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Coz: Finding Code that Counts with Causal Profiling


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Improving performance is a central concern for software developers. To locate optimization opportunities, developers rely on software profilers. However, these profilers only report where programs spend their time: optimizing that code may have no impact on performance. Past profilers thus both waste developer time and make it difficult for them to uncover significant optimization opportunities.

This paper introduces causal profiling. Unlike past profiling approaches, causal profiling indicates exactly where programmers should focus their optimization efforts, and quantifies their potential impact. Causal profiling works by running performance experiments during program execution. Each experiment calculates the impact of any potential optimization by virtually speeding up code: inserting pauses that slow down all other code running concurrently. The key insight is that this slowdown has the same relative effect as running that line faster, thus "virtually" speeding it up.

We present Coz, a causal profiler, which we evaluate on a range of highly-tuned applications such as Memcached, SQLite, and the PARSEC benchmark suite. Coz identifies previously unknown optimization opportunities that are both significant and targeted. Guided by Coz, we improve the performance of Memcached by 9%, SQLite by 25%, and accelerate six PARSEC applications by as much as 68%; in most cases, these optimizations involve modifying under 10 lines of code.

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1. Introduction

Improving performance is a central concern for software developers. While compiler optimizations are of some assistance, they often do not have enough of an impact on performance to meet programmers' demands.2 Programmers seeking to increase the throughput or responsiveness of their applications thus must resort to manual performance tuning.

Manually inspecting a program to find optimization opportunities is impractical, so developers use profilers. Conventional profilers rank code by its contribution to total execution time. Prominent examples include oprofile, perf, and gprof.7,9,11 Unfortunately, even when a profiler accurately reports where a program spends its time, this information can lead programmers astray. Code that runs for a long time is not necessarily a good choice for optimization. For example, optimizing code that draws a loading animation during a file download will not make the program run faster, even though this code runs just as long as the download.


 

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