A Visualization System and Monitoring Tool to Measure Concurrency in MPICH Programs

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1 A Visualization System and Monitoring Tool to Measure Concurrency in MPICH Programs Michael Scherger Department of Computer Science Texas Christian University Zakir Hussain Syed Department of Computing Sciences Texas A&M University - Corpus Christi Abstract Distributed algorithms implemented using MPI are often concerned with time and message complexities. These measures mainly address the quantitative aspect of the distributed programs. The qualitative aspect, which is the performance of the distributed program, can be ascertained only by measuring the concurrency of the distributed program. Raynal, in 1992, developed a method of computing concurrency of distributed computations based on the volumetric abstractions of cones and cylinders. Both cone and cylinder abstractions make use of the values: weight, volume and height. With the help of these 3 values, the measure of concurrency can be calculated for both cone and cylinder abstraction. In this research, a visualization system was developed that measures and analyzes the concurrency of a distributed program by making use MPICH event clog2 event trace files and the above cone and cylinder abstractions. This paper will discuss the development of this visualization system and the accuracy and efficiency of the visualization tool. Keywords: Concurrency measurements, distributed systems, software visualization tools, MPI, vector clocks. 1. Introduction In distributed algorithms, time and message complexities are the two measures that are used to identify the efficiency of a distributed program. However, these two measurements are quantitative and do not answer address any concerns regarding the quality of the distributed program, or amount of concurrency in a program. Without a measure to calculate the quality of the distributed program, it is not certain to know if the execution is well distributed and if the execution will have delays due to synchronization constraints [8]. These questions can be answered by finding the amount of concurrency in the distributed computation. The underlying theoretical approach, used in this research and visualization system, is quoted in [8]. According to this approach, the degree of concurrency can be calculated by quantifying the synchronization delay. Synchronization delay is a delay that might exist between two successive events in the same process. In order to measure the concurrency, this research makes use of two abstractions, a cone and a cylinder. These geometric shapes are used to quantify the synchronization delay and to find the concurrency measure. The cone abstraction is associated with individual events, whereas the cylinder abstraction is associated with the whole program computation. Three values are associated with both the abstractions: weight, volume and height. Cone and cylinder abstraction will be discussed further in section 2. The design of the visualization system that automates the calculation of concurrency by making use of the said approach will be discussed in section 3. The evaluation of the visualization system is tested on a sample program as is discussed in section 4. Section 5 will present future work and conclusions. 2. Background and Related Research To implement the concept to causality relation in distributed system, Lamport proposed logical clocks in 1978 [6]. He proposed the concept of causality relation (also known as happen before relation) denoted by which described the causal ordering of events. In [5] and [7], Fidge and Mattern independently proposed vector clocks that overcame the limitation of logical clocks. A vector clock of a system of N processes is a vector of N logical clocks, one clock per process. In vector clocks, a local copy of the global clock array is kept in each process. However, a vector clock only provides the timestamp of the event. The use of these timestamp values to find the interval between 2 events but the concurrency of the distributed computation cannot be known from the vector clock elements Concurrency Measures While calculating a concurrency measure, it is assumed that the message passing is instantaneous. It

2 is also assumed that each event consumes approximately the same amount of computing time. This computing time is known as time unit. Figure 2: Cone abstraction on time-event diagram. Figure 1: Sample time-event diagram. The time-event diagram shown in Figure 1 has 3 processes and each process contains some events. Vector timestamps are shown on each event. The delay between two events is known as synchronization delay. The purpose of concurrency measure is to effectively measure the number of synchronization delay. This idea was first introduced in [3] Cone and cylinder abstractions In order to effectively measure synchronization delay, cone and cylinder abstractions were introduced. Cone abstraction deals with the concurrency measure of the individual event. Cylinder abstraction deals with the concurrency measure of the entire computation. From [8], with cone and cylinder abstractions, three values are associated: volume, weight and height. Weight, denoted by wt, in a cone abstraction is the exact number of events that causally precede an event e. Weight in a cylinder abstraction is the number of events that are produced in the total computation. Volume, denoted by vol, in a cone abstraction is the maximum number of events that could possibly precede an event e. Volume in a cylinder abstraction is the maximum number of events that can be produced in the entire computation. Height, denoted by ht, in a cone abstraction is the number of events that could come before the event e but it has to be on the longest causal path ending with event e. Height in a cylinder abstraction is the largest logical time associated with an event e. Note: a similar analysis can be constructed for a cylinder abstraction on a time-event diagram Calculating Concurrency From [8], the concurrency measure is calculated for cone and cylinder abstraction using the following formulas, For cone abstraction:!!"#!"#!!"(!"#(!))!! =!!"#!"#!!"(!"#(!)) For cylinder abstraction:!"#!"#!!"(!"#(!))!!! =!"#!"#!!"(!"#! ) In the above equations the numerator denote the total number of synchronization delay which have actually occurred and the denominator denotes the maximum number of synchronization delay which could have possibly occurred. Once the value of α e (e) and α (C) are calculated, it is subtracted from 1 in order to get the concurrency measure. The reason it has to be subtracted from 1 is because as it is seen in both [3] and [4], when α = 1, the computation is said to be maximally concurrent and when α = 0, the computation is said to be sequential. Hence by subtracting by 1, the concurrency measure calculated in [8] can be made compatible with [3] and [4]. 3. System Design The input to the visualization system is a clog2 trace file that is generated by the program written using MPICH2. The clog2 is a default log format of Multi-Processing Environment (MPE), which is a suite of performance analysis tools for programs written for MPI. The visualization system was written solely in Java and supporting tools. The

3 concurrency measure database was created using a MySQL database and the bridge between Java and MySQL was made with the help of Hibernate. Thirdparty Java libraries such as JFreeChart were used to display graphs and Jssh was used to establish connection with the Linux server. As shown in Figure 3, the user would initiate the application by giving authorization to make a connection to the Linux server by entering the hostname, username and password. Figure 3: Concurrency visualization system design. Once the connection is established with the server, Figure 3 shows the GUI that is displayed prompting the user to enter the name of the MPI program, number of processes and a command line argument, which is optional. The application can access the MPI program file on the server written in C/C++ only when it is stored in a folder called Project and has a makefile. The application then executes the MPI program on the server multiple times and each time the MPI program executes, the clog2 file will be converted from binary to text format using the clog2_print UNIX command and then the contents of clog2 file will be written and stored in a text file on the client machine. With the help of these clog2 files, the visualization system can measure the concurrency of the distributed computation and it stores the concurrency measures in the concurrency measure database. When all the concurrency measure values have been calculated, the output GUI is displayed. From the output GUI, the user can select a graph to view. Figure 4: System input GUI screen. Once the generate button is pressed, the user s input would be used to generate several script files to control the execution of the program. The program will then generate a series of clog2 files which are then parsed and imported into a database for further analysis. One of the challenges of this research was to determine the structure of the clog2 file generated by the MPE environment when an MPI program is executed. The original clog2 file is passed through this parser to find the size of the communicator and the number assigned to the starting and ending event of the internal event. Scanning each line of the clog2 file searching for some specific words does this. The first word is max_comm_word_size, the variable assigned to this word will be the size of the communicator. The size of the communicator is extracted and stored in the variable size. The words s_et and e_et corresponds to start event and end event respectively and the number assigned to both start and end event are extracted, as shown in Figure 5. Once the have the size, start event and end event are determined, the document can be searched for only the required information. The parser scans each sentence, searching for the occurrence of send, recv (start event, and end event). The lines that contain these words will be written to another text file. This text file will be named as the name of the program entered by the user in the input GUI followed by _required info. The newly created text file will be used by the application to measure the concurrency of the distributed computation. The text file created in the previous section will be passed through this parser, which is known as InfoParser. This parser would first scan the text file line by line determining which event is occurring on each line. If the event is a send event then the Send Event method is called which will update the vector clock of the sender process. It first checks if the clock has been initialized, if the clock has already been initialized it will call the Update Clock method otherwise it will call the New Clock method.

4 Similarly the vector clock of the receiver process is updated if the type of the event is internal event. In the case of the receive event, the receiver process will be updated similarly to send and internal event, however it has an additional method call Receiver Update. Figure 5: Sample clog2 file. The parser will create two new vector clocks for each event in the distributed computation. The first vector clock is known as LocalClock, this vector clock is like any other vector clock. However, the second vector clock, which is known as WeightClock, is a special vector clock, as it keeps tracks of the synchronization delays between the events. Summaries of methods created for this visualization system are described here. Method Line separator: When a line is read from the text file, the parser will determine if the event is a send, receive or internal event by looking at the type of the message and call the appropriate subordinate method. Method Internal event: If the event is an internal event then it will either call the New Clock method (first call) or the Update Clock method. Method Send event: If the event is a send event then it will either call the New Clock method (first call) or the Update Clock method. Method Receive event: If the event is a receive event then it will either call the New Clock method (first call) or the Update Clock method. After updating the vector clock of the receiver process, receive event will call Receiver update to update its value by comparing with the vector clock of the corresponding send event. Method Receiver update: Receiver update is called to update the vector clock of the receiver process by comparing it with the vector clock of the sender process. In the case of multiple send events as seen in send event, receiver update will first retrieve the corresponding vector clock of the send event from the array list and then each position of the vector clock in receiver process is compared with the corresponding position in the vector clock of the sender process. The maximum of the two values is then updated in the vector clock of the receiver process. The above procedure only works when a corresponding send event has already occurred before this receive event. In some cases, it was noticed that the clog2 text file had receive events occurring before send events. Simulating a send event solved this problem. For example, if receive event occurred before the send event at the very start of the clog2 file then the vector clock of the receive event will be updated vector clock with 1 in the i th position, where i is the rank of the sender process and keeping the remaining elements as zero. If the receive event occurred before the send event in the middle of the clog2 file, then the vector clock of the receive event will be updated with the latest vector clock of the sender process where the i th position of the vector clock has been incremented by 1 to simulate the occurrence of send event. Method New clock: New clock is called to initialize the vector clock. It initializes the vector clock by first creating a list. Method Update clock: Update clock is called to update the vector clock. It updates the vector clock of the event by first retrieving the latest vector clock of that process i and then it increments the i th position of the vector clock. Method ConcCal: This method calculates the measure of concurrency. This method takes the weight, volume and height as arguments. After receiving the values, it computes in the formula to calculate the measure of concurrency [8]. Method Cylinder abstraction: After all the values have been calculated for LocalClock and WeightClock, the method CylinderCal is called. This method first takes the i th position of each process i and stores it in an array. It then adds the elements of the array to find the weight of the cylinder. The height of the cylinder is calculated by taking the WeightClock of the last event of the distributed computation and finding the maximum value among them. Volume is calculated by simply multiplying the height with the number of processes. Once the value is known for weight, volume and height, the concurrency can be easily calculated by sending the 3 values as arguments to ConcCal method, which after calculating the concurrency of the cylinder returns it back to CylinderCal. Method Cone abstraction: Since the cone abstraction is associated with individual events, its concurrency must be calculated after completion of each event. This can be achieved by having 3 methods for weight, volume and height.

5 Once the value of weight, volume and height is known for each event, the method ConeCal is called. This method will remove the first element from the lists Weight, Volume and Height and then it sends the 3 values as arguments to the method ConcCal, which after calculating concurrency returns it backs to ConeCal. This is repeated until the 3 lists are empty and the measure of concurrency for each event in the distributed computation. The output GUI consists of 6 buttons, 5 of the 6 buttons corresponds to a type of graph and the 6 th is the exit button, as shown in Figure 6. graph is a collection of smaller graphs stacked one over the other, where each graph corresponds to a process as shown in Figure 11. Figure 8: Sample graph of % concurrency vs. number of events. Figure 6: Output graph selection GUI screen. The 1 st button when clicked would display the graph plotted with percentage of concurrency measure on the y-axis and the number of processes on the x-axis as shown in Figure 7. The 2 nd button when clicked would display the graph plotted with percentage of concurrency measure on the y-axis and the number of events on the x-axis as shown in Figure 8. Figure 9: Sample graph of % concurrency vs. time (individual process). Figure 7: Sample graph of % concurrency vs. number of processes. The 3 rd button when clicked would display the graph plotted with percentage of concurrency measure on the y-axis and time (in milliseconds) on the x-axis as shown in Figure 9. The 4 th button when clicked would display the graph plotted with percentage of concurrency measure on the y-axis and time (in milliseconds) on the x-axis, however the concurrency measure of each process is displayed separately as shown in Figure 10. The 5 th button when clicked would display the graph plotted with percentage of concurrency measure on the y-axis and time (in milliseconds) on the x-axis, however this Figure 10: Sample graph of % concurrency vs. time (combined processes). Figure 11: Sample graph of % concurrency vs. time (stacked processes).

6 4. Experimental Results The visualization system was tested on several benchmark and sample student-programming assignments. (Author s note: Due to page length limitations only one such program is discussed.- MCS) This section provides the testing and demonstration of a distributed algorithm for leader election using a ring of processes. In this program there are two functions, manager and worker. The manager is responsible for assigning a unique identifier to each process and then sending a message to process 1 to initialize the election algorithm. The worker function is responsible for electing the leader, once the election initialization message is received from the manager, it will start the election algorithm. In the election algorithm it performs 3 checks, it checks if the received identifier is less than, greater than or equal to itself. If the received identifier is less than its own identifier, then it will forward its own identifier to its neighbor. If the received identifier is greater than its own identifier than it will simply forward the received identifier to its neighbor and finally if the received identifier is equal to its own identifier then it means that it has the highest identifier and it will elect itself the leader and forward an elected message to its neighbor. The elected message goes round the ring until it reaches back to the process which was elected the leader and the program terminates. Figure 12 shows the graph for the percentage of concurrency with respect to the number of processes. It can be seen from the graph that the change in number of process will result in change in concurrency of the distributed computation. program and finding the concurrency, so in some of the cases the number of events can be same for two or more programs. Figure 13: Leader election % concurrency vs. number of events (single process). Figure 14 shows the graph for the percentage of concurrency with respect to time in milliseconds. It can be seen from the graph that the concurrency of this program oscillates between 0 and 1 in the beginning and then it slowly reduces with time. Figure 14: Leader election % concurrency vs. time (single process). Figure 15 shows the graph for the percentage of concurrency with respect to time in milliseconds. It is interesting to see in the graph that one process 0 had concurrency of 100% while the concurrency of other processes was in the range of 0 to 10%. Figure 12: Leader election % concurrency vs. number of processes. Figure 13 shows the graph for the percentage of concurrency with respect to the number of events. It can be seen from the graph that the change in number of events will result in change in concurrency of the distributed computation. Since this program does not take any command line arguments, the graph would be plotted by counting the events occurring in the

7 Figure 15: Leader election % concurrency vs. number of events (all processes). Figure 16 shows the graph for the percentage of concurrency with respect to time in milliseconds. It can be seen from the graph that the concurrency of process 0 was 100% and the remaining processes had maximum concurrency of 10%. scan the clog2 file and find any inconsistencies and try to rectify them. It is known that sometimes the test programs can be large and may take a lot of time to measure the concurrency. In the future, having a proper database to store the concurrency values of each program can solve this problem. The GUI could be modified where the user can select the name of the program that has already run through the tool and the tool will just load the values from the database and display them instead of calculating them again. References 1. D.P. Bertsekaa and J.N. Tsitsiklis., Parallel and Distributed Computation, Numerical Methods. Prentice-Hall, Inc., Chandy, K.M and Lamport, L., Distributed Snapshots: Determining Global States of Distributed Systems, ACM Transactions on Computer Systems, Vol. 3, No. 1, February 1985, Pages Charron-Bost, B. Measure of Parallelism of Distributed Computations, In B. Monien, editor, Proceedings of the 6th Annual Symposium ontheoretical Aspects of Computer Science (STACS 89), pages , Figure 16: Leader election % concurrency vs. time (stacked processes). 5. Future Work and Conclusions In this research, a visualization system to measure the concurrency of a distributed computation is implemented. The visualization system is based on the approach proposed in [8]. The proposed visualization system is designed to be very user friendly. The user can enter the input values in the user interface and the tool does all the necessary calculations to compute and visualize the concurrency of a program. This tool was able to accurately measure the concurrency for both cylinder and cone abstraction. By looking at the graphs generated by this tool, we were able to easily differentiate between a sequential program and a program that was written concurrently. The visualization system can be further improved by implementing a scanner at the very start of the program. The function of this scanner would be to 4. Fidge, C.J., A Simple Run-Time Concurrency Measure. 3 rd Australian Transputer and OCCAM User Group Conference, Fidge, C.J., Logical Time in Distributed Computing Systems, Computer, vol.24, no.8, pp.28-33, Aug, Lamport, L., Time, Clocks, and the Ordering of Events in a Distributed System, Communications of the ACM, Vol. 21, no.7, July Mattern, F., Virtual Time and Global States of Distributed Systems, Proceedings of the International Workshop on Parallel and Distributed Algorithms, Raynal, M.; Mizuno, M.; Neilsen, M.L.; "Synchronization and concurrency measures for distributed computations, Proceedings of the 12 th International Conference on Distributed Computing Systems, pp , 1992.

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