Chip multi-processor scalability for single-threaded applications
Author(s) -
Neil Vachharajani,
Matthew Iyer,
Chinmay Ashok,
Manish Vachharajani,
David I. August,
Daniel A. Connors
Publication year - 2005
Publication title -
acm sigarch computer architecture news
Language(s) - English
Resource type - Journals
eISSN - 1943-5851
pISSN - 0163-5964
DOI - 10.1145/1105734.1105741
Subject(s) - uniprocessor system , computer science , scalability , parallelism (grammar) , task parallelism , instruction level parallelism , parallel computing , limit (mathematics) , computer architecture , chip , embedded system , multiprocessing , telecommunications , mathematical analysis , mathematics , database
The exponential increase in uniprocessor performance has begun to slow. Designers have been unable to scale performance while managing thermal, power, and electrical effects. Furthermore, design complexity limits the size of monolithic processors that can be designed while keeping costs reasonable. Industry has responded by moving toward chip multi-processor architectures (CMP). These architectures are composed from replicated processors utilizing the die area afforded by newer design processes. While this approach mitigates the issues with design complexity, power, and electrical effects, it does nothing to directly improve the performance of contemporary or future single-threaded applications.This paper examines the scalability potential for exploiting the parallelism in single-threaded applications on these CMP platforms. The paper explores the total available parallelism in unmodified sequential applications and then examines the viability of exploiting this parallelism on CMP machines. Using the results from this analysis, the paper forecasts that CMPs, using the "intrinsic" parallelism in a program, can sustain the performance improvement users have come to expect from new processors for only 6-8 years provided many successful parallelization efforts emerge. Given this outlook, the paper advocates exploring methodologies which achieve parallelism beyond this "intrinsic" limit of programs.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom