SEARCH
ISTC-CC NEWSLETTER
RESEARCH HIGHLIGHTS
Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
ISTC-CC provides a listing of useful benchmarks for cloud computing.
Another list highlighting Open Source Software Releases.
Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
ISTC-CC Abstract
Cache Topology Aware Mapping of Stream Processing Applications onto CMPs
33rd IEEE International Conference on Distributed Computing Systems (ICDCS’13), July 2013.
Fang Zheng, Chitra Venkatramani*, Karsten Schwan, Rohit Wagle*
Georgia Institute of Technology
*IBM T. J. Watson Research Center
Data Stream Processing is an important class of data intensive applications in the Big Data” era. Chip Multi-Processors (CMPs) are the standard hosting platforms in modern data centers. Gaining high performance for stream processing applications on CMPs is therefore of great interest. Since the performance of stream processing applications largely depends on their effective use of the complex cache structure present on CMPs, this paper proposes the StreamMap approach for tuning streaming applications’ use of cache. Our major idea is to map application threads to CPU cores to facilitate data sharing AND mitigate memory resource contention among threads in a holistic manner. Applying StreamMap to the IBM’s System S middleware leads to improvements of up to 1.8× in the performance of realistic applications over standard Linux OS scheduler on three different CMP platforms.
FULL PAPER: pdf
