From Simulation to Experiment: A Case Study on Multiprocessor Task Scheduling


 Darlene Gray
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1 From to Exeriment: A Case Study on Multirocessor Task Scheduling Sascha Hunold CNRS / LIG Laboratory Grenoble, France Henri Casanova Det. of Information and Comuter Sciences University of Hawai i at Mānoa, U.S.A. Frédéric Suter IN2P3 Comuting Center, CNRS/IN2P3 LyonVilleurbanne, France Abstract is a oular aroach for emirically evaluating the erformance of algorithms and alications in the arallel comuting domain. Most ublished works resent results without quantifying simulation error. In this work we investigate accuracy issues when simulating the execution of arallel alications. This is a broad question, and we focus on a relevant case study: the evaluation of scheduling algorithms for executing mixedarallel alications on clusters. Most such scheduling algorithms have been evaluated in simulation only. We comare simulations to realworld exeriments in a view to identify which features of a simulator are most critical for simulation accuracy. Our first finding is that simle yet oular analytical simulation models lead to simulation results that cannot be used for soundly comaring scheduling algorithms. We then show that, by contrast, simulation models instantiated based on bruteforce measurements of the target execution environment lead to usable results. Finally, we develo emirical simulation models that rovide a reasonable comromise between the two revious aroaches. I. INTRODUCTION Exerimentation lays an imortant role in comuter science, and in articular in arallel and distributed comuting. They are used to rove or disrove conjectures, validate a model, or quantify the erformance of a articular design under realistic conditions. Methodologies range from executing a realworld alication on a realworld latform to modeling the alication behavior in a simulator [1]. s are not as realistic as realworld exeriments, which we simly term exeriments in this work. s are attractive because they allow reroducibility of results, rovide an objective basis for alication comarison, and afford the caacity to exlore a broad range of scenarios (some of them not tractable in exeriments) in a reasonable amount of time. Parallel and distributed alications consist of comutation, communication, and ossibly I/O activities. Consequently, all three must be accurately modeled by a simulator. models used in ractice range from analytical models to oerational models. For instance, the execution of an alication task can be simulated using a simle analytical model based on comutational comlexity, or via execution of actual alication code on a cycleaccurate simulator. Similarly, network communication can be simulated using a simle latency/bandwidth affine model of data transfer time, or using a acketlevel simulator. The motivation for analytical models is clear: they can be comuted quickly and scalably (e.g., a few minutes of simulation on a single comuter could suffice for simulating an alication that runs for several hours on hundreds of cluster nodes). Consequently, the vast majority of ublished simulation results in the arallel alication scheduling literature are obtained with analytical simulation models. In articular, simulation tools used routinely in the community use analytical, some of which have been validated against oerational models [2]. The key question we investigate in this work is whether relying on analytical simulation models leads to scientifically valid conclusions. This is a somewhat disturbing question to ask given the sheer number of ublished results that rely on such models. It is also a broad question that does not have a single answer since the answer deends on the alications and latforms at hand. Consequently, we focus on a relevant case study: scheduling algorithms for executing mixedarallel alications on clusters. Like most scientific workflows, mixedarallel alications can be reresented as task grahs, but these tasks are dataarallel comutations. By combining the taskarallelism offered by the workflow structure and dataarallelism within each task, mixed arallelism increases otential arallelism and can thus lead to higher scalability and erformance. Mixed arallelism can be imlemented in many scientific alications [3]. In recent years several algorithms have been roosed for scheduling mixedarallel alications on clusters [4], [5], [6], [7], and most of these algorithms have been evaluated via simulations. Several of these simulations were enabled by the SimGrid toolkit [8], a stateoftheart tool for erforming discrete event simulations of distributed arallel systems using analytical simulation models. There are several other toolkits for building discrete event simulator, e.g., GridSim [9]. However, it is not the objective of this work to comare several simulation toolkits for their simulation quality. As all simulators entail an abstraction error, we are rather interested in answering if simulation results in this domain of scheduling allow the scientist to transfer the solution into a real runtime environment. In this work, we comare SimGrid simulations with exeriments on a realworld cluster in the context of mixedarallel alication scheduling. By comaring simulations to exeriments, we can quantify the simulation error and, more imortantly, identify its root causes. We also investigate to which extent simulation models should cature alication
2 and latformsecific overheads in order to imrove accuracy so that a valid comarison of the scheduling algorithms is ossible. We refine our simulation model accordingly, first using a bruteforce aroach that measures all ossible overheads and later using emirical models that use interolation for estimating overheads. From these develoments we then infer guidelines for sound simulation ractices. This aer is organized as follows. Section II describes our case study. Section III resents our alication execution environment and Section IV resents our simulation environment. Section V comares simulations to exeriments. Section VI shows how simulation accuracy can be imroved so that simulations lead to meaningful results. Section VII gives a method for obtaining a simulation model that is less involved than that in Section VI, but still roduces reasonable results. Related work is discussed in Section VIII and overall conclusions are drawn in Section IX. II. CASE STUDY A. Problem Statement and Algorithms We target the scheduling of a mixedarallel alication onto a cluster with N identical comute nodes and a dedicated interconnect. The alication consists of tasks with data deendencies, reresented as a Directed Acyclic Grah (DAG). Each task is moldable, i.e., it can be executed with an arbitrary number of rocessors (within some bounds in ractice). An MPI dataarallel alication is an examle of a moldable task. An examle mixedarallel alication is multile such alications in a scientific workflow. Many algorithms to schedule mixedarallel alications on homogeneous clusters to minimize alication execution time, or makesan, have been roosed [5], [6], [7], [10], [11], [12], [13]. All these algorithms decomose the scheduling in two hases. In an allocation hase, they determine task allocations, i.e., the number of rocessors that should be used to execute each task. In a maing hase, they select the resective rocessor set on which a task is executed. Previously ublished results show that the Critical Path and Areabased scheduling (CPA) algorithm [7] has a low comutational comlexity and leads to good results when comared to its cometitors. Two extensions to CPA are used in this work: Heterogeneous CPA (HCPA) [12] and Modified CPA (MCPA) [5]. The original CPA algorithm roduces task allocations that can become too large, thereby degrading overall erformance. Both HCPA and MCPA remedy this roblem using different solutions and thus leading to different schedules. See [5], [12] for full details on these algorithms. B. Problem Instances A roblem instance for the scheduling consists of a latform and a mixedarallel alication. For all our exeriments we use a cluster, located at the University of Bayreuth, Germany, which comrises N = 32 nodes (each with two 2 GHz AMD Oteron 246) interconnected with a Gigabit Ethernet switch. We had dedicated access to this cluster and could install our own alication execution framework and scheduler. The vast Table I PARAMETERS USED FOR GENERATING RANDOM DAGS. arameter values number of tasks 10 number of inut matrices (DAG width) 2, 4, 8 ratio addition / multilication tasks 0.5, 0.75, 1.0 matrix size (# elements er dimension) 2,000, 3,000 number of samles 3 total DAG instances 54 majority of the roduction workflows today consist of nonmoldable, urely sequential tasks. It would be ossible to enhance such a workflow with mixedarallelism, admittedly at the exense of develoment time and effort, and use this workflow as the target alication for our case study. Instead, since our goal is to study simulation accuracy rather than obtaining novel results regarding scheduling algorithms themselves, we generate random DAGs, as described hereafter. In our random DAGs, tasks are either matrix additions or matrix multilications. Both these comutational kernels can be easily arallelized using standard arallel algorithms [14]. We use a vanilla 1D arallelization: if a n n matrix of size n is maed onto rocessors, each rocessor holds n/ columns. For n n square matrices, the time comlexity of the addition is O(n 2 ) and that of the multilication is O(n 3 ). We can thus easily generate alications with different comutation to communication ratios (CCRs) by varying the fraction of tasks that imlement matrix addition or multilication. we have imlemented matrix multilication and addition in Java using the MPIJava (v1.2.5) library, using the mich2 (v1.0.8) native communication library. Our DAG generator oerates as follows. First, it randomly icks the number of entry tasks between 1 and log 2 (v), where v denotes the number of inut matrices. So as to generate DAGs of different widths (i.e., otential task arallelism), v is a arameter that we set to 2, 4, or 8. Each entry tasks oerates on two matrices and roduces a new matrix as outut. The number of tasks in subsequent levels of the DAG is icked between one and the logarithm of the number of matrices so far, i.e., the inut matrices and those roduced by revious tasks. The DAG generation ends when a secified total number of tasks has been generated. We control the CCR by setting the ratio of matrix additions to matrix multilications (e.g., a ratio of 0.2 for 10 tasks leads to 2 additions and 8 multilications). We generate instances by setting this arameter to 0.5, 0.75, or 1.0. A fourth arameter secifies the size of the rocessed matrices and has an imact on the overall execution time. We consider n n square matrices of double recision elements with n = 2,000 and n = 3,000, for 30 MB and 68 MB of data er matrix. The scheduling algorithms do not consider memory constraints, and these sizes ensure that all data will fit into the main memory of a rocessor. For each instantiation of the above arameters we generate three samle DAGs, for a total of 54 DAGs. Table I gives a summary of the DAG generator arameters and their values.
3 III. EXECUTION FRAMEWORK We use the TGrid software for running exeriments on our cluster [15]. TGrid is Javabased because initially designed for heterogeneous latforms. TGrid is ideally suited to our case study because it suorts the execution of interdeendent multirocessor tasks, i.e., mixedarallel alications. TGrid consists of a runtime environment and of a develoment library. The library makes it ossible to imlement arallel tasks with MPIJava and rovides mechanisms for defining data deendencies between tasks. The runtime environment sawns and monitors tasks accordingly to a given schedule. We have imlemented a module on to of TGrid to execute a mixedarallel alication according to a given schedule that secifies task execution order and which rocessors to use for each task. In a mixedarallel alication, different arallel tasks may use different data distributions (e.g., 1D, 2D, blockcyclic). Data redistribution is thus often necessary when transferring outut data from a task to its deendents. Imlementing data redistribution is often difficult. Fortunately, TGrid rovides a comonent for transarent data redistribution. This comonent determines the source and target rocessors for each data element, determines the messages that have to be transferred, and erforms all necessary ointtooint data transfers. IV. SIMULATION FRAMEWORK Our simulator is based on SimGrid [8], a framework secifically designed for the evaluation of scheduling algorithms. The simulator takes as inut a latform secification, an alication secification and a scheduling algorithm, and oututs an alication execution trace. The latform secification for our 32node cluster is constructed as follows. Since our alication execution framework is Javabased and that our alication tasks are simle linear algebra kernels, we have quantified the comute seed of the cluster by benchmarking a matrix multilication running on the JVM on our cluster. Accordingly, the comute seed of each node is set to 250 MFlo/s. The interconnection network is reresented by four arameters: the bandwidths and latencies of the the cluster s switch and those of the rivate links connecting each node to the switch. Since our cluster uses a Gigabit Ethernet interconnect, these bandwidths are set to 1 Gb/s and the latencies to 100 µs. Note that SimGrid simulates contention between network communications that share a network link [2]. SimGrid rovides a model (called Ptask L07) to simulate the execution of arallel tasks. In this model, a userrovided array a describes the number of floating oint oerations that each rocessor has to execute and a userrovided matrix B describes the communication attern (i.e., the number of bytes exchanged between each air of rocessors). This model makes it ossible to simulate fully arallel tasks (a 0, B = 0), data redistribution tasks (a = 0, B 0), and arallel tasks with communication (a 0, B 0). We use this model to instantiate simulation models reresentative of those used in the scheduling literature, as exlained hereafter. 1) Modeling Task Execution Time: The traditional aroach is to rely on analytical models of alication execution times, which we can imlement in SimGrid. Recall that our imlementations of arallel tasks use 1D data distributions. Let n denote the dimension of the matrices, and the number of rocessors on which a arallel task is executed. For the arallel matrix multilication, each rocessor executes 2n 3 / floating oint oerations and sends n 2 / data elements er communication ste. The number of oerations to be erformed for a arallel matrix addition is n 2 /, and no communication is needed. We use these quantities to instantiate the comutation array and communication of the Ptask L07 arallel task model in SimGrid. Our initial exeriments showed that the time to erform matrix additions is negligible. The O(n 2 ) comlexity does imly a lower execution time than matrix multilication, and in ractice this time is so small that it has no effect on the overall schedules. For this reason we artificially increase the comlexity of matrix additions by reeating them several times. More recisely, each matrix addition is executed n/4 times, leading to a total of n 4 n2 oerations. Even after this adjustment, there is still a factor 8 between the number of floating oint oerations requires for matrix multilications and matrix additions. Consequently, our additions and multilication tasks still have significantly different CCRs. All results hereafter are obtained with this adjustment. 2) Modeling Data Redistribution Time: To model the data redistribution time between subsequent tasks, we create data distribution tasks defined in the Ptask L07 model by a communication matrix that contains the number of bytes exchanged by each rocessor air. Our use of 1D data distributions for all our arallel tasks allows us to determine exactly the content of this communication matrix by comuting the overlaing intervals between two matrix distributions. A. Methodology V. SIMULATIONS VS. EXPERIMENTS As described in Section IIB, we generate 54 random alication DAGs of matrix addition and multilication arallel tasks. Each such DAG is assed to our simulator, along with a scheduling algorithm and the latform secification. The simulator oututs the comuted schedule and the (simulated) alication makesan. The comuted schedule secifies the order in which the tasks must be executed as well as the rocessors used for each task. We then execute the alication on the hysical cluster following this schedule, and measure the achieved makesan. We can then comare the simulated makesan to the exerimental makesan. We resent results of this comarison for all random DAGS, both for the HCPA and MCPA scheduling algorithms, and attemt to answer the question: can simulation results be used to redict real trends regarding which algorithm is referable? B. Results Figure 1 shows results for all DAGs with inut matrices of size n = 2,000. Each oint on the xaxis corresonds to one secific DAG. The yaxis shows the makesan achieved by
4 HCPA relative to that achieved by MCPA, in ercentage (a negative value means that HCPA leads to a shorter makesan than MCPA). For each DAG, two bars are shown. The black bar is for simulation results and the white bar is for exerimental results. We sorted the DAGs on the xaxis by increasing relative simulated makesan to make the figure easier to read. rel. makesan of HCPA Exeriment DAGs (sorted by rel. makesan from simulation) Figure 1. HPCA makesan relative to that of MCPA using analytical models (n = 2,000). results differ from exerimental results significantly, to the oint that simulation results simly cannot be used to redict the relative erformance of the two scheduling algorithms. For 16 out of the 27 DAGs shown in the figure, or 60%, relying on simulations to comare HCPA and MCPA lead to a result that is the oosite of the exerimental result. For larger matrices, n =3,000, simulations lead to erroneous comarisons between HCPA and MCPA in 7 out of the 27 DAGs, or 26% (results not included here). We conclude that our simulator simly does not roduce meaningful results. C. Analysis When analyzing the simulated and realworld schedules, we found that some tasks have negligible execution time in simulations but not in exeriments, meaning that simulated execution times are often grossly underestimated. Furthermore, data redistributions take significantly longer in exeriments. We have isolated three causes for these discreancies: a) Task execution time: Our task imlementations do not use highlyotimized kernels, e.g., assembly or finely tuned BLAS imlementations. This contributes to less redictable/ modelable task execution time, while otimized BLAS libraries often erform close to eak erformance. Our Java code is often far from eak erformance and turns out to be sensitive to number of rocessors and the size of the matrices, which is not cature by the simulated analytical models. b) Task startu overhead: Starting a task in TGrid is exensive as it entails starting a Java Virtual Machine (JVM) on each rocessor via SSH. This overhead is not catured in the simulated analytical models. c) Data redistribution overhead: In TGrid, for a rocess to communicate with a rocess from a different context (i.e., another MPI task) it must first register to a local subnet manager comonent and then retrieve information regarding other rocesses from that comonent. There is a single subnet manager and the time to retrieve information increases with the number of rocesses assigned to a task. Again, this overhead is not catured by the simulated analytical models. The three culrits above ertain to our execution environment. Another environment would ossibly obviate some of these factors or reveal new ones. Regardless, the overall message here is that, unsurrisingly, to be meaningful a simulator must account for secifics of the environment. Yet, simulators used in the scheduling literature are tyically generic and can thus easily lead researchers to draw erroneous conclusions. VI. REFINING THE SIMULATION MODELS A. Task Execution Times Based on Profiles One of the reasons why our simulator leads to inaccurate results is because it uses flawed analytical models of (arallel) task execution times. The grah on the lefthand side of Figure 2 shows the relative error of the analytical erformance model for matrix multilication when comared to our Java imlementation for n = 2,000 and n = 3,000. The xaxis shows the number of rocessors allocated to the task. We see that the error fluctuates without clear atterns u to 60%. One may argue that this negative result is due to our use of Java. To counter this argument, we have also conducted exeriments with PDGEMM, a arallel matrix multilication imlementation from the LibSci scientific library. The grah on the righthand side of Figure 2 shows similar results obtained on a Cray XT4 (Franklin, LBNL) for three matrix sizes (1024, 2048, and 4096). The analytical model is 2n3, where F LOP S is the flo rate measured on the machine, which is at MFLOPS. The average rediction error oscillates at about 10% and goes u to 20%. We conclude that, even for a task as simle as arallel matrix multilication, task execution time is not easily catured by a simle analytical model. Instead, one should use black box simulation models that are built directly from exerimental measurements. Accordingly, we modify our simulator using a bruteforce aroach by which we simly rofile each task on our cluster for all ossible allocations ( = 1,..., 32) and matrix sizes (n = 2,000, 3,000). The simulator can then simulate task execution times by looking u a table of rofiled execution times. B. Task Startu Overhead 1 F LOP S The TGrid framework sawns an MPI rocess by connecting to remote hosts via SSH, starting on that host a JVM and a task container, which then registers with the local TGrid server. This server sends byte code to the container, which executes it. We measured the task startu overhead for arallel tasks as the execution time of an alication that consists of = 1,..., 32 noo rocesses. Since the byte code of a real rocess would be larger than that for a noo rocess, our measured task startu overheads are underestimations. Figure 3 shows the startu overhead for = 1,..., 32, averaged over 20 trials. Surrisingly, the average startu time
5 relative error D MM/Java relative error PDGEMM/C Figure 2. Relative runtime rediction errors when relying on an analytical model: 1D matrix multilication in Java (on the lefthand side) and PDGEMM matrix multilication in C (on the righthand side). time [ms] (source) (dest) Figure 4. Data redistribution overhead. (src): number of source rocessors. (dst): number of destination rocessors startu overhead [s] number of rocessors Figure 3. Task startu overhead for allocations from = 1 to = 32. is not monotonically increasing with the number of rocessors. The simulator simulates task startu overhead by looking u a table that records these average values. C. Redistribution Overhead In TGrid, source and destination tasks do not know each other rior to the start of the redistribution. As exlained earlier, a subnet manager comonent is used to establish communication between all relevant tasks, which causes overhead not catured in the original analytical model. We measure this overhead as the time to erform a data redistribution for a mostly emty matrix so that the overall data transfer times are negligible, but designed so that each rocessor must send at least one byte of data. This ensures that the maximum number of rotocol messages will be transferred. Figure 4 shows the overhead versus the number of sending ((src)) and receiving ((dst)) rocesses, averaged over 3 trials. We see that the overhead deends mostly on (dst). We thus comute this overhead for a given (dst) value averaged over all (src) values. The simulator simulates data redistribution overhead by looking u a table that records these average values. D. Exerimental Evaluation We ran all simulations for the generated DAGs with a new simulator that uses the refinements to the simulation model described in the revious three sections. The simulator now uses task rofiles for simulating task execution times, with an added startu overhead deending on the size of the allocation. The time for redistributing data is still based on the SimGrid simulation, but an extra redistribution overhead is added. Results are resented in Figure 5, which is similar to Figure 1. The chart on the lefthand side if for all DAGs with n = 2,000 increasing relative makesans obtained in simulation. The simulation outcome (i.e., whether HCPA outerforms MCPA or not) is erroneous for only two cases, and in these cases the difference between the relative makesans is well bellow 10%. For results for n = 3,000, shown on the righthand side, the simulation outcome is erroneous in only three cases. The simulation and exerimental results are in general agreement and haen to show that HCPA roduces shorter schedules than MCPA for n = 2,000, while no algorithm is a clear winner for n = 3,000. We conclude that the refined simulator makes it ossible to draw scientifically sound conclusions regarding the relative erformance of the scheduling algorithms. VII. DERIVING AN EMPIRICAL MODEL The downside of our refined simulator is that it relies on extensive (and thus timeconsuming) measurements of the target latform. The measurements of the task startu overheads and of the data redistribution overheads are indeendent on the nature of the tasks. They can thus be obtained once and for all on a given latform (and ossibly scaled to simulate hyothetical target latforms). Unfortunately the measurements of task execution times are alication and roblem size secific. Even if, as in our case, only two comutation kernels and two roblem sizes are used, obtaining the comlete rofiles for all numbers of rocessors is time consuming, exensive, and ultimately not ractical. In this section we attemt to derive emirical models of task execution times and of the overheads that (i) rely only on a few measurements for construction a regression; and (ii) lead to simulation results accurate enough that they can be used to draw valid conclusions.
6 rel. makesan of HCPA Exeriment rel. makesan of HCPA Exeriment DAGs (sorted by relative makesan from simulation) DAGs (sorted by relative makesan from simulation) Figure 5. HPCA makesan relative to that of MCPA using full rofiles. Lefthand side: n = 2,000. Righthand side: n = 3,000. time [s] time [s] n=2000 n=3000 Figure 6. Fitting Results. Left: Regression model with outliers at = 8 and = 16 and n = 3,000. Right: Final regression model without outliers for n = 2,000 and n = 3,000. A. Regression Models Our goal is to obtain an regressive models of task execution times, both to matrix multilication and addition, given the number of rocessors. We initially based our regression on measured execution times for = {1, 2, 4, 8, 16, 32} because such owersoftwo values are commonly used in erformance analysis. A single regression model does not suffice because overhead start dominating task execution times when 16. Consequently, we use two models: a nonlinear a 1/ + b model for 16, and a linear a + b model for > 16. Unfortunately, even with these two models, the fit to exerimental data is of oor quality. Consider the results shown on the lefthand side of Figure 6 for matrix multilication and n = 3,000. The oor quality of the fit comes from unexected outliers at = 8 and = 16. For these numbers of rocessors, our Java imlementation suffers from abnormally long execution times. For = 8, although the arallel execution is loadbalanced, the comutation of the local matrix udates for the multilication are simly slower. We have observed this henomenon reeatedly for many trials. We conclude that the likely causes are memory hierarchy effects, which are notoriously difficult to model, and esecially with the JVM. For = 16, the comutation is not well balanced. This is due to our vanilla imlementation of the 1D matrix multilication, which leads to noticeable load imbalance for n = 3000 and = 16 rocessors (the last rocessor is simly allocated two many matrix rows/columns). This outlier is thus less interesting and would not be resent with a better matrix multilication imlementation. Both these outliers highlight the difficulty of deriving highquality models of arallel task execution times in ractice. To sideste the outlier roblem for now, we have used different data oints for building our regression model (relacing 8 and 16 by 7 and 15). In ractice, one could address this roblem by obtaining a larger number of measurements for the regression, and/or ossibly identify outliers, still without requiring a full rofile. The modified fit is shown on the righthand side of Figure 6 both for n = 2,000 and n = 3,000. This regressive model is based on only 6 measurements as oosed to 32 measurements for the simulator in Section VI. For larger clusters one would likely need to erform more measurements in order to derive a robust model. A similar regression analysis for HPC alications on large clusters has shown that 15 to 20 measurements are needed to get robust fits [16]. Note also that for ractical uses one would have to include the matrix size into the model as an indeendent variable, which we did not do in this case study. Straightforward regressions can be used to model matrix additions (a single a 1/ + b model), task startu overheads, and redistribution overheads. Table II rovides a summary of all these emirical models. B. Exerimental Evaluation Results with our emirical simulation models are shown in Figure 7. results match well with exerimental ones, esecially for n = 2,000 in which case errors on task execution time have less imact on the overall makesan. For n = 2,000, the simulation result regarding which of HCPA or MCPA achieves the lowest makesan is incorrect in only one case out of 27. For n = 3,000, the conclusion is incorrect in 6 cases. This is twice as many incorrect results as when using the bruteforce simulation aroach in Section VI, but still well below the 60% error rate with the urely analytical simulation models in Section IV. When analyzing schedules
7 Table II REGRESSION MODELS (INCLUDING VALUES FOR INSTANTIATION AND REGRESSION COEFFICIENTS). time to model functions regression values execution time (multilication) = {2, 4, 7, 15} = {15, 24, 31} 1 n = 2000 a 2 + b c + d (a, b, c, d) = (239.44, 3.43, 0.08, 1.93) n = 3000 a 1 + b c + d (a, b, c, d) = (537.91, 25.55, 0.09, 11.47) execution time (addition) = {2, 4, 7, 15, 24, 31} n = 2000 a 1 + b (a, b) = (22.99, 0.03) n = 3000 a 1 + b (a, b) = (73.59, 0.38) = {1, 16, 32} redistribution startu a + b (a, b) = (7.88, ) task startu time a + b (a, b) = (0.03, 0.65) rel. makesan of HCPA Exeriment rel. makesan of HCPA Exeriment DAGs (sorted by relative makesan from simulation) DAGs (sorted by relative makesan from simulation) Figure 7. HPCA makesan relative to that of MCPA using emirical models. Lefthand side: n = 2,000. Righthand side: n = 3,000. error [%] analytic rofile emirical error [%] analytic rofile emirical than the two other versions by orders of magnitude, while the emirical version rovides a reasonable alternative to the rofilebased version. We conclude that the emirical models rovide a good tradeoff between the time and effort needed to instantiate them and the rovided simulation accuracy. In the scoe of our case study, simulation based on these models is accurate enough to draw valid conclusions regarding the HCPA and MCPA algorithms. Such conclusions would simly not have been achievable with urely analytical models. Figure 8. Makesan simulation error for the three different simulation models. Lefthand side: HCPA results. Righthand side: MCPA results. for n = 3,000, we found that the largest discreancies seen on the righthand side of Figure 7 are for cases in which a scheduling algorithms allocates = 16 rocessors to several tasks. For this number of rocessors, recall that we have found our imlementation of matrix multilication to lead to unexectedly oor erformance (the outliers in Figure 6). Our regressive task execution time model in this case is a oor fit to exerimental results, which exlains the discreancies. To rovide a global view of simulation errors for the three versions of our simulator (analytical, rofilebased, and emirical), Figure 8 shows error statistics over all exeriments for both each scheduling algorithm in a boxandwhisker fashion. The urely analytical version leads to errors larger VIII. RELATED WORK There is a large literature on simulation techniques and models for (redicting the erformance of) arallel alications. Not surrisingly, many authors have roosed simulators for MPI alications ranging from analytical simulators (e.g., [17]) to oerational simulators (e.g., [18]). An overarching conclusion from such works is that reasonable simulation accuracy requires extensive and ossibly alicationsecific measurements on a reresentative realworld execution environment. Yet, most ublished results in the scheduling literature are obtained with urely analytical simulation models. In this work we used a case study to quantify how such models should be evolved to achieve scientifically sound results. We have roosed emirical models that rely on simle regressions, which has also been done by other authors, and recently in [16]. Beyond those authors that have roosed a
8 simulator, some have erformed studies to comare simulations to exeriments. For instance, the work in [19], which also uses SimGrid, comares simulations and exeriments for a heat roagation alication. However, the imact of the execution environment on the simulation errors is not studied. IX. CONCLUSIONS is an attractive roosition for studying the erformance of arallel alications, and articular oular for studying scheduling algorithms. However, since simulation errors exist and are tyically not quantified, it is not clear whether simulation results lead to scientifically valid conclusions. In a case study, we have comared two stateoftheart algorithms for scheduling mixedarallel alications, using both simulations and realworld exeriments. We have created three versions of our simulator, which are all built on to of the SimGrid framework. Thus, simulators and case study are bound to the features rovided by SimGrid. The first simulator relies on urely analytical models such as those commonly used in the scheduling literature. We found that this simulator simly does not roduce scientifically meaningful results. Root causes of simulation errors were identified and boil down to a need to account for secifics of the target execution environment. We thus develoed a second version of the simulator based on a bruteforce aroach: instantiate the simulation based on extensive and ossibly alication secific rofiles measured on the execution environment. This simulator roduces dramatically better accuracy (under 10% error on average) and can be used to draw valid conclusions regarding the two scheduling algorithms. Since obtaining full rofiles may be imossible in ractice, we have develoed a third version of our simulator based on emirical (regressionbased) models built from sarse rofiles. This simulator rovides a good comromise between the first two versions, leading to results dramatically better than those of the first version while still enabling meaningful scientific conclusions. Although the vast majority of ublished results in the scheduling literature are obtained with analytical simulation models, our findings should rovide a strong recommendation for the use of emirical simulation models that are connected to the target execution environment. Alternately, these models could be instantiated for an existing execution environment and scaled to simulate an hyothetical execution environment. In our case study, idiosyncrasies of our alication s imlementation made it difficult to build valid emirical models due to outliers. Arguably, our use of Java and of a vanilla alication imlementations was the root cause for these outliers, and our results could be imroved with better imlementations. Indeed, others have successfully alied regression techniques for arallel alication erformance modeling [16]. Nevertheless, we exect that many relevant realworld alications and latforms may suffer from outlier roblems and that deriving reasonable emirical models from sarse erformance rofiles is challenging. Addressing this challenge is, however, necessary for running valid simulations without resorting to costly comlete erformance rofiles. ACKNOWLEDGMENTS We would like to thank the University of Bayreuth and Lawrence Berkeley Labs for roviding access to their machines. This work is artially suorted by the ANR roject USS SimGrid (08ANRSEGI022). REFERENCES [1] J. Gustedt, E. Jeannot, and M. Quinson, Exerimental Methodologies for LargeScale Systems: A Survey, Parallel Processing Letters, vol. 19, no. 3, , Se [2] P. Velho and A. Legrand, Accuracy Study and Imrovement of Network in the SimGrid Framework, in 2nd Int. Conf. on Tools and Techniques (SimuTools), Rome, Italy, March [3] S. Chakrabarti, J. Demmel, and K. Yelick, Modeling the Benefits of Mixed Data and Task Parallelism, in Symosium on Parallel Algorithms and Architectures (SPAA 95), 1995, [4] N. Vydyanathan, S. Krishnamoorthy, G. Sabin, U. Catalyurek, T. Kurc, P. Sadayaan, and J. Saltz, An Integrated Aroach for Processor Allocation and Scheduling of MixedParallel Alications, in Proc. of the 35th Int. Conf. on Parallel Processing, Aug. 2006, [5] S. Bansal, P. Kumar, and K. Singh, An Imroved TwoSte Algorithm for Task and Data Parallel Scheduling in Distributed Memory Machines, Parallel Comuting, vol. 32, no. 10, , [6] S. Hunold, T. Rauber, and F. Suter, Redistribution Aware TwoSte Scheduling for MixedParallel Alications, in Proc. of the IEEE Int. Conf. on Cluster Comuting, Tsukuba, Jaan, Se. 2008, [7] A. Radulescu and A. van Gemund, A LowCost Aroach towards Mixed Task and Data Parallel Scheduling, in Proc. of the 15th Int. Conf. on Parallel Processing, Valencia, Sain, Se [8] H. Casanova, A. Legrand, and M. Quinson, SimGrid: a Generic Framework for LargeScale Distributed Exeriments, in Proc. of the 10th IEEE Int. Conf. on Comuter Modeling and, Mar [9] R. Buyya and M. Murshed, GridSim: A Toolkit for the Modeling and of Distributed Resource Management and Scheduling for Grid Comuting, Concurrency and Comutation: Practice and Exerience, vol. 14, no. 13, , [10] R. Leère, D. Trystram, and G. Woeginger, Aroximation Algorithms for Scheduling Malleable Tasks Under Precedence Constraints, Int. J. on Foundations of Comuter Science, vol. 13, no. 4, , [11] J. Turek, J. Wolf, and P. Yu, Aroximate Algorithms for Scheduling Parallelizable Tasks, in 4th Annual ACM Symosium on Parallel Algorithms and Architectures, San Diego, CA, Jun. 1992, [12] T. N také, F. Suter, and H. Casanova, A Comarison of Scheduling Aroaches for MixedParallel Alications on Heterogeneous Platforms, in Proc of the 6th Int. Sym. on Parallel and Distributed Comuting, Hagenberg, Austria, Jul [13] S. Hunold, LowCost Tuning of TwoSte Algorithms for Scheduling MixedParallel Alications onto Homogeneous Clusters, in Proc. of the 10th Int. Sym. on Cluster, Cloud and Grid Comuting, [14] H. Casanova, A. Legrand, and Y. Robert, Parallel Algorithms. Chaman and Hall, [15] S. Hunold, T. Rauber, and G. Rünger, TGrid Grid Runtime Suort for Hierarchically Structured Taskarallel Programs, in Proc. of the 5th Int. Worksho on Algorithms, Models and Tools for Parallel Comuting on Heterogeneous Networks (Heteroar 06), [16] W. Pfeiffer and N. Wright, Modeling and Predicting Alication Performance on Parallel Comuters Using HPC Challenge Benchmarks, in Proc. of the Int. Parallel and Dist. Processing Sym., 2008, [17] T. Hoefler, C. Siebert, and A. Lumsdaine, LogGOPSim  Simulating LargeScale Alications in the LogGOPS Model, in Proc. of the ACM Worksho on LargeScale System and Alication Performance, [18] E. León, R. Riesen, and A. Maccabe, InstructionLevel of a Cluster at Scale, in Proc. of the Int. Conf. for High Performance Comuting and Communications, Nov [19] A. Guermouche and H. Renard, A First Ste to the Evaluation of SimGrid in the Context of a Comlex Alication, in Proc. of 19th Int. Heterogeneity in Comuting Worksho, 2010.
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