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Locating Cache Performance Bottlenecks Using Data Profiling
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@MISC{Pesterev_locatingcache,
author = {Aleksey Pesterev and Nickolai Zeldovich and Robert T. Morris},
title = {Locating Cache Performance Bottlenecks Using Data Profiling},
year = {}
}
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Abstract
Effective use of CPU data caches is critical to good performance, but poor cache use patterns are often hard to spot using existing execution profiling tools. Typical profilers attribute costs to specific code locations. The costs due to frequent cache misses on a given piece of data, however, may be spread over instructions throughout the application. The resulting individually small costs at a large number of instructions can easily appear insignificant in a code profiler’s output. DProf helps programmers understand cache miss costs by attributing misses to data types instead of code. Associating cache misses with data helps programmers locate data structures that experience misses in many places in the application’s code. DProf introduces a number of new views of cache miss data, including a data profile, which reports the data types with the most cache misses, and a data flow graph, which summarizes how objects of a given type are accessed throughout their lifetime, and which accesses incur expensive cross-CPU cache loads. We present two case studies of using DProf to find and fix cache performance bottlenecks in Linux. The improvements provide a 16–57 % throughput improvement on a range of memcached and Apache workloads.
Keyphrases
data type cache miss effective use new view small cost large number code profiler output poor cache use pattern good performance data profile cpu data cache apache workload case study execution profiling tool typical profilers data structure cache performance bottleneck cache miss data data flow graph frequent cache miss application code many place specific code location expensive cross-cpu cache load cache miss cost throughput improvement
