1 research outputs found
Providing High and Controllable Performance in Multicore Systems Through Shared Resource Management
Multiple applications executing concurrently on a multicore system interfere
with each other at different shared resources such as main memory and shared
caches. Such inter-application interference, if uncontrolled, results in high
system performance degradation and unpredictable application slowdowns. While
previous work has proposed application-aware memory scheduling as a solution to
mitigate inter-application interference and improve system performance,
previously proposed memory scheduling techniques incur high hardware complexity
and unfairly slowdown some applications. Furthermore, previously proposed
memory-interference mitigation techniques are not designed to precisely control
application performance.
This dissertation seeks to achieve high and controllable performance in
multicore systems by mitigating and quantifying the impact of shared resource
interference. First, towards mitigating memory interference and achieving high
performance, we propose the Blacklisting memory scheduler that achieves high
performance and fairness at low complexity. Next, towards quantifying the
impact of memory interference and achieving controllable performance in the
presence of memory bandwidth interference, we propose the Memory Interference
induced Slowdown Estimation (MISE) model. We propose and demonstrate two use
cases that can leverage MISE to provide soft performance guarantees and high
overall performance/fairness. Finally, we seek to quantify the impact of shared
cache interference on application slowdowns, in addition to memory bandwidth
interference. Towards this end, we propose the Application Slowdown Model
(ASM). We propose and demonstrate several use cases of ASM that leverage it to
provide soft performance guarantees and improve performance and fairness.Comment: CMU PhD Thesi