Third Software Crisis - Computer Science and...

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Third Software CrisisThe Multicore Problem

Saman AmarasingheMassachusetts Institute of Technology

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The “Software Crisis”

“To put it quite bluntly: as long as there were no machines, programming was no problem at all; when we had a few weak computers, programming became a mild problem, and now we have gigantic computers, programming has become an equally gigantic problem."

-- E. Dijkstra, 1972 Turing Award Lecture

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The First Software Crisis

• Time Frame: ’60s and ’70s

• Problem: Assembly Language Programming– Computers could handle larger more complex programs

• Needed to get Abstraction and Portability without loosing Performance

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How Did We Solve the First Software Crisis?

• High-level languages for von-Neumann machines– FORTRAN and C

• Provided “common machine language” for uniprocessors

Single memory image

Single flow of control

Common PropertiesUniprocessors

ISA

Functional Units

Register File

Differences:

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The Second Software Crisis

• Time Frame: ’80s and ’90s

• Problem: Inability to build and maintain complex and robust applications requiring multi-million lines of code developed by hundreds of programmers– Computers could handle larger more complex programs

• Needed to get Composability, Malleability and Maintainability– High-performance was not an issue left for Moore’s Law

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How Did We Solve the Second Software Crisis?

• Object Oriented Programming– C++, C# and Java

• Also…– Better tools

– Component libraries, Purify – Better software engineering methodology

– Design patterns, specification, testing, code reviews

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The OO Revolution

• Object Oriented revolution did not come out of a vacuum

• Hundreds of small experimental languages

• Rely on lessons learned from lesser-known languages– C++ grew out of C, Simula, and other languages– Java grew out of C++, Eiffel, SmallTalk, Objective C, and Cedar/Mesa1

• Depend on results from research community

J. Gosling, H. McGilton, The Java Language Enviornment1

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Object Oriented Languages

• Ada 95• BETA• Boo• C++• C#• ColdFusion• Common Lisp• COOL (Object

Oriented COBOL)• CorbaScript• Clarion• Corn• D• Dylan• Eiffel• F-Script• Fortran 2003• Gambas• Graphtalk• IDLscript• incr Tcl• J• JADE

• Java• Lasso• Lava• Lexico• Lingo• Modula-2• Modula-3• Moto• Nemerle• Nuva• NetRexx• Nuva• Oberon (Oberon-1)• Object REXX• Objective-C• Objective Caml• Object Pascal (Delphi)• Oz• Perl 5• PHP• Pliant• PRM• PowerBuilder

• ABCL• Python• REALbasic• Revolution• Ruby• Scala• Simula• Smalltalk• Self• Squeak• Squirrel• STOOP (Tcl

extension)• Superx++• TADS• Ubercode• Visual Basic• Visual FoxPro• Visual Prolog• Tcl• ZZT-oop

Source: Wikipedia

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Language EvolutionFrom FORTRAN to a few present day languages

Source: Eric Levenez

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Origins of C++

Source: B. Stroustrup, The Design and Evolution of C++

1960

1970

1980

1990

Structural influenceFeature influence

FortranAlgol 60

CPL

BCPL

C

ANSI C

Simula 67

C with Classes

C++

C++arm

C++std

ML CluAlgol 68

Ada

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Academic Influence on C++

“Exceptions were considered in the original design of C++, butwere postponed because there wasn't time to do a thorough job ofexploring the design and implementation issues.

In retrospect, the greatest influence on the C++ exceptionhandling design was the work on fault-tolerant systems started at the University of Newcastle in England by Brian Randell and his colleagues and continued in many places since.”

-- B. Stroustrup, A History of C++

…

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Origins of Java• Java grew out of C++, Eiffel, SmallTalk, Objective C, and Cedar/Mesa• Example lessons learned:

– Stumbling blocks of C++ removed (multiple inheritance, preprocessing, operator overloading, automatic coercion, etc.)

– Pointers removed based on studies of bug injection– GOTO removed based on studies of usage patterns– Objects based on Eiffel, SmallTalk– Java interfaces based on Objective C protocols– Synchronization follows monitor and condition variable paradigm (introduced by Hoare,

implemented in Cedar/Mesa)– Bytecode approach validated as early as UCSD P-System (‘70s)

Lesser-known precursors essential to Java’s success

Source: J. Gosling, H. McGilton, The Java Language Enviornment

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Role of Research in Language Design

1. Invent new features and paradigms2. Simplify or eliminate bad features3. Experimental evaluation

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1. Invent New Features

Modula-2Modules

Simula 67Monads

CobolMacros

KRC (influencing Haskell)List comprehension

ISWIM (influencing Haskell)Lazy evaluation

Simula 67Inheritance

Scheme R4RSHygienic macros

LispHeap allocation

LispGarbage collection

Algol 58Explicit typing

PL/IException handling

Algol 58Compound statements

CobolComments

LispClosure

Simula 67Class

BCPLChained comparisons

Algol WCase statement

Algol 60Block nesting with scope

Algol 60 syntaxBNF (Backus-Naur Form)

C++Assignment operator overloading

Simula 67Abstract data types

First DemonstrationProgramming Concept

Algol 68Using C as portable assembler

C++ (Cfront)Variable decl anywhere in block

MLUser-defined data types

HaskellType inference

CobolType classes

FortranStructures (records)

Algol 60Static allocation

Algol 58Stack dynamic variables

Fortran IIStack allocation

Algol 68Separate compilation

PL/IReferences

Hope (influencing ML)Pointer datatype

Algol WPattern matching

FortranPass by value/result

Algol 60Pass by value

MLPass by name

Algol 68Parametric Polymorphism

Algol 68Orthogonality

Simula 67Operator overloading

HaskellObject-oriented Programming

First DemonstrationProgramming Concept

Source: Pascal Rigaux, Mandriva

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2. Remove Bad Features

• E. Dijkstra, Go To Statement Considered Harmful

• B.W. Kernighan, Why Pascal is Not my Favorite Programming Language

• T.A. Cargill, The Case Against Multiple Inheritance in C++

• E. A. Lee, The Problem With Threads

• H.J. Boehm, Threads Cannot be Implemented as a Library

• O. Shivers, Higher-order Control-flow Analysis in Retrospect:Lessons Learned, Lessons Abandoned

• Lowe & Ericsson, Component Adaptations and Optimisations Considered Harmful

• F. Steimann, Why Most Domain Models are Aspect Free

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3. Experimental Evaluation

• To advance the state-of-the-art programming languages, need to evaluate practical impact of ideas

“Multiple inheritance is a great theory that in practice creates as many problems as it solves.”

-- Labview Object-Oriented Programming: The Decisions Behind the Design

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The Third Software Crisis

• Time Frame: 2010 to ??

• Problem: Sequential performance is left behind by Moore’s law

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1

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Moore’s Law

From David Patterson

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10,000

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From Hennessy and Patterson, Computer Architecture: A Quantitative Approach, 4th edition, 2006

Num

ber of Transistors

19

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386

486

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P3P4

ItaniumItanium 2

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10000

100000

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Uniprocessor Performance (SPECint)


1,000,000,000

100,000

10,000

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10,000,000

100,000,000


Num

ber of Transistors

20

8086

286

386

486

PentiumP2

P3P4

ItaniumItanium 2

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10

100

1000

10000

100000

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Uniprocessor Performance (SPECint)

• General-purpose unicores have stopped historic performance scaling– Power consumption– Wire delays– DRAM access latency– Diminishing returns of more instruction-level parallelism


1,000,000,000

100,000

10,000

1,000,000

10,000,000

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Num

ber of Transistors

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• Needed continuous and reasonable performance improvements – to support new features– to support larger datasets

• While sustaining portability, malleability and maintainability without unduly increasing complexity faced by the programmer

• à critical to keep-up with the current rate of evolution in software

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à critical to keep-up with the current rate of evolution in software

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critical to keep-up with the current rate of evolution in software

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How to programmulticores?

1985 199019801970 1975 1995 2000 2005

Raw

Power4Opteron

Power6

Niagara

YonahPExtreme

Tanglewood

Cell

IntelTflops

Xbox360

CaviumOcteon

RazaXLR

PA-8800

CiscoCSR-1

PicochipPC102

Boardcom 1480

20??

# ofcores

1

2

4

8

16

32

64

128256

512

Opteron 4PXeon MP

AmbricAM2045


4004

8008

80868080 286 386 486 Pentium P2 P3P4Itanium

Itanium 2Athlon

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How Are We Going to Solve the Third Software Crisis?

1. Build and mobilize the community

2. Seed the proliferation of research into new – Programming Models– Languages – Compilers– Tools

3. Facilitate Testing and Feedback for Rapid Evolution

4. Create a framework to identify and collect winning ideas for standardization and adoption

5. Support the migration of the dusty deck to the new paradigm

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1. Build and Mobilize the Community

• Bring the High Performance Languages/Compilers/Tools folks out of the woodwork!– Then: A few customers with the goal of performance at any cost.– Then: Had to compete with Moore’s law– Now: Reasonable performance improvements for the masses

• Bring the folks who won the second crisis– Then: the focus is improving programmer productivity– Now: how to maintain performance in a multicore world– Now: If not solved, all the productivity gains will be lost!

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1. Build and Mobilize the Community

• Bring the High Performance Languages/Compilers/Tools folks out of the woodwork!– Then: A few customers with the goal of performance at any cost.– Then: Had to compete with Moore’s law– Now: Reasonable performance improvements for the masses

• Bring the folks who won the second crisis– Then: the focus is improving programmer productivity– Now: how to maintain performance in a multicore world– Now: If not solved, all the productivity gains will be lost!

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2. Proliferation of Research

• Need to generate a lot of new ideas– We haven’t done much in the last decade!

• Need to look into the areas of – Programming Models– Languages – Compilers– Tools

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Experience from High Performance Languages

• In the early 90’s– HPF, FORTRAN-D etc.– Low productivity languages– Ended up low performance languages!– Architects (and compiler writers) are not good in designing languages

• The next decade – StreamIt, Brook, Cilk etc.– Very little work, not a priority

• Now– HPC effort: Fortress, X10 etc.

– Trying to achieve a lot without many positive or recent examples to follow

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Why New Languages?• Paradigm shift in architecture

– From sequential to multicore– Need a new “common machine language”

• New application domains– Streaming– Scripting– Event-driven (real-time)

• New hardware features– Transactions – Introspection– Scalar Operand Networks or Core-to-core DMA

• New customers– Mobile devices– The average programmer!

• Can we achieve parallelism without burdening the programmer?

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How to design a new language• Language design is an art form

• Good languages need to support– Powerful– Useable by Novice to Expert– Supports Abstraction– Supports Modularization– Supports Portability– Supports Malleability– Increases Programmer Productivity

• Need to understand and have a lot of experience in all the concepts – One bad feature can kill a language

• Need many experimental languages before building one with mass acceptance – A language with mass acceptance so far is once a decade event

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Domain Specific Languages

• There is no single programming domain!– Many programs don’t fit the OO model (example streaming)

• Need to identify new programming models/domains– Develop domain specific end-to-end systems– Develop languages, tools, applications a body of knowledge

• Stitching multiple domains together is a hard problem– A central concept in one domain may not exist in another

– Shared memory is critical for transactions, but not available in streaming – Need conceptually simple and formally rigorous interfaces – Need integrated tools– But critical for many DOD and other applications

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programmability

domain specificoptimizations

enable parallelexecution

simple and effective optimizations for domain specific abstractions

boost productivity, enable faster development and rapid prototyping

Compiler-Aware Language Design: StreamIt Experience

• Some programming models are inherently concurrent– Coding them using a sequential language is…

– Harder than using the right parallel abstraction– All information on inherent parallelism is lost

• There are win-win situations– Increasing the programmer productivity while extracting parallel performance

target tiled architectures, clusters, DSPs, multicores, graphics processors, …

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Parallelizing Compilers:SUIF Experience

• Automatic Parallelism is not impossible– Can work well in many domains (example: ILP)

• Automatic Parallelism for multiprocessors “almost” worked in the ‘90s– SUIF compiler got the Best SPEC results by automatic

parallelization

• But…– The compilers were not robust– Clients were impossible (performance at any cost)– Multiprocessor communication was expensive – Had to compete with improvements in sequential performance– The Dogfooding problem

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Compilers

• Compilers are critical in reducing the burden on programmers– Identification of data parallel loops can be easily automated, but

many current systems (Brook, PeakStream) require the programmer to do it.

• Need to revive the push for automatic parallelization– Best case: totally automated parallelization hidden from the user– Worst case: simplify the task of the programmer

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Tools

• A lot of progress in tools to improve programmer productivity

• Need tools to– Identify parallelism– Debug parallel code– Update and maintain parallel code– Stitch multiple domains together

• Need an “Eclipse platform for multicores”

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3. Facilitate Evaluation and Feedback for Rapid Evolution

Language/Compiler/ToolsIdea

Implementation

Evaluation

Evaluation

Develop aProgram

FunctionalDebugging

PerformanceDebuggingEvaluate

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The Dogfooding ProblemCAD Tools vs. OO Languages

• CAD Tools– Universally hated by the users– Only a few can hack it– Very painful to use

• Object Oriented Languages– User friendly – Universal acceptance – Use by ordinary programmers– Huge improvements in

programmer productivity

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• Origins– Developed by CAD experts– User community is separate



• Origins– Developed by PL experts– The compiler is always written

using the language/tools– Rapid feedback

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• Origins– Developed by CAD experts– User community is separate



• Origins– Developed by PL experts– The compiler is always written

using the language/tools– Rapid feedback

• High Performance Languages– User community is separate– Hard to get feedback– Slow evolution

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Rapid Evaluation

• Extremely hard to get– Real users have no interest in flaky tools– Hard to quantify – Superficial users vs. Deep users will give different feedback

– Fatal flaws as well as amazing uses may not come out immediately

• Need a huge, sophisticated (and expensive) infrastructure – How to get a lot of application experts to use the system? – How do you get them to become an expert?– How do you get them to use it for a long time?– How do you scientifically evaluate?– How go you get actionable feedback?

• A “Center for Evaluating Multicore Programming Environments”??

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4. Identify, Collect, Standardize, Adopt

• Good languages/tools cannot be designed by committee

• However, you need a vibrant ecosystem of ideas

• Need a process of natural selection – Quantify Productivity and Performance – Competition between multiple teams– Winner(s) get to design the final language

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5. Migrate the Dusty Deck

• Help rewrite the huge stack of dusty deck– Application in use– Source code available– Programmer long gone

• Getting the new program to have the same behavior is hard– “Word pagination problem”

• Can take advantage of many recent advances– Creating test cases– Extracting invariants– Failure oblivious computing

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Conclusions• Programming language research is a critical long-term investment

– In the 1950s, the early background for the Simula language was funded by the Norwegian Defense Research Establishment

– In 2002, the designers received the ACM Turing Award “for ideas fundamental to the emergence of object oriented programming.”

• We need to lay the foundation for the programming paradigm of the 21st century

• Switching to multicores without losing the gains in programmer productivity may be the Grandest of the Grand Challenges

– Half a century of work still no winning solution– Will affect everyone!

• Need a government industry partnership to solve this– Intel is worried multicores are needed to deliver Moore’s law gains to customers– Microsoft is concerned need to program the multicores – DOD will be affected from a problem at the labs to one that every programmer

needs to tackle (from the smallest embedded device to supercomputers)

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