Research Article  Open Access
Yong Peng, Long Qin, Quanjun Yin, "A Scalable GVT Estimation Algorithm for PDES: Using Lower Bound of EventBulkTime", Mathematical Problems in Engineering, vol. 2015, Article ID 680283, 15 pages, 2015. https://doi.org/10.1155/2015/680283
A Scalable GVT Estimation Algorithm for PDES: Using Lower Bound of EventBulkTime
Abstract
Global Virtual Time computation of Parallel Discrete Event Simulation is crucial for conducting fossil collection and detecting the termination of simulation. The triggering condition of GVT computation in typical approaches is generally based on the wallclock time or logical time intervals. However, the GVT value depends on the timestamps of events rather than the wallclock time or logical time intervals. Therefore, it is difficult for the existing approaches to select appropriate time intervals to compute the GVT value. In this study, we propose a scalable GVT estimation algorithm based on Lower Bound of EventBulkTime, which triggers the computation of the GVT value according to the number of processed events. In order to calculate the number of transient messages, our algorithm employs EventBulk to record the messages sent and received by Logical Processes. To eliminate the performance bottleneck, we adopt an overlapping computation approach to distribute the workload of GVT computation to all workerthreads. We compare our algorithm with the fast asynchronous GVT algorithm using PHOLD benchmark on the shared memory machine. Experimental results indicate that our algorithm has a light overhead and shows higher speedup and accuracy of GVT computation than the fast asynchronous GVT algorithm.
1. Introduction
Due to its remarkable availability, reproducibility, and costeffectiveness, parallel simulation is a crucial approach for studying, developing, and evaluating mathematical models of systems in different domains, such as economics [1], medicine [2], human behaviors [3], and military [4]. Parallel Discrete Event Simulation (PDES) is realized through partitioning simulation problems into several distinct objects and then allocating the resulting partitions to different LPs that run concurrently. Such a parallel mechanism requires synchronization protocols to prevent causality violations or to guarantee state consistency in case of causality violations. In general, the existing synchronization protocols fall into two categories: conservative synchronization protocol and optimistic synchronization protocol (Time Warp) [5].
In optimistic synchronization protocols, Global Virtual Time (GVT) is defined as the minimum among the local virtual times of all processes and the timestamps of all messages in transit [6, 7]. A transient message is a delayed message that has been sent but has not been received. Any processed event with a timestamp smaller than the GVT value will not be rolled back. Moreover, the memory associated with it can be safely reclaimed. An efficient GVT algorithm is crucial for the optimistic time synchronization strategy to handle causality errors, reclaim memory, and detect the termination of simulation execution [8]. Numerous GVT algorithms have been devised and implemented within PDES simulators, such as GTW [9], ROSS [10], WARPED [11], ParaSol [12], and ROOTSim [13]. Because of its high performance, scalability, simple programming model, and application transparent parallelization, ROOTSim is probably the most advanced and widely used open source speculative PDES platform [14, 15].
Designs of GVT algorithms focus on either shared memory or distributed memory computers [16, 17]. Shared memory algorithm assumes that certain variables are accessible by all processors. So they perform well on symmetric multiprocessing machines [15, 18, 19]. Distributed memory algorithm does not use global variables and therefore is more scalable. For them both, time interval approach is a common solution for triggering GVT computation. However, the GVT value depends on the timestamps of events rather than wallclock time or logical time intervals. Therefore, it is difficult for traditional algorithms to select appropriate time intervals to obtain an accurate GVT value.
In this study, we propose a scalable GVT estimation algorithm based on Lower Bound of EventBulkTime (LBEBT). In our algorithm, the start of GVT computation is triggered by the end of the EventBulk (EB) instead of wallclock time or logical time intervals. Processors use EBs to record the number of events sent and received by LPs, as well as the LVT of each LP and the timestamps of events in transit. GVT computation via the EB approach can avoid message acknowledgement, which is a common solution for the problem of transient event in distributed GVT algorithms [16, 17, 20, 21]. In our algorithm, the size of EventBulk is negatively correlated with the accuracy of GVT computation but positively correlated with the performance of GVT computation. Through tuning the size of EventBulk, we can achieve a balance between the performance and accuracy of GVT computation. In addition, to make the algorithm scalable and efficient, we adopt an overlapping computation approach to distribute the workload of GVT computation to all workerthreads.
In experiments, we test our algorithm using PHOLD benchmark [22] in a prototypal testbed on the shared memory machine. We conduct three sets of experiments to study the overhead, speedup, and accuracy of GVT computation. In addition, we compare our algorithm with a fast asynchronous GVT algorithm (FAGVT) that triggers the GVT computation according to wallclock time intervals. Experimental results indicate that our algorithm outperforms the FAGVT algorithm in terms of speedup and accuracy.
2. Related Work
2.1. Distributed Memory Algorithms
In GVT algorithms for distributed memory platforms, LPs communicate their LVTs and the timestamps of events in transit with each other by exchanging messages. GVT algorithms have to tackle two problems: the LVT simultaneous reporting problem and the transient message problem [20, 21]. The LVT simultaneous reporting problem is that it is difficult to measure LVTs of all LPs running concurrently on different processors (or threads) at the same wallclock time. The transient message problem is caused by delay messages. Processors that send or receive messages do not consider the timestamps of transient messages when they compute LVT values [23].
In the early studies of GVT algorithms, researchers often apply overlapping intervals and message acknowledgement techniques to solve these two problems above. For example, wallclock time slices are used as LPs’ intervals to compute the GVT value [23–25]. The startup of GVT computation is triggered by control messages that are broadcasted [24, 26, 27] or circulated [25, 28, 29] among LPs. The limitation of overlapping intervals is the difficulty in selecting appropriate time intervals. In case of a large time interval, the accuracy of GVT value decreases. At the same time, the memory for storing processed events and the states of LPs increases. Contrarily, in case of a small time interval, the number of control messages transmitted among LPs increases. Message acknowledgement scheme is a widely used technique for the transient message problem in early GVT algorithms [25–27]. However, this technique comes with some limitations. For example, acknowledging individual messages scheme doubles the number of messages to decrease the performance of simulations. Although some methods, such as acknowledging batches of messages [30] and piggyback acknowledgement [21, 24], were devised to reduce the acknowledgement overhead, the acknowledgement scheme becomes complex.
Another more efficient solution than the earlier GVT algorithms is to combine consistent snapshot and the global reduction method. Consistent snapshot (or two cuts method) GVT algorithm was firstly proposed by Mattern [31]. A vector structure called vector clock is used to monitor the number of transient messages in his algorithm. A token carrying the vector clock is passed among LPs to construct the cuts. LVTs of LPs and timestamps of messages in transit are recorded in cuts to calculate the GVT value. Consistent snapshot method is more efficient than message acknowledgement technique in solving transient message problem. However, the size of the vector clock is dependent on the number of LPs, which hampers the scalability of the algorithm. An improved version of Mattern’s algorithm was proposed by Choe and Tropper [32], which uses a scalar counter instead of vector clock to monitor the number of transient messages. Other similar algorithms with Mattern’s algorithm can be found in [17, 28, 33]. Global reduction algorithms do not pass tokens among LPs but use reduction operation at the synchronization point to collect the timestamps of the transient messages and the LVTs of LPs. A global reduction GVT algorithm was proposed by Perumalla and Fujimoto [34]. Other similar GVT algorithms based on global reduction method are in [35, 36]. The main drawback of these algorithms is that all LPs have to synchronize for reduction operations.
Other GVT algorithms with different ideas are represented as follows. To compute GVT value, D’Souza et al. applied a GVT manager to collect information from all LPs [37]. Similarly, Chen and Szymanski used hierarchy GVT masters to collect GVT reports from LPs passively and then distribute the new GVT value to LPs [17]. Bauer et al. assumed that the maximum delay of messages transmitted on network is known [38]. They devised network atomic operation method to create zerocost consistent cuts to calculate GVT. Deelman and Szymanski proposed a continuously monitored GVT algorithm that allows LPs to calculate the GVT value based on the local information [39].
2.2. Shared Memory Algorithms
In shared memory algorithms, the shared variable approach is a general method for calculating the GVT value. For example, fast asynchronous GVT algorithm (FAGVT) proposed by Xiao et al. [19] is a typical GVT algorithm for shared memory machines. The FAGVT algorithm triggers GVT computation according to wallclock time intervals and relies on shared variables that are accessed by workerthreads to compute the GVT value under the control of a critical section. Due to the existence of the critical section, FAGVT algorithm is neither scalable nor waitfree for LPs. Fujimoto and Hybinette proposed a similar approach [18].
In order to improve the scalability of the algorithm proposed by Fujimoto and Hybinette, Pellegrini and Quaglia proposed a waitfree GVT computation algorithm on shared memory machines. Pellegrini and Quaglia used memory atomic operations on certain shared counters/flags to track the advancement of workerthreads within the different phases of GVT computation [15]. Experiments reveal that Pellegrini’s approach is more efficient and more scalable than the algorithm in [18]. Rizvi et al. proposed a GVT algorithm based on Unacknowledged Message List (UML) scheme [21]. They assumed that certain variables are accessible by all processors. In addition, Rizvi et al. used a dedicated controller LP to monitor the GVT computation process.
3. Design of LBEBT GVT Algorithm
3.1. Algorithm Description
3.1.1. Principle of Our Algorithm
We assume that there is a GVT master process in our algorithm, which is in charge of computing the GVT value and reporting the new GVT value to processors. Figure 1 gives an example to illustrate the principle of our algorithm. The process of a round of GVT computation in our algorithm consists of two phases.
In the first phase, each processor constructs EventBulks (EBs) and sends the data of EBs to the GVT master via EB Report messages. At the startup of a round of GVT computation, each processor maintains a scale counter to record the number of event/antievent processed by it. Once the value of the counter reaches the size of EB (the size of EB is two in this example shown in Figure 1), the processor ends current EB and starts to construct the subsequent EB. At the same time, the processor sends an EB Report message to GVT master. EB Report message carries the data that is necessary for computing the GVT value. Therefore, our algorithm uses EBs (instead of wallclock or logical time intervals) to trigger the computation of GVT value. The size of EB of a processor at a round of GVT computation is only determined by the processor according to the requirements of memory management, the accuracy of GVT value, and the frequency of committing events.
In the second phase, GVT master computes the GVT value and sends the new GVT value to processors. During the simulation execution, the GVT master is listening to EB Report messages from processors. Once receiving EB Report messages from all processors, GVT master performs GVT computation and then sends the new GVT value to all processors via GVT Notifying messages.
Due to all workload of GVT computation being processed in GVT master, GVT master becomes the performance bottleneck when there is a larger amount of processors. The algorithm mentioned above is a centralized algorithm and is not scalable [17]. To overcome this drawback, we remove the GVT master and distribute the workload of GVT computation to all processors via a communication topology of processors. The communication topology acts as a route to pass EB Report messages and GVT Notifying messages among processors.
The communication topology of processors is represented as a complete ary tree [40]. Parameter is an integer and subject to ( is the number of processors). In graph theory, ary tree is a rooted tree in which each node has children at most. A complete ary tree is a ary tree, which is maximally space efficient. It must be completely filled on every level except the last level [40]. The communication topology of our algorithm is represented as and depicted in Figure 2.
After employing the communication topology of processors, a round of GVT computation in our algorithm is extended to three phases. In the first phase, whenever any processor (including leaf processors) ends its EB, it sends control messages to all leaf processor(s) to request them to report EB Report messages (rather than sending an EB Report message to GVT master as mentioned before). Once receiving a control message, a leaf processor has two choices according to the index of its current EB. In case that the index is smaller than the index in the control message, the leaf processor ends current EB and sends an EB Report message to its parent processor. Otherwise, the leaf processor just discards the control message. In the communication topology, EB Report messages in any GVT computation round are sent by the leaf processors firstly.
In the second phase, a nonleaf processor records its EB and starts to construct the subsequent EB at the end of current EB. The processor does not send EB Report messages to its parent processor until it receives EB Report message(s) from all its children processors. However, once receiving EB Report message(s) from all its children processors and even the number of processed events is less than the size of EB, the processor ends its current EB. At the same time, the processor handles the data of its current EB and the data of all EB Report messages from all its children processors and then sends the result to its parent processor via an EB Report message.
In the final phase, when the root processor receives EB Report messages for one round of GVT computation from all its children processor, it performs GVT computation and then sends the new GVT value to its children processor via GVT Notifying messages. Once a processor receives a GVT Notifying message, it updates its local GVT value and then sends GVT Notifying messages to its children processors. GVT Notifying messages are sent to descendant processors until all leaf processors get the new GVT value.
By using the communication topology of processors, the workload of GVT computation is distributed to all processor. Our algorithm does not use a GVT monitor or GVT master to perform GVT calculation like other algorithms [17, 21, 32, 37]. Each processor maintains a counter indicating the index of the current EB and increases the counter at the end of the EB. Once a processor ends an EB, it then starts subsequent EB immediately and does not need to be blocked or synchronized. The startup of subsequent round of GVT computation can be triggered before the completion of the previous round. While a processor performs GVT computation for one round, other processors can process EBR messages for another GVT computation round. Theoretically, a processor can process any number of basic messages and construct any number of EBs before a GVT computation round finishes. According to the indices of EBR messages and the communication topology, the root processor can calculate the number of transient messages in each round and knows which round of GVT computation is completed. This solution is suitable for largescale simulation systems and can reduce the overhead of GVT computation.
In addition, EB Report messages and basic event/antievent messages are labeled with the index of EB before they are sent to destination processors. Processors can correctly process EB Report messages and even EB Report messages or event/antievent messages are not transferred in FirstInFirstOut (FIFO) manner. In other words, our approach is not dependent on the FIFO communication channels, which is the precondition of some literature algorithms, such as the Time Quantum GVT algorithm [17] and the Hypercube GVT algorithm [26].
3.1.2. Design Detail
We define some concepts before describing the algorithm in detail. In our algorithm, the messages used to carry remote events/antievents are called basic messages formalized in Table 1. In addition, EB messages and the GVT messages (including EB Report messages, GVT Notifying messages, and control messages) are also defined in Table 1. According to the definition of EB, we define the notion of EventBulkTime (EBT). The EBT of an EB consists of the and the of EB. The Lower Bound of EBT is therefore the minimum of these two parts.

We define three operators for a processor node in the communication topology: , , and , to describe our algorithm in detail. Operator described by (1) is used to obtain the parent processor node of :where and are the identifiers of child processor and parent processor, respectively. is the factor of . is the integer part of .
Operator in (2) obtains the set of children nodes of a processor :
Node is the first child of in case of . If having no first child, a processor cannot have any children and becomes a leaf node. Whether processor is a leaf node processor can be determined by this condition. The operator is described as
Beyond these three operators, the set of leaf nodes is described by the operator according to the following equation: where is the ID of the first processor node on the deepest nonleaf level of and is the number of nonleaf processor nodes on the deepest nonleaf level of , with . is the identifier of the first leaf processor node.
All operators described above are only executed at the startup of the simulation to construct the communication topology and will not be executed any more during the simulation execution unless the topology is changed.
The pseudocode of LBEBT GVT algorithm is given in Algorithm 1. At the startup of simulation, the algorithm initializes parameters including the number of workerthreads, the number of LPs, the Stop Time of a simulation, and the size of EB. Then, the algorithm records the number of processed basic messages and updates LVT (see line S4). Besides, the algorithm tracks the number of remote basic messages that are sent and received (see line S6 and line S10). The minimum timestamp of remote basic messages is also recorded in MTSMAP (see line S9). Once the number of processed basic messages reaches the size of current EB or the LVT is greater than the Stop Time of the simulation, the processor ends the current EB and then requests the leaf processors to report their EBR messages (see lines S13–S17).

Once receiving an EBR message, a processor records the number of transient message(s) carried by the EBR message and calculates the minimum of LVTs and the minimum timestamp of transient messages (see lines S24–S28). If all children have reported messages, nonroot processors will send an EBR message to its parent processor (see lines S29–S39).
Once the root processor processed all EBR messages from its children processors for one round of GVT computation, the minimum of each LBEBT is stored in MTSMAP or LVTMAP of the root processor (see lines S32S33). And then the root processor can obtain the number of transient events through calculating the data in TMCMAP. If there are transient events, GVT value is determined by the minimum value of all LBEBT. Otherwise, the GVT value is the minimum of of the EBs in one round of GVT computation (see line S36). Finally, the new GVT value will be calculated by the root processor and then sent to its children processors via GN messages.
3.2. Example
We describe the process of GVT computation in our algorithm step by step through an example shown in Figure 3. We assume that there are four processors in a system. Factor in the communication topology of processor is two. Processor is the root processor node that has two children processor nodes: and . Processor is the child of . Then, and are leaf processors.
The process of the th round of GVT computation shown in Figure 3 is described below.
Step 1. All processors begin to construct their th EBs.
Step 2. Before sending a basic message, a processor sets the index of the basic message as the index of current EB. Once receiving a basic message, the processor adds a keyvalue pair record to of its current EB. The key is the index of the basic message. The number of basic messages stored in the record of of the current EB increases by one.
Step 3. Once the number of processed events in the th EB of a processor reaches the size of the th EB, the processor ends the th EB. At the same time, the processor sends control messages to ask the leaf processors to report their EBR messages. As shown in Figure 3, processor ends its th EB and then sends a control message to another leaf processor . Since processor is a leaf processor, it sends an EBR message, namely, , to its parent processor at the end of its th EB. The LVT of is 5.2 and three basic messages have been sent during the th EB. The minimum timestamp of these three basic messages is 6.8. receives two basic messages during the EB. These two messages are sent during the EB. The number of transient messages known to a processor during the th EB is the number of the sent messages subtracted from the number of the received messages by the processor. Therefore, the number of transient messages known to is one.
Step 4. A processor does not send an EBR message to its parent until receiving EBR messages from all its children (e.g., processor ). Each processor maintains three map data structures (, , and ) to record the data of EBR messages from its children processors. The key field of these three maps records the indices of EBR messages. Once a parent processor receives EBR messages from all its children, it updates its , , and with EBR messages according to the following rules (an EBR message from a child processor is denoted as ).
Rule 1. For each key in ,
Rule 2. If (),
Rule 3. If (),
For example, after processing the EBR message from and its th EB, of processor is 2. is 3.8. Similarly, of is 4.2. The EBR message of sent to is therefore .
Step 5. When the root processor has received all the th EBR messages from all of its children processors during the th round of GVT computation, the new GVT value can be calculated from , , and of the root processor according to the following equation:
For the example shown in Figure 3, the of is and the is . The of is . These three maps indicate that all remote messages sent before the th round of GVT computation have been received and processed. There are three transient messages in the th round of GVT computation. The new GVT value is the smaller one between and . Therefore, the GVT value is 3.3 in the th round of GVT computation.
Step 6. At the end of the th round of GVT computation, the root processor sends GN messages to its children processors to notify them of the new GVT value. Then, GN messages are sent to its descendant processors until all leaf processors get the new GVT value.
3.3. Algorithm Implementation
We implemented our algorithm on a multicore shared memory machine for prototypical test. As the multicore machine is prevalent, multithreading techniques are commonly used in the speculative PDES [19, 41, 42].
Our algorithm is implemented within a simulation engine that consists of a number of schedulers. Each scheduler is bound to a workerthread and in charge of carrying out the function of a processor. At the initial phase of a simulation, the simulation engine firstly uses the pthread multithreaded library to configure the number of schedulers according to the available idle CPU cores of the machine and then generates the communication topology for schedulers according to their identifiers. Finally, the engine creates all LPs and averagely dispatches them to the schedulers.
A scheduler maintains two queues for storing and processing events. One of these two queues, called msg_cached_queue, is a concurrent queue which is used to temporarily store basic messages sent by the LPs hosted on some other schedulers. The other queue is called event_priority_queue, which is a priority queue. A scheduler uses the event_priority_queue to schedule LPs for processing event according to LowestTimestampFirst scheme [43]. In case a LP sends a remote event (via the basic message) to the LP hosted by another scheduler, the sender LP directly inserts the event into the msg_cached_queue of the scheduler hosting the receiver LP. After a scheduler dispatching a given number (the size of EB) of events or discovering that the event_priority_queue is empty, it will pop events from msg_cached_queue and push them into the event_priority_queue for processing.
During simulation execution, a scheduler records the data (the number of events sent/received and minimum timestamp of events sent in one GVT computation round) to its current EB. In addition, a scheduler maintains three map data structures (, , and ) to record the data of EBR messages from its children schedulers. Once a scheduler is notified with the new GVT value by its parent scheduler, it forces all LPs hosted on it to perform fossil collection via calling the commit function of each LP.
Although there are some more simple GVT algorithms than ours on shared memory machines, they rely on the observability property of Time Warp systems [15, 18, 19]. The observability property of Time Warp systems requires the msg_cached_queue being observable to the scheduler, which is impossible for Time Warp systems on distributed memory machines. Our algorithm does not rely on this property, which makes our algorithm more suitable for distributed memory machines. Therefore, although our algorithm is carried out on shared memory machines due to the convenience of programming, debugging, and testing, it can be migrated to distributed memory systems with trivial modifications.
4. Experimental Results
4.1. Experiment Configuration
We use PHOLD benchmark to conduct three experiments on shared memory machines to compare FAGVT algorithm as the baseline algorithm with our LBEBT GVT algorithm. In first experiment, we study the overhead of our algorithm and optimize parameter of communication topology . In the second experiment, we compare the speedup and scalability of our LBEBT GVT algorithm with those of FAGVT. In the final experiment, we evaluate the accuracy of our algorithm and compare the results with FAGVT algorithm. The number of initial events per LP is set to 16 in all experiments. The parameter setting in experiments is depicted in Table 2.

All experiments are run on a DELL R610 server with 24 GB RAM and two Intel Xeon processors running on 2.4 GHz. Each processor has six cores and 12 threads (for a total of 24 threads) that share a L1 cache with 2 MB and a L2 cache with 12 MB. The operation system is Microsoft Windows 2008 server R2. All algorithms are programmed in C++ language and compiled with Microsoft Visual Studio 2008.
4.2. Parameter Optimization
Parameter of communication topology is a key factor that influences the time span and the number of GVT messages of a round of GVT computation in our algorithm. We try to explore an optimized value of parameter for different values of parameter in experiments. In addition, the number of LPs , mean delay time , and the remote event ratio in PHOLD model are set as 396900 (the largest simulation), 1 second, and 25%, respectively.
We firstly study the relationship between and the time span of one round of GVT computation. is the wallclock time interval beginning at the time point when the first leaf scheduler reports the EBR message and ending at the time point when the last scheduler receives the GN message. Results shown in Figure 4 depict the evolution of with and . It indicates that gradually drops down in the value interval [1, 3] of parameter but rises up from 4 to . The plots reach the bottom in case that equals two or three. The bottom of plots demotes the minimum overhead of algorithms. Overall, it is shown in Figure 4 that rises up when increases.
The relationship between the number of GVT messages (including EBR messages, messages, and control messages) and is illustrated in Figure 5. When is larger than 4, the number of GVT messages increases quickly. Because the number of messages and messages remains the same for different in one round of GVT computation, we infer that the control message contributes to the increase of the number of GVT messages.
The relationship between the time span of one round of GVT computation and the size of EB is described in Figure 6.
It is shown in Figure 6 that the time span of one round of GVT computation increases along with the increase of the size of EB. The curves denote that rises up slowly when the number of schedulers is small but goes up quickly when the number of workerthreads is larger than nine. This phenomenon can be explained in twofold. On the one hand, the increase of the number of workerthreads results in more GVT messages to be processed. On the other hand, when the size of EB becomes larger, the construction of an EB requests more basic messages being processed. However, the size of EB is not linear to the time span. For example, when the sizes of EB are 128 and 256, the corresponding time spans of one round of GVT computation for 20 workerthreads are 0.0499 seconds and 0.0600 seconds in wallclock time, respectively. The time span only increases by 20.4%. It is concluded that more GVT calculation of workerthreads is overlapped when the size of EB increases.
The relationship between the number of GVT message and the size of EB is shown in Figure 7. We find that the number of GVT messages slightly fluctuates with the increase of the size of EB. This indicates that the number of basic messages is the main impact factor on the increase of time span of one round of GVT computation rather than the number of GVT messages.
Beyond the number of GVT messages, the size of EBR messages can also introduce overhead to our algorithm. The size of EBR messages is not constant and increases in case a processor receives basic messages with different indices during an EB. The reason is that the number of basic messages received by a processor is recorded with an indexnumber pair in of EBR message. The evolution of the size of in EBR messages with different sizes of EB is described in Figure 8. The size of is the maximum value obtained by ten tests. We observe that the size of is directly proportional to the size of EB but is inversely proportional to the number of workerthreads. In addition, the maximum size of is smaller than four in all cases, which means that the overhead introduced by is small enough and can be considered as a constant.
Overall, we discover in this experiment that the overhead of LBEBT GVT algorithm is minimum when is equal to two. We will use this value for the following experiments. The size of of EBR message is less than four in all cases (different number of LPs and different number of workerthreads), which can be considered as a constant.
4.3. Speedup and Scalability
Speedup and scalability are two significant metrics to evaluate the performance of GVT algorithms, which can be measured in terms of the committed event rate.
In this experiment, we compare the scalability of our algorithm with FAGVT. The results are shown in Figure 9, where all samples have been obtained as the average over five runs. Different pseudorandom seeds are used in each run. However, the same seed is used for the corresponding runs with two different GVT algorithms. Given that the large amount of event can be processed per wallclock time unit, the check point period of FAGVT is set to 0.1 seconds in wallclock time.
In Figure 9, we find that committed event rate is slightly lower in our algorithm than in FAGVT algorithm in case that the number of workerthreads is smaller than ten. This phenomenon is explained as the cost of GVT messages and the cost of rollback in our algorithm are larger than those of FAGVT algorithm. The critical section in FAGVT algorithm does not become the performance bottleneck yet. Moreover, with the increase of the number of workerthreads, the committed event rate in our algorithm slightly decreases but significantly decreases in FAGVT algorithm.
The number of rollbacks of our algorithm and FAGVT is shown in Figure 10. When the number of workerthreads is smaller than ten, the number of rollbacks of our algorithm is larger than that of FAGVT. The reason causing this phenomenon is that the overhead in our algorithm is greater than that of FAGVT. With the increase of the number of workerthreads, the number of rollbacks slightly increases in our algorithm but sharply increases in FAGVT. FAGVT is not a waitfree algorithm. So workerthreads in FAGVT have to wait for each other to access the critical section to compute the GVT value. This leads to stranger events that result in rollbacks. However, workerthreads in our algorithm are not blocked for computing GVT value. The probability of stranger events caused by GVT computation our algorithm is lower than that of FAGVT.
Figure 11 illustrates the committed event rates of our algorithm for the largest simulation with 396900 LPs. About 2.571 × 10^{8} messages are committed in the experiment that spends 1200 units of simulation time. In case that the number of workerthreads is greater than three, is set as two. For other cases, is set as one. As shown in Figure 11, when the remote messages ratio of PHOLD model is 25%, committed event rate increases nearly linearly with the number of workerthreads. The algorithm is scalable with the number of workerthreads. The speedup reaches up to about 16 in case that the number of running workerthreads is 20. When the remote messages ratio reaches up to 50% and 75%, committed event rate drops by 21% and 30%, respectively. It is evident in Figure 11 that although there is a performance drop in terms of committed event rate, the performance of algorithm still grows almost linearly with number of workerthreads. We also find in Figure 11 that committed event rate changes slightly with the different size of EB.
(a) Size of EB
(b) Size of EB
(c) Size of EB
(d) Size of EB
Compared with a sequential algorithm, our algorithm has a performance drop in case of a single workerthread. The committed event rate with one workerthread in our algorithm decreases 40% compared with the sequential algorithm. We think there are three factors for this phenomenon. Firstly, it takes time to send GVT messages and calculate the GVT value. Secondly, there is an overhead of memory for storing and releasing the processed events in parallel algorithm when rollbacks occur [17]. Thirdly, parallel algorithm takes more time to tackle remote events between processors, while the sequential simulator does not have such kind of messages. However, the overhead introduced by all above factors is constant, so the performance of our algorithm grows linearly with the number of workerthreads.
Figure 12 shows the change of the committed event rate caused by different number of workerthreads (shown in axis ) and different number of LPs (shown in various line style). We observe that the performance degrades when the number of LPs decreases. As shown in Figure 12(a), when the number of LPs reduced from 396900 to 40000, the committed event rate decreased 23%. From Figures 12(a), 12(b), and 12(c), we find that there is a slight drop in the committed event rate when the number of remote messages increases. This phenomenon is caused by the increase of remote basic messages and GVT messages.
(a) %
(b) %
(c) %
4.4. Accuracy of GVT Algorithm
We define the accuracy of a GVT algorithm as the mean ratio of estimated GVT value to actual GVT value. The actual GVT value can be only obtained by the most efficient optimistic GVT algorithm, which can speculatively process events and restrain the occurrence of rollbacks. The estimated GVT value is an approximation of the actual GVT value. A higher accurate GVT value is helpful for the algorithm to commit more events processed and release more memory allocated for the events. It is impossible to continually measure the actual GVT at all time points during the simulation execution. However, we can obtain an approximate actual GVT (pseudoactual GVT) by setting the remote event rate as zero to ensure that there is no rollback in the simulation execution.
The pseudoactual GVT value in our experiment is sampled per 0.1 seconds in wallclock time. We compare the accuracy of our algorithm with that of FAGVT. The results are shown in Figure 13. The accuracy of our algorithm is 95.9% in case of . In other words, the mean approximate value estimated by our algorithm possesses 95.9% precision with respect to pseudoactual GVT. When the size of EB is equal to 1024, the accuracy of our algorithm drops to 93.6%. For FAGVT, the accuracy is 65.8% in case of . However, when increases to 1.0, the accuracy of FAGVT increases to 66.3%. In Figure 13, we see that the GVT computation period of LBEBT GVT algorithm () is approximately equal to that of FAGVT (). However, the accuracy of our algorithm is higher than that of FAGVT, which also reflects that the overhead of our algorithms is lower than that of FAGVT.
The relationship between the accuracy of our algorithm and the size of EB is shown in Figure 14. The accuracy of our algorithm decreases sharply when the size of EB exponentially increases. In case of the size of EB , the accuracy reaches a limitation. We then discover that the accuracy could not be improved any more even if the size of EB further decreases. The accuracy of LBEBT GVT algorithm can be increased by decreasing the size of EB. However, when the accuracy reaches a threshold, it is difficult to improve it, even if the size of EB is small enough. This is explained as the accuracy is limited by the delay of messages and the overhead of our algorithm. As shown in Figure 14, the ratio of GVT messages to basic messages decreases exponentially with the exponential increase of the size of EB.
5. Conclusions and Future Works
We propose a LBEBT GVT algorithm for PDES in this study. GVT computation is partitioned into rounds by EventBulks. EventBulk is defined to track transient messages by recording the number of messages sent and received by LPs. In this way, the overhead introduced by message acknowledgement for solving transient message problem can be eliminated. High accurate GVT value can be obtained through decreasing the size of EB. In addition, through distributing the workload of GVT computation to all workerthreads according to a ary tree communication topology, we adopt an overlapping GVT computation approach in our algorithm to accelerate the computation of the GVT value.
Experiments and comparisons are performed to evaluate the overhead, scalability, and accuracy of our algorithm. An optimized value of parameter in communication topology is obtained by the experiments. Experimental results indicate that our algorithm introduces slight overhead and outperforms the FAGVT algorithm in terms of speedup and accuracy. Moreover, through adjusting the size of EB, our algorithm can obtain higher accurate GVT value than that of FAGVT while the overhead of algorithm is still small enough.
Although our algorithm shows high performance and is scalable on the testbed, there are still some issues that should be studied in the future. Firstly, the size of EB should be selfregulated according to the memory usage of a simulation, the event granularity, and the accuracy requirement of the GVT value. Secondly, the PDES platform of our algorithm is a prototypal testbed, which is quite distant from real advanced PDES optimistic simulation platforms in terms of capabilities and actual support for model development. In the future, we will carry out our algorithm within the famous PDES platform, such as ROOTSim [13, 14] and ROSS [10], and confirm its performance and scalability on a really distributed architecture, for example, Tianhe super parallel computation system [44].
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
The authors gratefully acknowledge the financial support of the National Science Foundation of China (nos. 61473300, 61374185, and 61403402). A lot of thanks should be given to referees and editors; their valuable comments greatly improved the quality of the paper.
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Copyright © 2015 Yong Peng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.