PAGE: A PARTITION AWARE ENGINE FOR PARALLEL GRAPH COMPUTATION
Download as DOCX, PDF0 likes300 views
This document presents PAGE, a partition aware graph computation engine designed to optimize parallel graph processing by better utilizing high-quality graph partitions. The engine employs a dual concurrent message processor and a dynamic concurrency control model to enhance message processing efficiency across various graph partition qualities. Experimental results demonstrate PAGE's improved performance compared to existing systems that struggle with efficiently leveraging well-partitioned graphs.
![computing node. In this paper, PAGE focuses on the efficiency of a worker
processing messages.
PROPOSED SYSTEM:
The main contributions of our work are summarized as follows:
_ We propose the problem that existing graph computation systems cannot
efficiently exploit the benefit of high quality graph partitions.
_ We design a new partition aware graph computation engine, called PAGE. It can
effectively harness the partition information to guide parallel processing resource
allocation, and improve the computation performance.
_ We introduce a dual concurrent message processor. The message processor
concurrently processes incoming messages in a unified way and is the cornerstone
in PAGE.
_ We present a dynamic concurrency control model. The model estimates
concurrency for dual concurrent message processor by satisfying the producer
consumer constraints. The model always generate proper configurations for PAGE
when the graph applications or underlying graph partitions change.
_ We implement a prototype of PAGE and test it with real-world graphs and
various graph algorithms. The results clearly demonstrate that PAGE performs
efficiently over various graph partitioning qualities. This paper extends a
preliminary work [32] in the following aspects. First, we detailed analyze the
relationship among the workload of message processing, graph algorithms and](https://image.slidesharecdn.com/pageapartitionawareengine-150511053453-lva1-app6891/85/PAGE-A-PARTITION-AWARE-ENGINE-FOR-PARALLEL-GRAPH-COMPUTATION-3-320.jpg)
![computation engine named PAGE that monitors three high-level key running
metrics and dynamically adjusts the system configurations. In the adjusting model,
we elaborated two heuristic rules to effectively extract the system characters and
generate proper parameters. We have successfully implemented a prototype system
and conducted extensive experiments to prove that PAGE is an efficient and
general parallel graph computation engine.
REFERENCES
[1] Giraph. (2013). [Online]. Available: https://github.com/apache/giraph
[2] Storm. (2014). [Online]. Available: http://storm.incubator.apache.org/
[3] A. Amr, S. Nino, N. Shravan, J. Vanja, and S. J. Alexander, “Distributed
largescale natural graph factorization,” in Proc. 22nd Int. Conf. World Wide Web,
2013, pp. 37–48.
[4] N. Backman, R. Fonseca, and U. C¸ etintemel, “Managing parallelism for
stream processing in the cloud,” in Proc. 1st Int. Workshop Hot Topics Cloud Data
Process.,2012, pp. 1:1–1:5.
[5] L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan, “Group formation in
large social networks: Membership, growth, and evolution,” in Proc. 12th ACM
SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2006, pp. 44–54.
[6] P. Boldi and S. Vigna, “The webgraph framework I: Compression techniques,”
in Proc. 13th Int. Conf. World Wide Web, 2004, pp. 595– 602.](https://image.slidesharecdn.com/pageapartitionawareengine-150511053453-lva1-app6891/85/PAGE-A-PARTITION-AWARE-ENGINE-FOR-PARALLEL-GRAPH-COMPUTATION-10-320.jpg)
![[7] P. Boldi, M. Rosa, M. Santini, and S. Vigna, “Layered label propagation: A
multiresolution coordinate-free ordering for compressing social networks,” in Proc.
20th Int. Conf. World Wide Web, 2011, pp. 587–596.
[8] S. Brin and L. Page, “The anatomy of a large-scale hypertextual web search
engine,” in Proc. 7th Int. Conf. World Wide Web, 1998, pp. 107–117.
[9] A. Chan, F. Dehne, and R. Taylor, “CGMGRAPH/CGMLIB: Implementing
and testing CGM graph algorithms on PC clusters and shared memory machines,”
J. High Perform. Comput. Appl., pp. 81–97, 2005.
[10] G. Cong, G. Almasi, and V. Saraswat, “Fast PGAS implementation of
distributed graph algorithms,” in Proc. Int. Conf. High Perform. Comput., Netw.,
Storage Anal., 2010, pp. 1–11.
[11] J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large
clusters,” in Proc. Operating Syst. Des. Implementation, 2004, pp. 107–113.
[12] D. Gregor and A. Lumsdaine, “The parallel BGL: A generic library for
distributed graph computations,” in Proc. Parallel Object-Oriented Sci. Comput.,
2005, pp. 1–18.
[13] C. A. R. Hoare, “Communicating sequential processes,” Commun. ACM, vol.
21, pp. 666–677, 1978.
[14] U. Kang, C. E. Tsourakakis, and C. Faloutsos, “PEGASUS: A petascale graph
mining system implementation and observations,” in Proc. IEEE 9th Int. Conf.
Data Mining, 2009, pp. 229–238.](https://image.slidesharecdn.com/pageapartitionawareengine-150511053453-lva1-app6891/85/PAGE-A-PARTITION-AWARE-ENGINE-FOR-PARALLEL-GRAPH-COMPUTATION-11-320.jpg)
PAGE: A PARTITION AWARE ENGINE FOR PARALLEL GRAPH COMPUTATION
- 1. PAGE: A PARTITION AWARE ENGINE FOR PARALLEL GRAPH COMPUTATION Abstract—Graph partition quality affects the overall performance of parallel graph computation systems. The quality of a graph partition is measured by the balance factor and edge cut ratio. A balanced graph partition with small edge cut ratio is generally preferred since it reduces the expensive network communication cost. However, according to an empirical study on Giraph, the performance over well partitioned graph might be even two times worse than simple random partitions. This is because these systems only optimize for the simple partition strategies and cannot efficiently handle the increasing workload of local message processing when a high quality graph partition is used. In this paper, we propose a novel partition aware graph computation engine named PAGE, which equips a new message processor and a dynamic concurrency control model. The new message processor concurrently processes local and remote messages in a unified way. The dynamic model adaptively adjusts the concurrency of the processor based on the online statistics. The experimental evaluation demonstrates the superiority of PAGE over the graph partitions with various qualities EXISTING SYSTEM: This work is related to several research areas. Not only graph computation systems are touched, but also the graph partition techniques and effective integration of
- 2. them are essential to push forward current parallel graph computation systems. Here we briefly discuss these related research directions as follows. Graph computation systems. Parallel graph computation is a popular technique to process and analyze large scale graphs. Different from traditional big data analysis frameworks, most of graph computation systems store graph data in memory and cooperate with other computing nodes via message passing interface . Besides, these systems adopt the vertex-centric programming model and release users from the tedious communication protocol. Such systems can also provide fault-tolerant and high scalability compared to the traditional graph processing libraries, such as Parallel BGL and CGMgraph. There exist several excellent systems, like Pregel, Giraph, GPS, Trinity. Since messages are the key intermediate results in graph computation systems, all systems apply optimization techniques for the message processing. Pregel and Giraph handle local message in computation component but concurrently process remote messages. This is only optimized for the simple random partition, and cannot efficiently use the well partitioned graph. Based on Pregel and Giraph, GPS applies several other optimizations for the performance improvement. One for message processing is that GPS uses a centralized message buffer in a worker to decrease the times of synchronization. This optimization enables GPS to utilize high quality graph partition. But it is still very preliminary and cannot extend to a variety of graph computation systems. Trinity optimizes the global message distribution with bipartite graph partition techniques to reduce the memory usage, but it does not discuss the message processing of a single
- 3. computing node. In this paper, PAGE focuses on the efficiency of a worker processing messages. PROPOSED SYSTEM: The main contributions of our work are summarized as follows: _ We propose the problem that existing graph computation systems cannot efficiently exploit the benefit of high quality graph partitions. _ We design a new partition aware graph computation engine, called PAGE. It can effectively harness the partition information to guide parallel processing resource allocation, and improve the computation performance. _ We introduce a dual concurrent message processor. The message processor concurrently processes incoming messages in a unified way and is the cornerstone in PAGE. _ We present a dynamic concurrency control model. The model estimates concurrency for dual concurrent message processor by satisfying the producer consumer constraints. The model always generate proper configurations for PAGE when the graph applications or underlying graph partitions change. _ We implement a prototype of PAGE and test it with real-world graphs and various graph algorithms. The results clearly demonstrate that PAGE performs efficiently over various graph partitioning qualities. This paper extends a preliminary work [32] in the following aspects. First, we detailed analyze the relationship among the workload of message processing, graph algorithms and
- 4. graph partition. Second, technical specifics behind the dynamic concurrency control model are analyzed clearly. Third, the practical dynamic concurrency control model, which estimates the concurrency in incremental fashion, is discussed. Fourth, to show the effectiveness of DCCM, the comparison experiment between DCCM and manual tuning are conducted. Fifth, to show the advantage and generality of PAGE, more graph algorithms, such as diameter estimator, breadth first search (BFS), single source shortest path (SSSP), are ran on PAGE. Module 1 Graph Algorithms In reality, besides the graph partition, the actual workload of message processing in an execution instance is related to the characteristics of graph algorithms as well. Here we follow the graph algorithm category. On basis of the communication characteristics of graph algorithms when running on a vertex-centric graph computation system, they are classified into stationary graph algorithms and non- stationary graph algorithms. The stationary graph algorithms have the feature that all vertices send and receive the same distribution of messages between supersteps, like PageRank. In contrast, the destination or size of the outgoing messages changes across supersteps in the non-stationary graph algorithms. For example, traversal-based graph algorithms, e.g., breadth first search and single source shortest path, are the non-stationary ones. In the stationary graph algorithms, every vertex has the same behavior during the execution, so the workload of message
- 5. processing only depends on the underlying graph partition. When a high quality graph partition is applied, the local message processing workload is higher than the remote one, and vice versa. The high quality graph partition helps improve the overall performance of stationary graph algorithms, since processing local messages is cheaper than processing remote messages, which has a network transferring cost. For the traversal-based graph algorithms belonging to the non- stationary category, it is also true that the local message processing workload is higher than the remote one when a high quality graph partition is applied. Because the high quality graph partition always clusters a dense sub graph together, which is traversed in successive super steps. However, the high quality graph partition cannot guarantee a better overall performance for the non-stationary ones, because of the workload imbalance of the algorithm itself. This problem can be solved by techniques. In this paper, we focus on the efficiency of a worker when different quality graph partitions are applied. The systems finally achieve better performance by improving the performance of each worker and leave the workload imbalance to the dynamic repartition solutions. The next subsection will reveal the drawback in the existing systems when handling different quality graph partitions. Module 2 Messageprocessing
- 6. In Pregel-like graph computation systems, vertices exchange their status through message passing. When the vertex sends a message, the worker first determines whether the destination vertex of the message is owned by a remote worker or the local worker. In the remote case, the message is buffered first. When the buffer size exceeds a certain threshold, the largest one is asynchronously flushed, delivering each to the destination as a single message. In the local case, the message is directly placed in the destination vertex’s incoming message queue . Therefore, the communication cost in a single worker is split into local communication cost and remote communication cost. Combining the computation cost, the overall cost of a worker has three components. Computation cost, denoted by tcomp, is the cost of execution of vertex programs. Local communication cost, denoted by tcomml, represents the cost of processing messages generated by the worker itself. Remote communication cost, denoted by tcommr, includes the cost of sending messages to other workers and waiting for them processed. In this paper, we use the cost of processing remote incoming messages at local to approximate the remote communication cost. There are two reasons for adopting such an approximation. First, the difference between two costs is the network transferring cost, which is relatively small compared to remote message processing cost. Second, the waiting cost of the remote communication cost is dominated by the remote message processing cost. The workload of local (remote) message processing determines the local (remote) communication cost. The graph partition influences the workload distribution of local and remote message processing. A high quality graph partition, which is balanced and has small edge cut ratio, usually
- 7. leads to the local message processing workload is higher than the remote message processing workload, and vice versa. Module 3 PAGE PAGE, which stands for Partition Aware Graph computation Engine, is designed to supportdifferent graph partition qualities and maintain high performance by an adaptively tuning mechanism and new cooperation methods. Similar to the majority of existing parallel graph computation systems, PAGE follows the master-worker paradigm. The computing graph is partitioned and distributive stored among workers’ memory. The master is responsible for aggregating global statistics and coordinating global synchronization. Thus the workers in PAGE can be aware of the underlying graph partition information and optimize the graph computation task. Module 4 Dual concurrent messageprocessor The dual concurrent message processor is the core of the enhanced communication model, and it concurrently processes local and remote incoming messages in a unified way. With proper configurations for this new message processor, PAGE
- 8. can efficiently deal with incoming messages over various graph partitions with different qualities. Messages are delivered in block, because the network communication is an expensive . But this optimization raises extra overhead in terms that when a worker receives incoming message blocks, it needs to parse them and dispatches extracted messages to the specific vertex’s message queue. In PAGE, the message process unit is responsible for this extra overhead, and it is a minimal independent process unit in the communication module. A remote (local) message process unit only processes remote (local) incoming message blocks. The message processor is a collection of message process units. The remote (local) message processoronly consists of remote (local) message process units. Module 5 Dynamic concurrencycontrol model The concurrency of dual concurrent message processor heavily affects the performance of PAGE. But it is expensive and also challenging to determine a reasonable concurrency ahead of real execution without any assumption. Therefore, PAGE needs a mechanism to adaptively tune the concurrency of the dual concurrent message processor. The mechanism is named Dynamic Concurrency Control Model, DCCM for short. In PAGE, the concurrency control problem can be modeled as a typical producer- consumer scheduling problem, where the computation phase generates messages as
- 9. a producer, and message process units in the dual concurrent message processor are the consumers. Therefore, the producer- consumer constraints should be satisfied when solving the concurrency control problem. For the PAGE situation, the concurrency control problem rises consumer constraints. Since the behavior of producers is determined by the graph algorithms, PAGE only requires to adjust the consumers to satisfy the constraints (behavior of graph algorithms), which are stated as follows. First, PAGE provides sufficient message process units to make sure that new incoming message blocks can be processed immediately and do not block the whole system. Meanwhile, no message process unit is idle. Second, the assignment strategy of these message process units ensures that each local/remote message process unit has balanced workload since the disparity can seriously destroy the overall performance of parallel processing. CONCLUSION In this paper, we have identified the partition unaware problem in current graph computation systems and its severe drawbacks for efficient parallel large scale graphs processing. To address this problem, we proposed a partition aware graph
- 10. computation engine named PAGE that monitors three high-level key running metrics and dynamically adjusts the system configurations. In the adjusting model, we elaborated two heuristic rules to effectively extract the system characters and generate proper parameters. We have successfully implemented a prototype system and conducted extensive experiments to prove that PAGE is an efficient and general parallel graph computation engine. REFERENCES [1] Giraph. (2013). [Online]. Available: https://github.com/apache/giraph [2] Storm. (2014). [Online]. Available: http://storm.incubator.apache.org/ [3] A. Amr, S. Nino, N. Shravan, J. Vanja, and S. J. Alexander, “Distributed largescale natural graph factorization,” in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 37–48. [4] N. Backman, R. Fonseca, and U. C¸ etintemel, “Managing parallelism for stream processing in the cloud,” in Proc. 1st Int. Workshop Hot Topics Cloud Data Process.,2012, pp. 1:1–1:5. [5] L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan, “Group formation in large social networks: Membership, growth, and evolution,” in Proc. 12th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2006, pp. 44–54. [6] P. Boldi and S. Vigna, “The webgraph framework I: Compression techniques,” in Proc. 13th Int. Conf. World Wide Web, 2004, pp. 595– 602.
- 11. [7] P. Boldi, M. Rosa, M. Santini, and S. Vigna, “Layered label propagation: A multiresolution coordinate-free ordering for compressing social networks,” in Proc. 20th Int. Conf. World Wide Web, 2011, pp. 587–596. [8] S. Brin and L. Page, “The anatomy of a large-scale hypertextual web search engine,” in Proc. 7th Int. Conf. World Wide Web, 1998, pp. 107–117. [9] A. Chan, F. Dehne, and R. Taylor, “CGMGRAPH/CGMLIB: Implementing and testing CGM graph algorithms on PC clusters and shared memory machines,” J. High Perform. Comput. Appl., pp. 81–97, 2005. [10] G. Cong, G. Almasi, and V. Saraswat, “Fast PGAS implementation of distributed graph algorithms,” in Proc. Int. Conf. High Perform. Comput., Netw., Storage Anal., 2010, pp. 1–11. [11] J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” in Proc. Operating Syst. Des. Implementation, 2004, pp. 107–113. [12] D. Gregor and A. Lumsdaine, “The parallel BGL: A generic library for distributed graph computations,” in Proc. Parallel Object-Oriented Sci. Comput., 2005, pp. 1–18. [13] C. A. R. Hoare, “Communicating sequential processes,” Commun. ACM, vol. 21, pp. 666–677, 1978. [14] U. Kang, C. E. Tsourakakis, and C. Faloutsos, “PEGASUS: A petascale graph mining system implementation and observations,” in Proc. IEEE 9th Int. Conf. Data Mining, 2009, pp. 229–238.