on multiple data
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Specify an operation on the collection as a whole
• Union/intersection, permute/sort, filter/select/map
• Reduce-reorderable (A) /reduce-ordered (B)
– (A) Eg., ADD(1,7,2) = (1+7)+2 = (2+1)+7 = 10
– (B) Eg., CONCAT(“the “, “lazy “, “fox “) = “the lazy fox “
• Note the link to MapReduce …. its no accident
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High Performance Computing Approach
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HPC was almost the only home for parallel computing in the 90s
Physical simulation was the killer app – weather, vehicle design,
explosions/collisions, etc – replace “wet labs” with “dry labs”
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Physics is the same everywhere, so define a mesh on a set of particles, code
the physics you want to simulate at one mesh point as a property of the
influence of nearby mesh points, and iterate
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Bulk Synchronous Processing (BSP): run all updates of mesh points in parallel
based on value at last time point, form new set of values & repeat
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Defined “Weak Scaling” for bigger machines – rather than make a fixed
problem go faster (strong scaling), make bigger problem go same speed
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Most demanding users set problem size to match total available memory
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High Performance Computing Frameworks
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Machines cost O($10-100) million, so
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emphasis was on maximizing utilization of machines (congress checks)
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low-level speed and hardware specific optimizations (esp. network)
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preference for expert programmers following established best practices
Developed MPI (Message Passing Interface) framework (eg. MPICH)
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Launch N threads with library routines for everything you need:
• Naming, addressing, messaging, synchronization (barriers)
• Transforms, physics modules, math libraries, etc
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Resource allocators and schedulers space share jobs on physical cluster
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Fault tolerance by checkpoint/restart requiring programmer save/restore
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Proto-elasticity: kill N-node job & reschedule a past checkpoint on M nodes
Very manual, deep learning curve, few commercial runaway successes
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Broadening HPC: Grid Computing
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Grid Computing started with commodity servers (predates Cloud)
Frameworks were less specialized, easier to use (& less efficient)
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Beowulf, PVM (parallel virtual machine), Condor, Rocks, Sun Grid Engine
For funding reasons grid emphasized multi-institution sharing
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So authentication, authorization, single-signon, parallel-ftp
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Heterogeneous workflow (run job A on mach. B, then job C on mach. D)
Basic model: jobs selected from batch queue, take over cluster
Simplified “pile of work”: when a core comes free, take a task from
the run queue and run to completion
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Cloud Programming, back to the future
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HPC demanded too much expertise, too many details and tuning
Cloud frameworks all about making parallel programming easier
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Willing to sacrifice efficiency (too willing perhaps)
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Willing to specialize to application (rather than machine)
Canonical BigData user has data & processing needs that require
lots of computer, but doesn’t have CS or HPC training & experience
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Wants to learn least amount of computer science to get results this week
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Might later want to learn more if same jobs become a personal bottleneck
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Cloud Programming Case Studies
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MapReduce
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Package two Sipelstein91 operators filter/map and reduce as the base of a
data parallel programming model built around Java libraries
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DryadLinq
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Compile workflows of different data processing programs into
schedulable processes
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GraphLab
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Specialize to iterative machine learning with local update operations and
dynamic rescheduling of future updates
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MapReduce (Majd)
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DryadLinq
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Simplify efficient data parallel code
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Compiler support for imperative and
declarative (eg., database) operations
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Extends MapReduce to workflows
that can be collectively optimized
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Data flows on edges between processes at vertices (workflows)
Coding is processes at vertices and expressions representing workflow
Interesting part of the compiler operates on the expressions
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Inspired by traditional database query optimizations – rewrite the
execution plan with equivalent plan that is expected to execute faster
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DryadLinq
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Data flowing through a graph abstraction
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Vertices are programs (possibly different with each vertex)
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Edges are data channels (pipe-like)
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Requires programs to have no side-effects (no changes to shared state)
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Apply function similar to MapReduce reduce – open ended user code
Compiler operates on expressions, rewriting execution sequences
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Exploits prior work on compiler for workflows on sets (LINQ)
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Extends traditional database query planning with less type restrictive code
• Unlike traditional plans, virtualizes resources (so might spill to storage)
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Knows how to partition sets (hash, range and round robin) over nodes
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Doesn’t always know what processes do, so less powerful optimizer than
database – where it can’t infer what is happening, it takes hints from users
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Can auto-pipeline, remove redundant partitioning, reorder partitionings, etc
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Example: MapReduce (reduce-reorderable)
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DryadLinq
compiler can
pre-reduce,
partition,
sort-merge,
partially
aggregate
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In MapReduce
you “configure”
this youself
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Background on Regression
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Regression problem: For given input A, and observation Y, find
unknown x parameter
A: n by m matrix
…..
…..
…..
X
=
……..
….
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x: m by 1
Y: n by 1
Eg. Alzheimer Disease data
463 sample by 509K features.
In case of pair-wise study,
# of features would be inflated
to 1011.
Sparse regression is one variation of regression that
favors a small number of non-zero parameters
corresponding to the most relevant features.
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Eg. Medical Research
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Find genetic patterns predicting disease
Genetic variation associated with disease
Samples
(patients)
AT…….CG T AAA
AT…….CG G AAA
AT…….CG T AAA
AT…….CG T AAA
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Millions to 1011 (pair-wise gene study) dimensions
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HPC Style: Bulk Synchronous Parallel
Iterative Improvement of Estimated “Solution”
Thread 1
1
2
3
4
5
Thread 2
1
2
3
4
5
Thread 3
1
2
3
4
5
Thread 4
1
2
3
4
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Time
Threads synchronize (wait for each other) every iteration
Parameters read/updated at synchronization barriers
Repetitive file processing: Mahout, DryadLinq, Spark
Distributed shared memory: Pregel, Hama, Giraph, GraphLab
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Synchronization can be costly
Thread 1
1
Thread 2
1
Thread 3
1
Thread 4
1
2
3
2
3
2
2
3
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Time
Machines performance or work assigned unequal
So threads must wait for each other
And larger clusters suffer longer communication in barrier sync
If you can, do more work between syncs, but not always possible
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Can we just run asynchronous?
Parameters read/updated at any time
Thread 1
1
2
Thread 2
1
2
Thread 3
1
2
Thread 4
1
2
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5
3
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Time
Threads proceed to next iteration without waiting
Threads not on same iteration #
In most computations this leads to wrong answer
In search/solve, however, it might still converge, but it might also diverge
G. Gibson, Mar 3, 2014
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GraphLab: managing asynch ML
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GraphLab provides a higher level programming model
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o
o
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Data is associated with vertices and edges between vertices,
inherently sparse (or we’d use a matrix representation instead)
Program a vertex update based on its edges & neighbor vertices
Background code used to test if its time to terminate updating
BSP can be cumbersome & inefficient
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Iterative algorithms may do little work per iteration and may not
need to move all the data each iteration
Use traditional database transaction isolation for consistency
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Scheduling is green field in ML
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Graphlab proposes updates do own scheduling
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Baseline: sequential update of each vertex once per iteration
Sparseness allows non-overlapping updates to execute in parallel
Opportunity for smart schedulers to exploit more app properties
• Prioritize specific updates over other updates because these communicate
more information more quickly
G. Gibson, Mar 3, 2014
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One way to think about scheduling
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“Partial” synchronicity
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Spread network communication evenly (don’t sync unless needed)
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Threads usually shouldn’t wait – but mustn’t drift too far apart!
Straggler tolerance
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Slow threads must somehow catch up
Make thread 1 catch up
Force threads to sync up
Thread 1
1
Thread 2
1
2
Thread 3
1
2
Thread 4 1
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Time
Stale Synchronous Parallel (SSP)
Staleness Threshold 3
Thread 1 waits until
Thread 2 has reached iter 4
Thread 1
Thread 2
Thread 3
Thread 4
0
1
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3
4
5
6
7
8
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Iteration
Note: x-axis is now iteration count, not time!
Allow threads to usually run at own pace: mostly asynch
Fastest/slowest not allowed to drift >S iterations apart: bound error
Threads cache (stale) versions of parameters, to reduce network traffic
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Why does SSP converge?
Instead of xtrue, SSP sees xstale = xtrue + error
The error caused by staleness is bounded
Over many iterations, average error goes to zero
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SSP uses networks efficiently
Time Breakdown: Compute vs Network
LDA 32 machines (256 cores), 10% data per iter
8000
Seconds
7000
6000
Network waiting time
5000
Compute time
4000
3000
2000
1000
0
0
BSP
8
16
24
32
40
48
Staleness
SSP balances network and compute time
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Advanced Cloud Computing Programming Models
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Ref 1: MapReduce: simplified data processing on large clusters. Jeffrey Dean
and Sanjay Ghemawat. OSDI’04. 2004.
http://static.usenix.org/event/osdi04/tech/full_papers/dean/dean.pdf
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Ref 2: DyradLinQ: A system for general-purpose distributed data-parallel
computing using a high-level language. Yuan Yu, Michael Isard, Dennis
Fetterly, Mihai Budiu, Ulfar Erlingsson, Pradeep Kumar Gunda, Jon
Currey. OSDI’08. http://research.microsoft.com/enus/projects/dryadlinq/dryadlinq.pdf
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Ref 3: Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos
Guestrin, and Joseph M. Hellerstein (2010). "GraphLab: A New Parallel
Framework for Machine Learning." Conference on Uncertainty in Artificial
Intelligence (UAI).
http://www.select.cs.cmu.edu/publications/scripts/papers.cgi
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