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Speedup stacks: Identifying scaling bottlenecks in multi-threaded applications
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| Venue: | in Proc. of the 2012 |
| Citations: | 5 - 2 self |
BibTeX
@INPROCEEDINGS{Eyerman_speedupstacks:,
author = {Stijn Eyerman and Kristof Du and Bois Lieven Eeckhout},
title = {Speedup stacks: Identifying scaling bottlenecks in multi-threaded applications},
booktitle = {in Proc. of the 2012},
year = {}
}
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Abstract
Multi-threaded workloads typically show sublinear speedup on multi-core hardware, i.e., the achieved speedup is not proportional to the number of cores and threads. Sublinear scaling may have multiple causes, such as poorly scalable synchronization leading to spinning and/or yielding, and interference in shared resources such as the lastlevel cache (LLC) as well as the main memory subsystem. It is vital for programmers and processor designers to understand scaling bottlenecks in existing and emerging workloads in order to optimize application performance and design future hardware. In this paper, we propose the speedup stack, which quantifies the impact of the various scaling delimiters on multithreaded application speedup in a single stack. We describe a mechanism for computing speedup stacks on a multi-core processor, and we find speedup stacks to be accurate within 5.1 % on average for sixteen-threaded applications. We present several use cases: we discuss how speedup stacks can be used to identify scaling bottlenecks, classify benchmarks, optimize performance, and understand LLC performance. 1
Keyphrases
speedup stack multi-threaded application sublinear speedup multithreaded application speedup sublinear scaling processor designer sixteen-threaded application main memory subsystem classify benchmark shared resource design future hardware multi-core hardware optimize performance multi-threaded workload multiple cause present several use case application performance single stack scalable synchronization understand llc performance lastlevel cache multi-core processor
