LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Kunz, Georg; Schemmel, Daniel; Gross, James; Wehrle, Klaus (2012)
Publisher: Mobile Network Performance Group
Languages: English
Types: Conference object
Subjects: event aggregation, external parallelism, GP-GPU, internal parallelism, latency hiding, parameter studies, PDES, Communication Systems, Kommunikationssystem, Telecommunications, Telekommunikation, Computer Engineering, Datorteknik
eveloping complex technical systems requires a systematic exploration of the given design space in order to identify optimal system configurations. However, studying the effects and interactions of even a small number of system parameters often requires an extensive number of simulation runs. This in turn results in excessive runtime demands which severely hamper thorough design space explorations. In this paper, we present a parallel discrete event simulation scheme that enables cost- and time-efficient execution of large scale parameter studies on GPUs. In order to efficiently accommodate the stream-processing paradigm of GPUs, our parallelization scheme exploits two orthogonal levels of parallelism: External parallelism among the inherently independent simulations of a parameter study and internal parallelism among independent events within each individual simulation of a parameter study. Specifically, we design an event aggregation strategy based on external parallelism that generates workloads suitable for GPUs. In addition, we define a pipelined event execution mechanism based on internal parallelism to hide the transfer latencies between host- and GPU-memory. We analyze the performance characteristics of our parallelization scheme by means of a prototype implementation and show a 25-fold performance improvement over purely CPU-based execution.

QC 20131218

  • No references.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Collected from