Model Integrated Computing: A Framework for Creating Domain Specific Design Environments

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1 Model Integrated Computng: A Framework for Creatng Doman Specfc Desgn Envronments James R. DAVIS Vanderblt Unversty, Insttute for Software Integrated Systems Nashvlle, TN 37203, USA ABSTRACT Model Integrated Computng (MIC) s a technology developed to ad n the rapd desgn and mplementaton of complex computer based systems. These systems typcally are characterzed by the ntegraton of ther nformaton processng systems and the physcal envronment of the actual system. MIC employs multple aspect, doman-specfc modelng technology to represent the system software, the system hardware, ts envronment, and ther relatonshps. Model nterpreters are used to transform the nformaton captured n the models nto the artfacts requred by the chosen analyss tools or run tme system. One of the largest advantages to usng MIC s the ablty to reason and desgn a complex system at a hgher level of abstracton. Ths paper wll descrbe one framework for applyng MIC to system tool desgn. A selected project where the framework s beng appled wll be ntroduced. The advantages to usng MIC for ths project wll be dscussed. Keywords: modelng, model translaton, modelng language specfcaton, smulaton ntegraton 1. Introducton Complex computer-based systems are often characterzed by the tght ntegraton of nformaton processng and the physcal envronment of the actual system. In addton, these systems are often msson crtcal systems; ther falure s unacceptable. Model- Integrated Computng (MIC) s a technology that s well suted for the rapd desgn, mplementaton, and evoluton of such systems [1]. MIC employs doman-specfc models to represent system software, ts envronment, and ther relatonshps. Wth Model-Integrated Program Synthess (MIPS), these models are then used to automatcally synthesze the embedded software and hardware applcatons and generate nputs to COTS analyss tools. The MIPS technque s possble only due to the capturng of the relatonshps between the software and the system s envronment. Ths approach speeds up the desgn cycle, facltates the evoluton of the applcaton, and helps system mantenance, dramatcally reducng costs durng the entre lfecycle of the system. 2. Model Integrated Computng The Multgraph Archtecture (MGA), developed at the Insttute for Software Integrated Systems at Vanderblt Unversty, s a toolkt for creatng multple aspect, doman-specfc MIPS envronments. The MGA s shown n Fgure 1. The metaprogrammng nterface s used to formally specfy the applcaton doman s modelng paradgm. The modelng paradgm captures all the syntactc, semantc, and presentaton nformaton regardng the doman whch concepts wll be used to construct models, what relatonshps may exst among those concepts, how the concepts may be organzed and vewed by the modeler, and rules governng the constructon of models. The modelng paradgm defnes the famly of models that can be created usng the resultant modelng envronment. All modelng paradgms addtonally adhere to a set of specfcatons regardng the presentaton features allowed by the MGA confgurable model edtor. Wth MIC, modelng paradgms are represented by metamodels. The metamodels are used to automatcally confgure the MIPS modelng envronment for the doman. Ths MIPS envronment conssts of a doman specfc model edtor, a customzed model database, and a set of model translators or nterpreters. An nterestng aspect of ths approach s that a MIPS envronment tself s used to buld the metamodels [2]. The generated doman-specfc MIPS envronment s then used by the system user to buld doman models that are stored n a model database. These models are used to automatcally generate the applcatons or to synthesze nput to dfferent COTS analyss tools. Ths process s called model nterpretaton. Model nterpreters are those enttes that automatcally translate the models nto other useful artfacts whle ensurng the semantcs between the modelng doman and external tools are kept consstent. The Generc Modelng Envronment The Generc Modelng Envronment (GME 2000), s a Wndows-based, doman-specfc, model-ntegrated program synthess tool for creatng and evolvng doman-

2 specfc, mult-aspect models of computer based engneerng systems. The GME 2000 s part of the Multgraph Archtecture (MGA) tool sute. In partcular, GME 2000 provdes the doman specfc model edtor that s used n the MGA systems [3]. The GME 2000 s confgurable, or metaprogrammable, whch means t can be programmed to work wth vastly dfferent domans. Another mportant feature s that GME 2000 s confgured from formal modelng envronment specfcatons or meta-models. Ths ensures that t can be quckly and safely evolved as modelng requrements change [4]. GME 2000 s used prmarly for model-buldng. The models take the form of graphcal, mult-aspect, attrbuted entty-relatonshp dagrams. The statc semantcs of a model are specfed by explct constrants that are enforced by a bult-n constrant manager. The dynamc semantcs s not the concern of GME 2000 that s determned later durng the model nterpretaton process. Metaprogrammng Interface Formal Specfcatons Meta-Level Translaton Envronment Evoluton DSME Envronment Model Bulder Models Model Interpreters Applcaton Evoluton App. 1 Applcaton Doman App. 2 Model Interpretaton Fgure 1 : The Multgraph Archtecture Modelng Concepts: The GME 2000 supports varous technques for buldng and managng the complexty of large-scale, complex models. The technques nclude: herarchy, multple aspects, sets, references, and explct constrants. The GME 2000 users manual [4] detals the dfferent relatonshps between the major modelng components. A bref overvew of the general concepts wll be gven here. Models are the centerpeces of a MIC envronment. They are compound objects that can have parts and nner structure. Models can contan other models, atoms (parts that cannot be further decomposed), sets, references, and connectons. Notce that snce models can contan other models, herarchcal systems can be constructed. Textual attrbutes can be attached to most GME objects. Ths allows for capturng nformaton that cannot be effcently modeled graphcally. Assocatons between objects are captured usng Connectons, References, and Sets. Connectons and References model relatonshps between at most two objects. References are used to assocate objects n App. 3 another part of the model herarchy. Sets can be used to specfy a relatonshp among a group of objects. The only restrcton s that all the members of a set must have the same parent and be vsble n the same Aspect. Another key feature of GME 2000 s the ablty to partton the models vsually usng Aspects. Usng multple aspects grants the ablty to hde part of the modeled nformaton from certan classes of users. Every Model has a predefned set of Aspects. Each component can be vsble or hdden n an Aspect. Every component has a set of prmary aspects where t can be created or deleted. There are no restrctons on the set of Aspects a Model, and t s parts, can have; a mappng can be defned to specfy what Aspects of a part s show n what Aspect of the parent Model. A specfc class of user may only want to see objects n the model that pertan to hardware. By carefully craftng the modelng language, the tool desgner can allow ths behavor. When a partcular type of model s created n a GME 2000 doman, t becomes a type (class). It can be sub typed and nstantated as many tmes as the user wshes. Please see [4] for more nformaton about sub-typng wth GME One, often confusng, ssue s that the concept of the Model s one level hgher n the meta herarchy than that of the class n an OO language. A partcular knd of Model n a modelng paradgm s equvalent to the concept of the class. In the resultng envronment, the end user of the MIC system can create specfc nstances of the Model, whch s smlar to nstantatng a class n an OO language. It s mportant to note that when usng GME 2000, the user deals wth components n ther doman. They do not need to understand models, atoms, references, etc. Instead, they need to understand how to use the features of ther paradgm to construct models for ther doman. In one of our projects [5], the users constructed models of a dscrete manufacturng plant as a process model. The users dealt wth processes, buffers, and conveyers; they dd not deal wth abstract models and atoms. A large part of the power of usng MIC comes from the customzaton of the tools to a partcular problem doman. Interfacng to GME 2000 GME 2000 has a modular, Mcrosoft COM-based archtecture depcted n Fgure 2. Detals of the dfferent components are outsde the scope of ths paper. Two mportant components that wll be dscussed here are the Add-On and Interpreter. The MGA and Meta components expose a set of COM nterfaces that can be used to wrte model nterpreters and add-ons. The GME 2000 user nterface has ts own COM nterface that supports program-drven vsualzaton of models. Notce that all GME 2000 components nterface through the use of the MGA and Meta component COM nterfaces. Through these nterfaces, the user can wrte nterpreters and add-ons that access the model nformaton and provde some type of translaton.

3 3. Doman Specfc Language Specfcaton GME 2000 GUI Browser MGA RepStorage MSR Constrant Manager Core Meta FleStorage Fle Interpreter Fgure 2: GME 2000 Archtecture Add-On In addton to these COM nterfaces, GME 2000 provdes an nterface for non-com programmers. A hgh-level component nterface sts on the top of the MGA and Meta COM nterfaces and provdes a C++ API for nterpreter wrtng. It mplements a set of C++ classes that are nstantated mmedately upon nterpreter nvocaton. A network of objects (called the Bulder Object Network) s bult that mrrors the structure of the whole project before the nterpreter gets control. It s mportant to note that the whole project s mrrored for potentally very large projects, the natve COM nterpreter nterface s preferred. The hgh level nterface unburdens the user from makng relatvely low-level COM calls. The user can use these servces through the publc nterfaces of the C++ objects. Ths nterface can be extended usng C++ nhertance. The user can derve from the bult-n classes and the nterface wll automatcally nstantate the userdefned classes nstead of the bult-n ones usng the object factory desgn pattern. In a graduate-level course on MIC, the extenson of the BON s stressed as almost essental for complex projects. Interpreters are the model translators dscussed earler n ths paper. They are executed on demand, take the models as nput, and delver some type of output based on the models. One can thnk of the model nterpreters as applyng the semantcs to the models. Add-ons can be consdered event-drven model nterpreters. A set of events, such as Object Deleted, Set Member Added, and Attrbute Changed are exposed by lower level GME components. External components can regster for a set of these events. They are automatcally nvoked by the GME 2000 components whenever the events occur. Add-ons are generally used for extendng the capabltes of the GME User Interface. When a partcular doman calls for some specal operatons, they can be supported wthout requrng the modfcaton of GME Ths archtecture s very flexble and supports extensblty of the entre envronment. The GME 2000 Users Manual provdes detaled documentaton on the hgh-level component nterface [4]. Defnng a doman specfc modelng paradgm s tself a problem doman. Metamodelng s a term used to descrbe the process of modelng the doman specfc modelng language. Semantcs, syntax, and presentaton are all captured n the metamodel. It s qute natural that GME 2000 s used to construct these modelng language models, or metamodels. There s a metamodelng paradgm defned that confgures GME 2000 for creatng metamodels. These models are then automatcally translated nto GME 2000 confguraton nformaton through the model nterpretaton process. Orgnally, the metamodelng paradgm was handcrafted. Once the metamodelng nterpreter was operatonal, meta-metamodels were created and the metamodelng paradgm was generated automatcally. Ths s smlar to wrtng a C compler n C. Note that meta-metamodels s the pont where the meta herarchy ends. Snce we use the metamodelng envronment tself to create the meta-metamodels, there s no need for an addtonal level; there are no meta-metametamodels [2, 6]. The metamodelng paradgm s an extenson of the Unfed Modelng Language (UML). In fact, the syntactc defntons are defned usng pure UML class dagrams and the statc semantcs are specfed wth constrant usng the Object Constrant Language (OCL). The specfcaton of presentaton/vsualzaton nformaton necesstated extensons to UML, manly n the form of predefned object attrbutes for such thngs as con fle names, colors, lne types etc. These could be specfed usng UML attrbutes. However, a desgn decson was made that, snce the vsualzaton nformaton only pertans to GME 2000 and usng GME 2000 features would make the envronment more user-frendly, extensons to UML were justfed. It s mportant to examne the use of constrants n defnng a modelng language. Some semantc rules cannot be vsually specfed usng UML or the extended UML. These rules requre the use of textual (OCL) constrants. However, the constrants can be parsed and evaluated durng the constructon of models. GME 2000 ensures that the constrants are met by verfyng that the model does not volate any constrants defned for the paradgm. The tool desgner can even specfy when to check certan constrants and whether or not a constrant can be overrdden. Some models may need to temporarly volate a constrant. For example, f the constrant says that every Process must be connected to at least one Conveyer, and every Conveyer must be connected to at least two Processes, how do you begn constructon of a new model? You must allow the user to temporarly volate the constrant so they can complete the model. However, all constrants should be verfed before model nterpreter occurs. Another feature that metamodelng allows s the evoluton of the system over tme. In Fgure 1, two types

4 of evoluton are shown: applcaton evoluton and envronment evoluton. For applcaton evoluton, the MIC envronment must support the ablty to add new or modfy exstng modelng nterpreters to compensate for changng applcaton requrements. For example, f the run tme system changes from a Unx system to a Wndows platform, some changes to the generated system may be requred. In ths case, changes to the modelng language are not needed. For envronment evoluton, the system needs the ablty to modfy the modelng envronment as the system requrements change over tme. Ths could be due to a new analyss tool that requres nformaton that cannot be captured n the current modelng language or to mprove the expressvty to the language. Wth MIC, the metamodel can be modfed to mprove the doman specfc language and then a new confguraton for GME 2000 can be generated. One mportant aspect of envronment evoluton s the problem of model mgraton. Models need to be mgrated to the mproved paradgm to elmnate the need to reconstruct them manually. For some cases, the GME 2000 tools support model mgraton. In the general case, research s ongong as to how to perform ths translaton process. For more nformaton a detaled descrpton of the metamodelng envronment can be found n [4]. 4. Verfcaton of Doman Specfc Models An added beneft to usng MIC s the ablty to perform some verfcaton and valdaton at the model level. At ths hgher level of abstracton, the user can concentrate on the models and ther ntended meanng nstead of tryng to decpher source code to determne whether some problem was an mplementaton or desgn flaw. Addtonally, the user can perform dagnostcs as to why a verfcaton routne faled rather than employng mplementers to check the valdty of ther source code. As usual, ths process s done through the use of a model nterpreter. Model nterpreters can be provded that perform verfcaton or that provde detaled nformaton from the models to outsde verfcaton tools. Snce the models should capture all of the nformaton necessary to analyze and synthesze the system, they wll also capture all of the nformaton necessary to perform verfcaton of the system models. In many cases, t s cheaper, easer, and qucker to perform the verfcaton at the model level nstead of at the system code level. By usng the MIC nterpreter nterfaces, the system developers are able to attach model verfcaton routnes to the modelng tools nstead of tryng to verfy the artfacts of the system generaton process. One example of model verfcaton usng MIC s a project where the modelng language allowed for the behavoral modelng of hgh assurance systems [7]. These models were converted nto Ordered Bnary Decson Dagrams (OBDDs) [8] and then symbolcally evaluated. Ths symbolc search through the models allowed extremely large modeled behavoral spaces to be examned. The result of the analyss was a set of relablty and safety data derved from the models. The system users dealt wth behavor models and relablty and safety data, whch was a natural form for the users. The system users were shelded from the detals of the verfcaton and model checkng routnes. A smlar technque was used n to verfy that selected models would meet run tme performance constrants [9, 10] by checkng the constrants aganst the models. By assurng that only vald models would be evaluated, the user dd not have to deal wth nterpretng and evaluatng models that dd not meet the constrants. 5. An Example MIC Applcaton Now, lets take a quck look at an example applcaton to show the utlty of MIC n practce. MILAN s a model-based, extensble smulaton framework that facltates rapd evaluaton of dfferent performance metrcs, such as power, latency, and throughput, at multple levels of granularty for a large set of embedded systems by seamlessly ntegratng dfferent, wdely-used smulators nto a unfed envronment. The MILAN framework s amed at the desgn of embedded hgh-performance computng platforms, of System-on- Chp (SoC) archtectures for embedded systems, and for the hardware/software co-desgn of heterogeneous systems. MILAN s a mult-year effort; only prelmnary results and future plans are dscussed n ths paper. MILAN s constructed usng the MIC technology and GME 2000 [10]. Fgure 3 shows the archtecture of the MILAN framework. At the top s the Generc Modelng Envronment confgured to support the modelng language developed specfcally for MILAN. There are three knds of models n MILAN: resource models, applcaton models and explct constrants. The applcaton models are based on a herarchcal sgnal flow representaton wth mportant extensons. Most notably, the modelng language allows for the specfcaton of explct desgn or mplementaton alternatves of any component. Among the other features are the ablty to model synchronous and asynchronous dataflows and the ablty to mx synchronous and asynchronous dataflows. At the lowest levels n the model herarchy, the user must specfy the functon to be executed. The model nterpreters, denoted by the crcles contanng Is, can take care of generatng the glue code necessary to execute the system as well as any schedulng that needs to occur. The modelng of alternatves allows the entre desgn space of the applcaton to be captured as opposed to a pont soluton. To manage ths desgn space, applcaton requrements, resource constrants and other specfcatons are captured as explct constrants n the models. The resource models capture the avalable hardware components and ther nterconnectvty.

5 The Desgn-Space Exploraton and Prunng tool takes the potentally very large desgn space and apples the constrants usng a symbolc constrant satsfacton technque to fnd the set of solutons that satsfy all the constrants. The modelng methodology and the desgnspace exploraton technque are descrbed n detal n subsequent sectons. The goal of desgn space exploraton s to dentfy a small number of vald canddate desgns. To fnd the balance between an underconstraned and an over-constraned model s a hghly teratve, human-n-the-loop process. One of the desgn goals of the modelng envronment and the desgn-space exploraton tools s to support the automaton of ths actvty. The next step n the desgn process s to utlze the ntegrated smulators to smulate the canddate desgns one-by-one. Each supported smulator has a correspondng model nterpreter that confgures the smulator from the system models. MILAN supports several dfferent classes of smulators. Functonal smulators, such as Matlab or SystemC, verfy the functonalty of the applcaton wthout regards to performance or power. The ntegrated hgh-level smulator provdes a rapd, reasonably accurate estmate of dfferent performance crtera of the system. Lower-level power and performance smulators, such as SmpleScalar or SmplePower, are also supported. Whle they can be very accurate, ther slow speed may prevent the smulaton of the whole system. One of the major challenges of an ntegrated smulaton framework lke MILAN s how to nterpret the results of dssmlar smulators. In our archtecture, model nterpreters specfc to each ndvdual smulator take results and feed them back to the models. Results from a SmpleScalar smulaton of a component can be stored n the models n the form of performance attrbutes that the hgh-level smulator can utlze n evaluatng the performance of the whole system. Ths allows the dfferent levels of smulaton to nteract through the models. Note that the nterpretaton of the results can be a human-n-the-loop process. For example, we do not plan any automatc model modfcaton based on a functonal smulaton n Matlab. Once a canddate desgn has been selected, through the process of smulaton and desgn space exploraton, the target applcaton can be automatcally syntheszed. Ths step s farly smlar to drvng the smulators. Instead of the semantcs of the target smulator, the semantcs of the runtme system have to be observed by the nterpreter. The MILAN Modelng Language A prelmnary representaton method has been selected that parttons the system nto three dstnct classes of models: applcaton models, resource models and constrants. Applcaton models descrbe the task to be performed whle the resource models descrbe the physcal hardware avalable. Constrants specfy requrements. A mappng between components of the applcaton and avalable resource models s used to capture the space of possble desgn choces lmted by the desgn constrants. Functonal Smulator Low - Level Power Smulator Applcaton Models Generc Modelng Envronment Constrants Desgn -Space Exploraton and Prunng DESIGN System Synthess SYSTEM Archtecture Models Hgh - Level Smulator Low - Level Performance Smulator Fgure 3: The MILAN Archtecture Applcaton models are currently constructed usng asynchronous and synchronous dataflow dagrams. These models are strongly typed. Mxed models are supported those that have synchronous and asynchronous components. The models can nclude explct mplementaton alternatves, whch represent the applcaton desgn space. At the leaf levels n the herarchy, the user must provde the mplementatons of the data flow blocks. Desgn constrants capture system requrements such as tmng, performance, power, cost, etc. Moreover, resource constrants and other nformaton also need to be specfed as part of the models. In MILAN, the Object Constrant Language (OCL) s used to specfy constrants n a formal manner. Desgn Space Exploraton The approach we have chosen for rapd exploraton of large desgn spaces reles on a symbolc representaton based on Ordered Bnary Decson Dagrams (OBDD). In ths symbolc representaton, a set (or space) s represented mathematcally by ts characterstc functon. Constrants express complex relatonshps and bounds over composte propertes of elements. Constrant applcaton s a logcal conjuncton of the functons derved from the models and the constrants. The resultant Boolean functon represents the pruned desgn space. The prncpal advantage of ths approach s that constrant satsfacton s accomplshed wthout the enumeraton of the entre space. Elmnatng the need for enumeraton makes the approach hghly scalable, and partcularly attractve to representng extremely large desgn spaces.

6 Currently Integrated Smulators Several smulaton engnes have already been mplemented n MILAN. Matlab can be used for functonal verfcaton of the applcaton models. SmpleScalar can also be confgured from the applcaton models. Hardware models are used to generate SystemC smulatons. A system level estmator has been ntegrated. Mnor modfcatons allowed the SmpleScalar nterpreter to support the use of PowerAnalyzer, a power aware cycle accurate smulator. Future smulaton engnes to be ntegrated nclude a VHDL smulator and other power-aware processor smulators. Work s underway to solve the problem of automatcally updatng model nformaton based on smulaton results. The system level smulator should utlze results ganed from component smulators. Ths vertcal smulaton ntegraton allows for ncreased clarty n the system level smulaton results. Feedback nterpreter generaton s part of our ongong work n ths area. All of these nterpreters make use of the hgh level nterpreter nterface provded by GME One of the prmary advantages of usng MIC s that several smulators can be confgured from the same set of models. In effect, a sngle system specfcaton s reused n provdng the dfferng smulators ther nputs. Ths s possble due to nvokng multple nterpreters on a sngle model. 6. Conclusons MIC s a proven technology for developng and evolvng complex computer based systems. Usng MIC allows for the creaton of graphcal models that defne the syntax, semantcs, and presentaton of a doman specfc language. These language specfcatons can be automatcally verfed for legalty and then used to confgure a new doman specfc tool envronment. GME 2000 s a cornerstone of these doman specfc desgn tools. A set of nterfaces allows for access to the modeled nformaton. Ths nformaton can then be used to verfy and valdated the models as well as for generaton and confguraton of the run tme system. One of the advantages of usng MIC s the ablty for the end user to desgn n a natural doman nstead of drectly wrtng source code. Ths allows the users to work wth ther desgn envronments at a hgher level of abstracton whle ensurng actual run tme systems can be created from the hgher level models. The MILAN framework s an excellent example of a MIC system. Whle t s currently beng developed, enough data s avalable to show the utlty of the project. It has been used to demonstrate the desgn and smulaton of several real-world problems. MILAN s only ntended to show the utlty of the MIC technology. Model Integrated Computng (MIC) wll allow the system to evolve wth the ever-changng smulaton requrements of embedded computng applcatons. 7. Acknowledgements I would lke to thank DARPA for ther support for a porton of the work presented n ths paper. MILAN s funded by DARPA under contract number F33615-C , whch s montored by Wrght Patterson Ar Force Base. 8. References [1] Sztpanovts, J. and Karsa, G.: Model-Integrated Computng, IEEE Computer, Aprl, [2] Ledecz, A. et.al.: Metaprogrammable Toolkt for Model-Integrated Computng, Engneerng of Computer Based Systems (ECBS), Nashvlle, TN, March, [3] Ledecz A., et.al.: The Generc Modelng Envronment, Workshop on Intellgent Sgnal Processng, Budapest, Hungary, May 17, [4] GME 2000 User s Manual, avalable from [5] Long E., Msra A., Sztpanovts J.: Increasng Productvty at Saturn, IEEE Computer Magazne, August, [6] Nordstrom G.: Formalzng the Specfcaton of Graphcal Modelng Languages, Proceedngs of the IEEE Aerospace 2000 Conference, CD-ROM Reference , Bg Sky, MT, March, [7] Davs J., Scott J., Sztpanovts J., Martnez M.: Mult-Doman Surety Modelng and Analyss for Hgh Assurance Systems, Proceedngs of the Engneerng of Computer Based Systems, pp , Nashvlle, TN, March, [8] Bryant, R.E., Symbolc Boolean Manpulaton wth Ordered Bnary Decson Dagrams, Techncal Report CMU-CS , School of Computer Scence, Carnege Mellon Unversty, June [9] Bapty T., Neema S., Scott J., Sztpanovts J., Asaad S.: Model-Integrated Tools for the Desgn of Dynamcally Reconfgurable Systems, VLSI Desgn, 10, 3, pp , [10] Agrawal A. et. al.: MILAN: A Model Based Integrated Smulaton Framework for Desgn of Embedded Systems, Workshop on Languages, Complers, and Tools for Embedded Systems (LCTES 2001), Snowbrd, Utah, June 2001.

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