CHAUS： Scalable VM-Based Channels for Unbounded Streaming
Stream processing is a special form of the dataflow execution model that offers extensive opportunities for optimization and automatic parallelism. A streaming application is represented by a graph of computation stages that communicate with each other via FIFO channels. In shared-memory environment...
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| Published in: | Journal of computer science and technology Vol. 32; no. 6; pp. 1288 - 1304 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Springer US
01.11.2017
Springer Nature B.V School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China |
| Subjects: | |
| ISSN: | 1000-9000, 1860-4749 |
| Online Access: | Get full text |
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| Summary: | Stream processing is a special form of the dataflow execution model that offers extensive opportunities for optimization and automatic parallelism. A streaming application is represented by a graph of computation stages that communicate with each other via FIFO channels. In shared-memory environment, an FIFO channel is classically a com- mon, fixed-size synchronized buffer shared between the producer and the consumer. As the number of concurrent stage workers increases, the synchronization overheads, such as contention and waiting times, rise sharply and severely impair application performance. In this paper, we present a novel multithreaded model which isolates memory between threads by default and provides a higher level abstraction for scalable unicast or multicast communication between threads -- CHAUS （Channel for Unbounded Streaming）. The CHAUS model hides the underlying synchronization details, but requires the user to declare producer-consumer relationship of a channel in advance. It is the duty of the runtime system to ensure reliable data transmission at data item granularity as declared. To achieve unbounded buffer for streaming and reduce the synchronization overheads, we propose a virtual memory based solution to implement a scalable CHAUS channel. We check the programmability of CHAUS by successfully porting dedup and ferret from PARSEC as well as implementing MapReduce library with Phoenix-like API. The experimental results show that workloads built with CHAUS run faster than those with Pthreads, and CHAUS has the best scalability compared with two Pthread versions. There are three workloads whose CHAUS versions only spend no more than 0.17x runtime of Pthreads on both 16 and 32 cores. |
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| Bibliography: | streaming, thread model, pipeline parallelism, unbounded channel, virtual memory 11-2296/TP Stream processing is a special form of the dataflow execution model that offers extensive opportunities for optimization and automatic parallelism. A streaming application is represented by a graph of computation stages that communicate with each other via FIFO channels. In shared-memory environment, an FIFO channel is classically a com- mon, fixed-size synchronized buffer shared between the producer and the consumer. As the number of concurrent stage workers increases, the synchronization overheads, such as contention and waiting times, rise sharply and severely impair application performance. In this paper, we present a novel multithreaded model which isolates memory between threads by default and provides a higher level abstraction for scalable unicast or multicast communication between threads -- CHAUS （Channel for Unbounded Streaming）. The CHAUS model hides the underlying synchronization details, but requires the user to declare producer-consumer relationship of a channel in advance. It is the duty of the runtime system to ensure reliable data transmission at data item granularity as declared. To achieve unbounded buffer for streaming and reduce the synchronization overheads, we propose a virtual memory based solution to implement a scalable CHAUS channel. We check the programmability of CHAUS by successfully porting dedup and ferret from PARSEC as well as implementing MapReduce library with Phoenix-like API. The experimental results show that workloads built with CHAUS run faster than those with Pthreads, and CHAUS has the best scalability compared with two Pthread versions. There are three workloads whose CHAUS versions only spend no more than 0.17x runtime of Pthreads on both 16 and 32 cores. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1000-9000 1860-4749 |
| DOI: | 10.1007/s11390-017-1801-4 |