Skip to main content
USENIX
  • Conferences
  • Students
Sign in
  • Home
  • Attend
    • Registration Information
    • Registration Discounts
    • Venue, Hotel, and Travel
    • Students and Grants
    • Co-located Events
      • HotCloud '15
      • HotStorage '15
  • Program
    • At a Glance
    • Technical Sessions
  • Activities
    • Birds-of-a-Feather Sessions
    • Poster Session
  • Participate
    • Call for Papers
    • Call for Practitioner Talks
    • Instructions for Participants
  • Sponsorship
  • About
    • Conference Organizers
    • Questions
    • Services
    • Help Promote
    • Past Conferences
  • Home
  • Attend
  • Program
  • Activities
  • Participate
  • Sponsorship
  • About

sponsors

Gold Sponsor
Gold Sponsor
Gold Sponsor
Gold Sponsor
Silver Sponsor
Bronze Sponsor
Bronze Sponsor
Bronze Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Industry Partner
Industry Partner

help promote

USENIX ATC '15 button

Get more
Help Promote graphics!

connect with us


  •  Twitter
  •  Facebook
  •  LinkedIn
  •  Google+
  •  YouTube

twitter

Tweets by @usenix

usenix conference policies

  • Event Code of Conduct
  • Conference Network Policy
  • Statement on Environmental Responsibility Policy

You are here

Home » Data Sharing or Resource Contention: Toward Performance Transparency on Multicore Systems
Tweet

connect with us

Data Sharing or Resource Contention: Toward Performance Transparency on Multicore Systems

Authors: 

Sharanyan Srikanthan, Sandhya Dwarkadas, and Kai Shen, University of Rochester

Abstract: 

Modern multicore platforms suffer from inefficiencies due to contention and communication caused by sharing resources or accessing shared data. In this paper, we demonstrate that information from low-cost hardware performance counters commonly available on modern processors is sufficient to identify and separate the causes of communication traffic and performance degradation. We have developed SAM, a Sharing-Aware Mapper that uses the aggregated coherence and bandwidth event counts to separate traffic caused by data sharing from that due to memory accesses. When these counts exceed pre-determined thresholds, SAM effects task to core assignments that colocate tasks that share data and distribute tasks with high demand for cache capacity and memory bandwidth. Our new mapping policies automatically improve execution speed by up to 72% for individual parallel applications compared to the default Linux scheduler, while reducing performance disparities across applications in multiprogrammed workloads.

Sharanyan Srikanthan, University of Rochester

Sandhya Dwarkadas, University of Rochester

Kai Shen, University of Rochester

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

Srikanthan PDF
Srikanthan PDF (Updated 7/8/15)
View the slides

Presentation Video 

Presentation Audio

MP3 Download

Download Audio

  • Log in or    Register to post comments

Gold Sponsors

Silver Sponsors

Bronze Sponsors

Media Sponsors & Industry Partners

Open Access Publishing Partner

© USENIX

  • Privacy Policy
  • Contact Us