Modern computers leverage multi-core or many-core processors to accelerate parallel applications. Unfortunately, the speedup of these applications is typically far from ideal, due to some hidden scalability issues. Previous research mainly focuses on application code to identify scalability bottlenecks, neglecting the fact that the application code interacts with numerous external components, including memory allocator, third-party runtime libraries, and the operating system. Understanding and fixing scalability problems should hence go beyond application code and consider the whole software stack. The project's novelties are to pinpoint scalability culprits hidden in different components of the whole stack and automatically fix the scalability bottlenecks. The project's impacts are significantly improved performance for applications running on multi-core processors and thus accelerated scientific discoveries and energy saving.

This project aims to systematically pinpoint and resolve latent software contention in all components of the whole software stack from user space. The proposed approaches are urgent due to the pervasive use of multi-core and many-core hardware. Also, according to Amdahl's law, a small degree of latent contention in any of the components may substantially limit the speedup potential on these modern hardware. The research plans to design low-overhead profilers to obtain runtime information for system calls, memory allocator behaviors, and all interacting events between components, as well as analyzers to automatically pinpoint the root causes of scalability bottlenecks. Through a runtime optimizer, the research aims to fix the identified scalability issues without intervention from the programmer. The project has potential to dramatically reduce manual effort for software optimization and improve performance for parallel applications on modern hardware.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Agency
National Science Foundation (NSF)
Institute
Division of Computer and Communication Foundations (CCF)
Type
Standard Grant (Standard)
Application #
1823004
Program Officer
Anindya Banerjee
Project Start
Project End
Budget Start
2018-10-01
Budget End
2020-04-30
Support Year
Fiscal Year
2018
Total Cost
$499,992
Indirect Cost
Name
University of Texas at San Antonio
Department
Type
DUNS #
City
San Antonio
State
TX
Country
United States
Zip Code
78249