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Automated task distribution in multicore network processors using statistical analysis
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Symposium On Architecture For Networking And Communications Systems archive
Proceedings of the 3rd ACM/IEEE Symposium on Architecture for networking and communications systems table of contents
Orlando, Florida, USA
SESSION: Hardware table of contents
Pages 67-76  
Year of Publication: 2007
ISBN:978-1-59593-945-6
Authors
Arindam Mallik  Northwestern University
Yu Zhang  Northwestern University
Gokhan Memik  Northwestern University
Sponsors
SIGARCH: ACM Special Interest Group on Computer Architecture
ACM: Association for Computing Machinery
SIGCOMM: ACM Special Interest Group on Data Communication
Publisher
ACM  New York, NY, USA
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ABSTRACT

Chip multiprocessor designs are the most common types of architectures seen in Network Processors. As the Network Processors are used to implement increasingly complicated applications, task distribution among the cores is becoming an important problem. In this paper, we propose a new task allocation scheme for such architectures. This scheme relies on the inherent modular nature of the networking applications and intelligently distributes modules among different execution cores. Additionally, we selectively replicate modules to parallelize execution of tasks having longer processing time. We have developed a technique that uses the probability distribution of the execution times of different modules in the networking applications. The proposed schemes result in resource utilization of up to 95%, 89%, and 84% on average for the processors with 2, 4, and 8 cores, respectively. The schemes are highly scalable and can improve the throughput by 6.72 times for 8 core processors, aggregated over four representative applications. The combination of selective replication of modules and variation-aware task allocation result in up to 12.5% (9.9% on average) performance improvement as compared to a scheme based on just mean processing time.


REFERENCES

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Collaborative Colleagues:
Arindam Mallik: colleagues
Yu Zhang: colleagues
Gokhan Memik: colleagues