A comparison of MPCP and MSRP when sharing resources in the Janus multiple-processor on a chip platform

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1 A comparson of MPCP and MSRP when sharng resources n the Janus multple-processor on a chp platform Paolo Ga, Marco D Natale, Guseppe Lpar, Scuola Superore Sant Anna, Psa, Italy {pj,marco,lpar}@sssup.t Claudo Gabelln, Paolo Marceca Magnet Marell Powertran Dv., Bologna, Italy {marceca,gabelln}@bologna.marell.t Alberto Ferrar PARADES, Roma, Italy aferrar@parades.rm.cnr.t Abstract The new generaton of embedded systems for automotve applcatons can take advantage of low-cost multprocessor system-on a chp archtectures. The real-tme software applcatons runnng on these systems requre realtme processor schedulng, and also requre the management of the communcaton and synchronzaton of tasks executng on dfferent processors wth lmted blockng tme. Conventonal real-tme technologes, lke the Rate Monotonc schedulng algorthm together wth the Multprocessor Prorty Celng Protocol (MPCP) can be used to ths purpose. In earler work, we proposed the Multprocessor Stack Resource Polcy (MSRP) for schedulng tasks and sharng resources n multprocessor on a chp archtectures. In ths paper we present an expermental evaluaton that compares the performance of our algorthm wth a soluton based on Rate Monotonc and MPCP n the context of the Janus multple processor archtecture. The evaluaton of the algorthm has been trggered by our ongong research n the automotve doman. We report on two sets of experments: the frst addresses a range of generc task confguratons to see f one of the algorthms can clearly outperform the other. The results show MSRP to be better for random task perods but are probably not conclusve. Later, we focus on a more applcaton-specfc (also more restrctve) archtecture desgn representng a typcal automotve applcaton: a power-tran controller. In ths case, MSRP clearly performs better. The performance gap between the two polces can be further ncreased when consderng that MSRP s much smpler to mplement, t has a lower overhead, and t allows RAM memory optmzaton. Keywords: real-tme, operatng systems, multprocessor, schedulng, system-on-a-chp Introducton The exponental growth of slcon capacty allows the prolferaton of embedded systems n dfferent applcaton Fgure. The Janus Dual Processor system domans offerng unprecedented performance and functonalty. In present and future hard real-tme automotve applcatons, the ntroducton of multple-processor-on-achp archtectures s seen as a very lkely soluton [4]. The Janus mcrontroller (Fgure ), developed by PA- RADES, ST Mcroelectroncs and Magnet Marell n the context of the MADESS project, s an example of a dual-processor platform for power-tran applcatons. Two 32-bt ARM7TDMI processors connected by a crossbar swtch to 4 memory banks and two perpheral buses for I/O processng (low and hgh bandwdth) provde twofold computatonal power, compared to a sngle (ARM7TDMI) processor archtecture, at very low ncrement of the slcon area,.e. at comparable system costs. Both CPUs share the same address space. The man memory s organzed n dfferent modules and types: SRAM and FLASH. In archtectures wth multple processors, memory access s the most mportant bottleneck of the system. Almost any communcaton flow s between the memory and other system components. To allow correct synchronzatons and communcaton among tasks allocated to dfferent processors, the archtecture provdes hardware support for nter-processor communcaton by nterrupt nter-processor mechansms and for shared memory by atomc test-and-set. The applcatons runnng on the new sngle-chp platform requre predctable (and fast) schedulng algorthms.

2 In addton, kernels must ft n a few kbytes of memory, and, together wth the applcaton, they must use the smallest possble amount of RAM memory. Resource sharng must be carefully handled and all communcaton prmtves on shared memory must be desgned n order to allow for a lmted blockng tme. In prevous work [5] we presented the MSRP schedulng and resource sharng protocol, whch allows reducng the amount of RAM memory allocated to the task stacks. One of the possble drawbacks of the MSRP polcy (and a major concern for the automotve applcaton developers that cooperate wth us n MADESS) s the cost of spn lockng n multprocessor real-tme systems when compared to other polces. In contrast, the multprocessor prorty celng protocol or MPCP, probably the best known polcy for boundng blockng tme n a predctably way n multprocessor systems, avods spn-lockng, but does not allow sharng the stack space of tasks. Furthermore, t requres a non trval run-tme support, whch results n greater overhead when compared to the mplementaton of MSRP. In order to settle ths dspute, we performed experments comparng MSRP wth MPCP n Janus. The experments are n two stages. In the frst stage, our smulator evaluates the schedulablty of a number of generc task sets to see f one of the algorthms can clearly outperform the other. The results are not conclusve, except that (as expected) MSRP s better when consderng few global resources and short crtcal sectons. In the second stage we focus on a doman-specfc example: a task set mplementng a power-tran controller, whch s the representatve of a typcal automotve applcaton and our target demonstrator n MADESS. For ths case, MSRP clearly outperforms MPCP, provng the vablty of a spn-lock based approach for sharng resources on the Janus platform. The structure of the paper s the followng: Sectons 2 and 3 contan the descrpton of our termnology and references to fundamental work n ths area. Sectons 4 and 5 contan a short ntroducton to the MPCP and MSRP algorthms wth the correspondng schedulablty analyss formulas. Secton 6 contans a comparson study of the two methods, dscussng ther mplementaton. Secton 7 provdes a general ntroducton to power-tran control systems and ntroduces the thread archtecture of the case study. Fnally, Secton 8 contans the expermental results of our smulatons n the general case and n our target automotve applcaton. 2 Assumptons and termnology In the paper, we use the terms thread and task nterchangeably. The assumptons and defntons for the terms and symbols used n the paper are the followng: Our embedded applcaton conssts of a set T = {τ,τ 2,...,τ n } of real tme tasks to be executed on a set P = {P,...,P m } of processors. The subset of tasks assgned to processor P k wll be denoted by T Pk T. Ataskτ s a nfnte sequence of jobs (or nstances) J,j. Every job s characterzed by a release tme r,j,an executon tme c,j, an absolute deadlne d,j and a prorty p A task can be perodc or sporadc. Wthout loss of generalty, we use the same symbol θ to ndcate the perod or the mnmum nterarrval tme of task τ. In the followng a task wll be characterzed by a worst case executon tme C =max{c,j } and a perod θ. We assume that the relatve deadlne of a task s equal to ts perod θ : thus, d,j = r,j + θ. Tasks are allowed to access mutually exclusve resources through crtcal sectons. Let R = {ρ,...,ρ p } be the set of shared resources. The k th crtcal secton of task τ on resource ρ j s denoted by ξ j k and ts maxmum duraton s denoted by ω j k. Fnally, we suppose that tasks have been statcally allocated to Processors and are always executed on the same processor. Dependng on ths allocaton, resources can be dvded n local and global resources. A crtcal secton protectng a global resource s called global crtcal secton or gcs. 3 Related Work The unprocessor Prorty Celng Protocol or PCP (see [2]) s one of the best known polces for avodng prorty nversons and lmtng the blockng tme n sngle processor systems. The PCP polcy defnes a (statc 2 ) celng attached to each semaphore (resource ρ k ) as the maxmum prorty among all tasks that can possbly lock the semaphore. cel(ρ k )=max{p τ uses ρ k }. and a dynamc system celng s defned as Π s (t)=max[{cel(ρ k ) ρ k s currently locked} {0}]. Job J requestng a resource s blocked f ts prorty s not hgher than the system celng. The Prorty Celng Protocol, whch can be used together wth the Rate Monotonc (RM) scheduler [8], ensures that a job can be blocked only once when accessng a shared resource held by a lower prorty job. Ths delay s called blockng tme and denoted by B. The maxmum local blockng tme for each task τ can be calculated as B = max τ j T, h {ωk jh p >p j p cel(ρ k )}. () The schedulablty condton for the PCP protocol when used together wth a RM scheduler s:, n n k= C k θ k + B θ n(2 /n ) (2) 2 In the case of mult-unts resources, the celng of each resource s dynamc as t depends on the current number of free unts.

3 The Stack Resource Polcy was proposed by Baker n [2] for schedulng a set of real-tme tasks on a unprocessor system. The SRP s smlar to the Prorty Celng Protocol, but t has the addtonal property that a task s never blocked once t starts executng. Lke PCP t can be used together wth RM or EDF. Accordng to the SRP, every real-tme (perodc and sporadc) task τ must be assgned a prorty p and a statc preempton level λ, such that: task τ s not allowed to preempt task τ j, unless λ >λ j. The defnton of the semaphore celng used by SRP s slghtly dfferent from the one used by PCP, snce t does nvolve preempton levels nstead of prortes: cel(ρ k )=max{λ τ uses ρ k }. and the SRP schedulng rule states that: a job s not allowed to start executng untl ts prorty s the hghest among the actve jobs and ts preempton level s greater than the system celng. The SRP ensures that, once a job s started, t cannot be blocked untl completon; t can only be preempted by hgher prorty jobs. However, the executon of a job J,k wth the hghest prorty n the system could stll be delayed by a lower prorty job. In [2] Baker proved that Formula 2 can be used for checkng the schedulablty of tasks under SRP+RM (or EDF). The maxmum blockng tme can be computed n exactly the same way t s computed for PCP, wth the only excepton that preempton levels must be consdered nstead of prortes. From an mplementaton vewpont, SRP allows tasks to share a unque stack. In fact, a task never blocks ts executon. The mplementaton of the SRP s straghtforward as there s no need to mplement watng queues. Furthermore, by dsablng (some) preempton [], the requrements for stack space can be reduced. Our MSRP algorthm extends ths dea to dynamc schedulng and multprocessor systems. The Multprocessor Prorty Celng Protocol (MPCP) has been proposed by Rajkumar n [0] for schedulng a set of real-tme tasks wth shared resource on a multprocessor. It extends the Prorty Celng Protocol [2] for global resources. Snce ths polcy s the term of comparson for our MSRP polcy we wll spend some extra tme dscussng ts features. 4 The MPCP Multprocessor Prorty Celng Protocol If tasks block on semaphores protectng global resources, the concept of blockng needs to nclude also remote blockng (when a job has to wat for the executon of a task of any prorty assgned to another processor.) MPCP extends the prorty celng protocol to multprocessor systems wth the assumpton that tasks are statcally bound to processors and scheduled accordng to the rate monotonc polcy. The goal of MPCP s to bound the remote blockng duraton of a job as a functon of the duraton of crtcal sectons of other jobs and not as a functon of the duraton of non-crtcal code. As a drect consequence, t s necessary that global crtcal sectons are assgned a celng that s hgher than the prorty of any other task n the system. If p H s the hghest prorty among all tasks, a prorty of p H ++max {p τ uses ρ k } s the prorty celng for the semaphore protectng the global resource ρ k.other mportant desgn choces of MPCP are the followng: jobs are suspended when they try to access a locked gcs; when a hgher prorty task s blocked on a global crtcal secton local tasks can be executed and may even try a lock on local or global crtcal sectons; when a global resource s released the task watng on top of the semaphore lst s awakened and nherts the prorty of the global crtcal secton. One very mportant consequence of lettng lower prorty local tasks execute and possbly nhert the prorty of global crtcal sectons s the possblty of prorty nverson occurrng whle a hgh prorty task s blocked on a gcs. Other assumptons are the followng: local crtcal sectons do not make nested access to global resources and vce versa, furthermore, nested accesses to global crtcal sectons are prohbted. MPCP allows for a bounded blockng tme and a formula exsts for checkng the schedulablty of real-tme tasks. The formula s an adaptaton of Formula 2 (to be evaluated for each processor) wth the only dfference that the blockng factor B must account for local and global prorty nversons. In order to smplfy the formulaton of the fve factors that add up to form the factor B the followng addtonal defntons are ntroduced. Task τ can access local (.e. allocated on the same processor) or global resources. The number of global crtcal sectons executed by τ s n G. NL,j s the number of jobs wth a lower prorty than J on ts processor. {J () r } s the set of jobs on processor P r wth gcs havng prorty hgher than global crtcal sectons that can drectly block J. NH,r,j s the number of global crtcal sectons of job J j {J () r } wth hgher prorty than a global crtcal secton on processor P r, whch can drectly block J. {ngs,j } s the set of global semaphores locked by both J and J j. Fnally, NC,j s the number of global crtcal sectons entered by J j and guarded by elements of {ngs,j }. The blockng tme for a job J on processor P j conssts of up to fve dfferent factors: where B = B + B 2 + B 3 + B 4 + B 5 B = n G ωlocal (where ω local s the longest crtcal secton accessed by jobs wth a prorty lower than J executng on the same processor), snce each tme J needs a global semaphore may suspend, lettng lower

4 prorty jobs execute on ts processor. These low prorty jobs can lock local semaphores and block J when t resumes ts executon. B 2 = n G ωglobal j (where ω global j s the longest global crtcal secton accessed by jobs wth a prorty lower than J executng on other processor) when job J tres to access a global crtcal secton and fnds t s accessed by a lower prorty job on another processor. B 3 = NC,j T /T j ω global j for each hgher prorty job executng on a processor dfferent from P and requestng the same global semaphore as J. B 4 = NH,r,j T /T j ω global j for hgher prorty global crtcal sectons, whch can preempt the global crtcal sectons of lower prorty jobs drectly blockng J. B 5 = mn(n G +,n G k )ωglobal j each tme J tres to access a global crtcal secton t can suspend lettng lower prorty jobs execute on ts processor. These jobs can lock or queue on global semaphores and eventually execute at a prorty hgher than P and preempt t when t executes non global code. 5 Multprocessor SRP The MSRP polcy provdes a soluton to the resource sharng as well as the task allocaton problem. Snce EDF+SRP cannot be drectly appled to multprocessor systems, we proposed an extenson of these protocols. Ths soluton allows tasks to use local resources under the SRP polcy and to access global resources wth a predctable blockng tme wthout nterferng wth the local executon order. Ths mechansm, when used n conjuncton wth preempton thresholds, and the creaton of non preemptve task groups [] [5] allows to perform tme guarantees mnmzng the requrement for RAM space. Accordng to our MSRP polcy, f a task tres to access a global resource and the resource s already locked by some other task on another processor, the task performs a busy wat (also called spn lock). The spn lock tme should be reduced as much as possble (the resource should be freed as soon as possble). For ths reason, the tasks become nonpreemptable when executng a crtcal secton on a global resource. The MSRP algorthm works as follows: For local resources, the algorthm s the same as the SRP algorthm. Tasks are allowed to access local resource through nested crtcal sectons (t s not possble to nest global crtcal sectons). When a task τ, allocated to processor P k accesses a global resource ρ j, the task becomes non preemptable. Then, the task checks f the resource s free: n ths case, t locks the resource and executes the crtcal secton. Otherwse, the task s nserted n a FCFS queue on the global resource, and then performs a busy wat. When a task τ, allocated to processor P k, releases a global resource ρ j, the algorthm checks the correspondng FCFS queue, and, n case some other task τ j s watng, t grants access to the resource, otherwse the resource s unlocked. The task becomes agan preemptable. The spn lock tme that every task allocated to processor P k needs to spend for accessng a global resource ρ j R s bounded from above by: spn(ρ j,p k )= max τ T p, h ωj h. p {P P k} Bascally, the spn lock tme ncrements the duraton ω j h of every global crtcal secton ξj h, and, consequently, the worst case executon tme C of τ. Moreover, t also ncrements the blockng tme of the tasks allocated to the same processor wth a preempton level greater than λ. We defne the actual worst case computaton tme C for task τ as the worst case computaton tme plus the total spn lock tme: C = C + spn(ρ j,p k ) ξ j h MSRP mantans the same basc propertes of the SRP, that s, once a job starts executng t cannot be blocked, but only preempted by hgher prorty jobs and a job can experence a blockng tme at most equal to the duraton of one crtcal secton (plus the spn lock tme, f the resource s global) of a task wth lower preempton level. It s noteworthy that the executon of all the tasks allocated on a processor s perfectly nested (because once a task starts executng t cannot be blocked), therefore all tasks can share the same stack. The blockng tme for a task can be dvded nto blockng tme due to local and global resources. B = max(b local,b global where B local B local B global and B global ) are defned as: =max j,h,k {ωk jh (τ j T P ) (ρ k s local to P ) (λ >λ j ) (λ cel(ρ k ))} =max j,h,k {ωk jh + spn(ρk,p ) (τ j T P ) (ρ k s global) (λ >λ j )} Suppose that tasks on processor P k are ordered by decreasng preempton level. The schedulablty test s as follows: P k P T Pk = {τ,...,τ nk } =,...,n k C l + B (3) θ l θ l=

5 6 Comparng MSRP wth MPCP The blockng factors B,...B 5 of MPCP are the result of a worst case analyss and can be reduced by carefully examnng the task set at hand. Nonetheless the guarantee formula s clearly extremely complcated. Consder also that PCP requres keepng track of local and global prorty celngs and the prevous formula holds f the perod enforcng technque (descrbed n [0]) s used. If, on the other hand, MSRP s used, we can expect to waste more local processor tme due to the use of spn locks when tryng to lock global resources. The guarantee formula of MSRP s smpler snce we do not have to account for the events that cause the blockng factors B and B 5 whch are the consequence of suspendng a task when tryng to access a locked crtcal secton. At frst sght, t would appear that, whenever global crtcal sectons are suffcently short, the MSRP approach would perform better (besdes beng much smpler to mplement). On the other hand, MPCP should be better when global crtcal sectons grow larger. We performed a frst set of experments tryng to determne where ths boundary les and n what condtons should desgners expect MSRP to perform adequately. Followng the results of these experments, we focused our analyss on a power-tran applcaton: a typcal case study from the automotve doman. 6. Implementaton notes An mplementaton of the MPCP protocol can be bascally dvded n two parts [0]: the mplementaton of a local prorty celng protocol and the mplementaton of the global nter processor synchronzaton. The local part of the protocol mplementaton can easly be done usng a prorty ordered Task queue, where the hghest prorty task n the queue s the runnng task. Moreover, a lst of locked semaphores (ordered by celng) has to be mantaned to allow the mplementaton of the nhertance of the prorty. The global part of the protocol subsumes the exstence of a shared data structure that records the state of a global mutex. In partcular, an ordered queue of the tasks that are blocked on the global resource has to be mplemented. The low-level access to that data structure has to be done n mutual excluson, and that s usually done usng a spn-lock approach (the duraton of the spn lock s not accounted nto the guarantee equatons, snce t s bounded by the maxmum tme needed to handle the nternal data structure) or usng an nter-processor nterrupt. Moreover, to guarantee a bounded blockng tme the Perod Enforcer technque must be mplemented. When usng SRP there s no need to mplement semaphores and queues for blocked tasks, and the blockng tme experenced by each task can be predctably bound. Furthermore, the SRP allows multple tasks to share a sngle stack. For these reasons, the SRP can be mplemented wth a small overhead and memory occupaton. The mplementaton of MSRP on the Janus platform has been smplfed by takng advantage of the fact that there are only two processors contendng for the use of global resources. In partcular, only one processor at a tme can be blocked on a global resource, so FCFS queues are not needed for watng tasks. Moreover, mplementng a spn-lock mechansm on Janus only requres a neglgble amount of code. Snce all memory s shared between the two processors, a sngle memory locaton can be used to synchronze all tasks usng theswpb ARM nstructon. 7 The Power-tran Control Applcaton 7. Introducton The goal of power-tran control systems s to offer approprate drvng performance (e.g. drveablty, comfort, safety) whle mnmzng fuel consumpton and pollutant emssons. In an engne management system, the fuel njecton and ar ntake are controlled to produce the desred mx to be transformed, by the combuston process, n torque and emssons. The combuston process takes place n the cylnders and the startng tme s controlled by the sparks generated from the spark plugs. The produced torque s then appled to the power-tran, whch s controlled by the gear selecton and clutch poston. The fnal result s the force appled, through the wheels, to the entre vehcle. Drveablty s an nformal measure of how favorably ths force s perceved by the drver under hs/her acton. The control strategy goal s acheved by means of several control nputs such as throttle poston, fuel njecton, spark gnton, gear selecton and clutch poston. Fuel njecton, spark gnton and part of the gear-box control are angle-based,.e. they must be synchronzed wth the engne poston 3 or drve-lne angle. The other control varables do not have these synchronzaton constrants and are called tme based. To compute the engne poston, the engne has two sensors (the crankshaft and cam-shaft toothed wheel sensors) provdng two angular references used for njecton and gnton synchronzaton. Synchronzaton s essental for tmng the openng of fuel njectors and the gnton of the spark plugs. The suppled torque and the emtted pollutants depend crucally on the accuracy of these operatons. 7.2 Task archtecture In order to evaluate the performance of the resource sharng algorthms for our target applcaton, we need a model vew representng the thread archtecture. The vew must defne the typcal abstractons used n schedulablty 3 For engne poston we mean both the angular poston of the crankshaft and the workng phase (.e. ntake, compresson, expanson or exhaust) of each cylnder.

6 nput to functonal block INC count_out sum_out code mplementaton of the functonal block _ Z output from functonal block Thread = LIMIT RESET Fgure 2. A thread contans the mplementaton of several functonal blocks analyss, such as the real-tme threads, each characterzed by ts actvaton mode (perodc or sporadc), and ts tmng characterstcs (such as the WCET) and the shared resources used by the threads, wth the executon tmes of the methods called upon them. An extremely short ntroducton to a typcal automotve software development process can help understandng the nature of the applcaton threads and the relatonshps between them and the set of shared resources. The threads runnng under the control of the RTOS are the result of a software development process, whch starts from the defnton of a hgh level model (usually a functonal model obtaned from a tool lke Smulnk) and contnues wth the automatc producton of software code mplementng the functonal blocks defned n the model. The code mplementng the functonal blocks s statcally scheduled n the context of a thread (see fgure 2). As a result, each thread performs many read and wrte accesses to the nput and output varables or devces defned by the functon blocks mplemented n t. These sets of nput and output varables/devces are possbly mplemented as shared resources and the resultng graph of use relatonshps among tasks and resources s qute densely connected, wth each task accessng many resources. Unfortunately, the exact specfcaton of the applcaton archtecture and ts performance/tmng data (as mplemented n the current verson of the controller) are consdered extremely senstve ndustral property. Furthermore, the current mplementaton s on a sngle-cpu controller and t s expected that t wll change when ported to the new Janus archtecture. Gven ths restrcton, our analyss had to settle for realstc data on the applcaton threads and resources, whch could be used for measurng the qualty of the algorthms and comparng ther performance. The model vew we analyzed can be consdered a good abstracton of the current mplementaton and the startng pont for evaluatng algorthms and solutons (on the worst-case sde) for the upcomng Janus mplementaton. Based on the analyss of the current mplementaton and based on the number and complexty of the functon ponts n the new mplementaton, we consdered from 0 to 20 perodc tasks and from 2 to 6 aperodc tasks wth perods rangng from ms to about 00 ms (gven the dependency from specfcaton requrements, such as the maxmum rpm of the engne, the rate requrements should be consdered qute relable data). Tasks are dvded nto 3 classes: hgh rate: from ms to 5 ms. medum rate: from 5 to 20 ms. low rate: from 50 to 00 ms. Tasks are dstrbuted among the three classes n ths way: 50% of the tasks belong to the medum rate type, the other types account for 25% of the tasks each. The processors are qute heavly utlzed, utlzaton ratos above 70% should be expected for each processor. The fracton of the processor utlzaton requred by each class s the followng: 50% of the processor tme s used to serve hgh rate tasks, 30% s allocated to the medum rate class, and the last 20% wll be used to execute the low rate class. As for resources, tasks share both physcal and logcal resources. The Janus physcal resources shared by tasks are the I/O channels for Analog to Dgtal (A/D) converson and the seral ports that are used for communcaton wth the outsde systems. The logcal resources consst of memory buffers for communcaton. Both knd of resources are protected by prorty celng (MSRP or MPCP) semaphores. Access to the shared I/O channels can be characterzed as follows: the seral bus s expected to work at hgh speeds (the target rate s 500 kb/s) transmttng one byte at a tme, correspondng to about 50 µs of requred access tme. The seral communcaton wll be used only once for each task. Two seral ports (UARTs) are mplemented n Janus. The A/D converson devce can be used multple tmes, from 5 up to a maxmum of 0 tmes for each task. The A/D access tme s domnated by the setup tme, resultng n crtcal sectons of about 5 µs. Tasks are expected to communcate through shared memory resources, whch are of two types: swtched (nowat) buffers and one-poston malboxes. Resources of the frst type do not actually need semaphores, snce the ponter swappng nstructon s provded as an atomc nstructon by the ARM processors and only one wrter task s expected for these resources. As for the second type of resources, tasks are expected to cooperate by exchangng nformaton on ther nternal state as a set of shared varables. These sets consst of 20 to 50 sets, each one contanng between 0 and 300 varables, whch must be wrtten and read atomcally, n order to keep the state consstent. Each varable s mplemented usng a 6 or 32 bt data type. These shared memory requrements actually represent a worst case approxmaton and n no case wll the overall memory allocated to shared varables exceed an

7 archtecture-specfc bound of 6 KBytes. Wrte operatons are expected to affect all the varables n the data set, and read operatons only address a subset (unformly dstrbuted between 0% and 00%) of the varables n the set. Each task s expected to perform from 3 up to 20 read accesses and from 2 to 8 wrte accesses to the sets of varables. Fnally, n order to ease the schedulablty of the task set, a large percentage of the resources accessed by hgh rate tasks s mplemented by usng swtched buffers (when allowed by the communcaton semantcs). 8 Expermental results 8. Generc task sets In the frst set of experments we compare the performance of the MSRP and MPCP algorthms on a range of task confguratons (random load) mapped on the 2 Janus processors. The experments consder a set of 6 to 0 tasks statcally allocated to each CPU. Dependng on the experments, task perods are chosen randomly between and 00 or by selectng approprate harmonc values. Harmonc perods are generated n the followng way: the perod of the frst s ; the perod of the next task s gven by the perod of the prevous task multpled by a random factor between and 4 (rato has the 30% of probablty, ratos 2,3,4 share the remanng 70%). If U s the system utlzaton, defned as U =Σ c /θ, the total load on each CPU ranges from U mn and U max, where U mn ranges from and wth steps of 0.025, and U max ranges from Umn to 0.95 wth steps of The number of local resources s always 6 for each processor, plus 6 global resources. The number of crtcal sectons accessed by each task s a random value chosen n the ntervals (0,2), (,4), (2,6) dependng on the experment. Tasks spend a percentage of ther computaton tme nto crtcal sectons. The fracton of executon tme that s spent n a crtcal secton (local or global) ranges between C mn and C max,wherec mn and C max belong to the set {0.0, 0.5, 0.0, 0.5, 0.2}, andc max s always greater than C mn. For each task set we consder a set of 0 possble confguratons, obtaned consderng that the tme spent n a crtcal secton s allocated for a percentage x to local crtcal sectons, and for a percentage (-x) to global crtcal sectons, wth x rangng from 0 to wth steps of 0.0. On each confguraton we check f the MSRP and the MPCP tests can guarantee the task set as schedulable (more than 520 mllon confguratons were tred). The frst set of experments s performed on task sets where perods are randomly chosen. The graphs show the percentage of tasks sets that can be guaranteed to be schedulable. It s easy to see how the MSRP polcy performs better than MPCP manly because of the hgher utlzaton bound of EDF when compared to Rate Monotonc. % of schedulable solutons Boundary between MSRP and MPCP, random perods, 2 CPUs MSRP 0% local 0.4 MSRP 25% local MSRP 50% local MSRP 75% local 0.3 MSRP 00% local MPCP 0% local MPCP 25% local 0.2 MPCP 50% local MPCP 75% local MPCP 00% local Maxmum utlzatonfactorfor each CPU Fgure 3. Percentage of schedulable solutons, random perods, varable percentage of local resource utlzaton. For hgher utlzatons the guarantee rate decreases, most notably for the MPCP soluton, where t fnally approaches a hyperbolc bound for hgher values (the hyperbolc bound for rate monotonc schedulng defned n [3] s used). In general, n all our experments on random task perods, MSRP always performed better. Even f ths s mostly due to the use of EDF as a task schedulng polcy, t s our opnon that ths advantage should not be easly dsmssed. A comparson whch does not gve an a pror advantage to MSRP because of the hgher schedulablty bound of EDF can be obtaned by selectng task sets where perods are harmonc, therefore havng a utlzaton bound of for the Rate Monotonc polcy. The results for ths case (Fgure 4) show that there s no algorthm performng better on the whole scale of the spectrum (for all the possble percentages of local resources). As one should expect MPCP performs better for a hgher percentage of global resources whle MSRP s better f a greater percentage of local resources s smulated. The MPCP curves are always between the mnmum and the maxmum curves for MSRP. Ths mples the exstence of a crossng pont whch dentfes the percentage of local access to resources that, for each U separates the zone where MPCP performs better from the range where MSRP guarantees an hgher percentage of schedulable sets. To better hghlght these regons t s useful to plot the data wth a dfferent X axs varable: the percentage of local resource utlzaton. For example, n Fgure 5 MSRP outperforms MPCP for hgh local resource usage, that s when at least 40% of the resource access tme s on local resources (the upper rght regon n Fgure 5). It can be noted that, as U ncreases, the lnes decrease (snce the system load s greater, fewer schedulable solutons can be found). Moreover, when the use of global resources ncreases (X axs gong to 0) there s a pont (the boundary n the fgure) where MPCP starts to perform bet-

8 Compartson between MPCP and MSRP wth harmonc perods Boundary between MSRP and MPCP, harmonc perods, 2 CPU 0.9 % of schedulable solutons % of schedulable solutons MSRP 0% local MSRP 50% local MSRP 00% local 0.2 MPCP 0% local MPCP 50% local MPCP 00% local Maxmum utlzatonfactorfor each CPU % CS length 5-0% CS length 0-5% CS length 5-20% CS length % of local resources Fgure 4. Percentage of schedulable solutons, harmonc perods, varable percentage of local resource utlzaton. Fgure 6. Boundary obtaned consderng 2 CPUs wth varous resourse usages. 8.2 The power-tran case % of schedulable solutons MSRP/MPCP boundary MSRP, U=0.275 MSRP, U=0.525 MSRP, U=0.775 MSRP, U=0.0 MPCP, U=0.275 MPCP, U=0.525 MPCP, U=0.775 MPCP, U=0.9 Boundary between MSRP and MPCP, harmonc perods, 2 CPUs %of local resources Fgure 5. Comparson of MPCP and MSRP wth the performance boundary (Y=percentage of schedulable solutons, X=percentage of local crtcal sectons). ter (whch can be explaned because the spn lockng term nfluences not only the blockng tme, but also the task computaton tme). A contnuous splne, nterpolatng the crossng ponts n the fgure gves an dea of the boundary between the areas where the two algorthms perform better. Experments clearly show how the area where the MSRP protocol performs better ncreases for a hgher use of shared resources. Ths s a sde effect of the reducton of schedulablty caused by a hgher use of shared resources. In case of Fgure 6, the lnes are not smply splnes, but are the result of comparatve experments for ponts of the plane (U, % of local resources). The results of our experments on generc task sets can hardly be consdered conclusve for the harmonc perods case. In our second set of experments we focused on our power-tran specfcatons, to see f more knowledge could be ganed when restrctng the applcaton doman. The task sets used for the evaluaton of our powertran case study were created usng the abstract archtecture specfcaton defned n Secton 7. Utlzaton A set of experments was performed for dfferent values of the system (2 CPUs) utlzaton. We consdered utlzaton values from.4 to.96 wth steps of In our graphs, the utlzaton value s the varable on the X axs. Tasks The total number of tasks n each experment s a random varable wth nteger values unformly dstrbuted n the nterval [2, 26]. Tasks are dvded n three subclasses accordng to ther rate of executon. We generate tasks wth random perods and wth harmonc perods. Perods have nteger values (n msecs). Worst case executon tmes are chosen n a way that the utlzaton of each class of tasks sums to the desred value for the class. Task allocaton s performed by a smulated annealng algorthm (descrbed n [5]). Resources and Crtcal Sectons Physcal resources are modeled as follows. The Janus chp has two seral ports (UARTs). We assume each seral port s allocated to the tasks runnng on one of the CPUs. In ths way the seral port s a resource shared only among local tasks. Resource ndex 0 s reserved to the Seral I/O channel. The crtcal sectons that use the UART are assumed as 50 µs long. Resource s the A/D converter. The crtcal sectons that use ths resource are 5 µs long. The remanng resources are shared memory resources. Gven the requrements of Secton 7, each memory resource uses from 20 to 200 bytes of memory. For our target (ARM-based) Janus plat-

9 form, the maxmum length of a crtcal secton s estmated as memorysze µs4. The smulator loop that generates the resource sets stops at the 6 Kbytes lmt and the crtcal sectons computed n step 4 are accepted untl the total crtcal secton tme s lower than the task WCET. Fnally, every crtcal secton accessed by a hgh rate task has a 40% probablty to be deleted, to account for the fact that hgh prorty tasks wll use swtched buffers when possble n order to reduce ther blockng tme. % of schedulable solutons % of schedulable solutons Comparson between MPCP and MSRP wth random perods MSRP MPCP Total Utlzato on each CPU Fgure 7. Percentage of schedulable task sets wth randomly selected perods on Janus by MPCP/MSRP Comparson between MPCP and MSRP wth harmonc perods MSRP MPCP Total Utlzato on each CPU Fgure 8. Percentage of schedulable task sets wth harmonc perods on Janus by MPCP/MSRP. 4 4to83µs s the expected tme to wrte the data on a 40Mhz Janus platform Results We ran experments for ncreasng processor utlzaton factors from.4 (approxmately 0.7 for each CPU) to 2.0. Frst, sets of tasks wth random nteger perods were tred. After processng about 6000 task sets generated accordng to our specfcatons, we obtaned the results shown n Fgure 7. Ths tme the performance dfference between the two algorthms s strkng: not a sngle task set s found schedulable wth MPCP and the schedulablty rato provded by MSRP goes down (almost lnearly) from about 50% at.4 utlzaton (0.7 for each CPU) to about 0 at.98 utlzaton. In the graph of Fgure 7, the MPCP curve s not vsble snce t s completely hdden by the X axs. The stuaton does not mprove sgnfcantly for MPCP when the task perods are forced to be harmonc. The MPCP guarantee rato goes barely up for.4 utlzaton but t s always below 0%. No schedulable set s found under MPCP for utlzaton values hgher than.7. In contrast, MSRP contnues to delver an acceptable performance gong from more than 40% of schedulable sets at.4 utlzaton, to vrtually no schedulable soluton at.8/.9 utlzaton. When compared wth the generc task graphs tred n the prevous set of experments, our power-tran case study has at least two strkng dfferences: Each crtcal secton s qute short when compared to the executon tme of the tasks. In our power-tran case hgh rate tasks spend up to 20% of ther tme whle accessng crtcal resources, but the tme spent by medum and low rate tasks s sgnfcantly lower. Furthermore, n our test case, the tme spent n each crtcal secton s qute small when compared to our prevous experments, snce tasks perform more accesses but wth shorter executon tmes. In the context of the results on the generc task sets ths means we expect our power-tran applcaton to be n the range of qute low resource usage. Each task uses many resources and each resource s accessed by many tasks. In our prevous case, tasks used from 0 up to a maxmum of 6 crtcal sectons each. In the power-tran case there s a much more connected graph of task-resource use relatonshps. In turn, ths means more pessmsm n the evaluaton of the worst case assumptons of MPCP, snce the factors n G, NC,j and NH,r,j from whch the blockng factors of MPCP depend lnearly are now sgnfcantly hgher. On the other sde, the blockng factors of MSRP depend only on the worst case length of ndvdual crtcal sectons. Snce we expect both characterstcs to be qute common for automotve applcatons developed accordng to the gudelnes descrbed n Secton 7 we expect MSRP to retan a sgnfcant advantage over MPCP even under sgnfcant changes n the number of task and/or resources n

10 the fnal mplementaton. 9 Conclusons and Future work Usng spn-lock for accessng mutual exclusve resources n real-tme mult-processor systems can possbly lead to a non-schedulable system, because the worst case executon tme of a task s ncreased whle keepng the processor dle. When we were faced wth the problem of desgnng a concurrency control protocol for the multprocessor Janus platform, our goal was to obtan a smple and effectve algorthm that allows extendng the SRP protocol and therefore, permts to share the stack among all the tasks that are allocated to one processor. As a soluton, we proposed a spn-lock mechansm for accessng global resources. We expected an advantage n terms of mplementaton complexty and a dsadvantage n terms of schedulablty. After performng an extensve set of smulatons, we dscovered that the spn-lock mechansm s not necessarly a dsadvantage, but performs even better (n terms of schedulablty guarantees) for gven applcaton contexts. Our smulatons show that no algorthm outperforms the other on the whole spectrum of the possble task sets. Whch one s the best n terms of the schedulablty bound depends on the characterstcs of the task set: when access to local resources s clearly domnatng wth respect to the use of global crtcal sectons, and when the crtcal sectons are short, MSRP presents a better schedulablty bound than MPCP. A second set of experments, performed on a powertran case study, clearly showed how MSRP can guarantee a hgher percentage of task sets when compared to MPCP. Fnally, even n the cases n whch MPCP s better than MRSP, t should be consdered that MSRP s very smple to mplement and has a lower overhead than MPCP. In the Janus case, smplcty and memory optmzaton were the prmary goals. Regardng other possble approaches to resource sharng, an nterestng possblty s to use lock-free algorthms. Lock-free approaches to real-tme schedulng were proposed by Anderson, Ramamurthy and Jeffay n []. In ths approach, a task can execute a crtcal secton more than once, because of the possble conflcts durng access. However, when consderng perodc real-tme tasks, the number of retres s bounded. Intutvely, these approaches can be used especally for short crtcal sectons. However, a deeper study s needed. References [2] T.P. Baker. Stack-based schedulng of real-tme processes. Journal of Real-Tme Systems, 3, 99. [3] Enrco Bn and Gorgo Buttazzo and Guseppe Buttazzo. A Hyperbolc Bound for the Rate Monotonc Algorthm. Proceedngs of the 3th IEEE Euromcro Conference on Real-Tme Systems,200 [4] A. Ferrar, S. Garue, M Per, S. Pezzn, L.Valsecch, F. Andretta, and W. Nesc. The desgn and mplementaton of a dual-core platform for power-tran systems. In Convergence 2000, Detrot (MI), USA, October [5] Paolo Ga and Guseppe Lpar and Marco D Natale. Mnmzng Memory Utlzaton of Real- Tme Task Sets n Sngle and Mult-Processor Systems-on-a-chp. Proceedngs of Real-Tme Systems Symposum, 200 [6] R.L. Graham. Bounds on the performance of schedulng algorthms, chapter 5. Coffman Jr. E. G. (ed.) Computer and JobShop Schedulng Theory, Wley, New Yorj, 976. [7] J. Y. T. Leung and J. Whtehead. On the complexty of fxed-prorty schedulng of perodc, realtme tasks. Performance Evaluaton, 2: , 982. [8] C.L. Lu and J.W. Layland. Schedulng algorthms for multprogrammng n a hard-real-tme envronment. Journal of the Assocaton for Computng Machnery, 20(), 973. [9] Yngfeng Oh and Sang H. Son. Allocatng fxedprorty perodc tasks on multprocessor systems. Journal on Real Tme Systems, 9, 995. [0] R. Rajkumar. Synchronzaton n multple processor systems. In Synchronzaton n Real-Tme Systems: A Prorty Inhertance Approach. Kluwer Academc Publshers, 99. [] Manas Saksena and Yun Wang. Scalable realtme system desgn usng preempton thresholds. In Proceedngs of the Real Tme Systems Symposum, December [2] Lu Sha, Ragunathan Rajkumar, and John P. Lehoczky. Prorty nhertance protocols: An approach to real-tme synchronzaton. IEEE transacton on computers, 39(9), September 990. [3] K. Tndell, A. Burns, and A. Wellngs. Allocatng real-tme tasks (an np-hard problem made easy). Real-Tme Systems Journal, 992. [] J. Anderson, S. Ramamurthy, and K. Jeffay, Real- Tme Computng wth Lock-Free Shared Objects ACM Transactons on Computer Systems, Volume 5, Number 2, pp , May 997.