Scott mahlke phd thesis introduction

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Skip to main content. Log In Sign Up. Instrumentation Sampling for Profiling Datacenter Applications.

Hardware performance scott mahlke phd thesis introduction Categories and Subject Descriptors D. Optimization, Run-time environments been proven to be effective to help programmers find code General Terms Languages, Performance regions to optimize by monitoring datacenter applications continuously on live traffic.

However, these hardware fea- Keywords Profiling, Instrumentation, Datacenters tures are inflexible and often buggy, limiting the types of data that can be more info. Introduction can complement or replace hardware functionality by pro- As cloud computing continues to expand, profile-guided op- viding more flexible scott mahlke phd thesis introduction targeted information scott mahlke phd. Un- timization PGO on datacenter applications has the poten- fortunately, the overhead of existing instrumentation mech- tial for huge cost savings.

Single-digit performance gains anisms prevents their use in production runs. In order to be from the compiler can yield tens of millions of dollors in used in datacenters, we need scott mahlke phd thesis introduction profiling mechanism to im- savings.

Isolating the execution thesis introduction datacenter applications pose overheads see more less than scott mahlke phd thesis introduction few percent, in terms of both can be complex or even impossible. One challenge of PGO throughput and latency, while still generating meaningful here datacenter applications is collecting profile data scott mahlke phd thesis introduction the profile data.

In order to moni- This paper presents instant profiling, an instrumenta- thesis introduction production runs, the profiling overhead in terms of both tion sampling technique using dynamic binary translation.

First and foremost, datacenter application owners filing /quotes-about-writing-essays-yale.html interleaves native execution and instru- are not scott mahlke phd thesis introduction of latency degradations even at the 99th mented execution according to configurable profiling dura- percentile of more than a few percent, unlike high perfor- tion and frequency parameters.

Scott mahlke phd thesis introduction further reduces the latency mance computing or other throughput-oriented applications, degradation thesis introduction initial profiling phases by pre-populating a because they hurt the quality of service.

Second, excessive software code cache. We evaluate the performance and ef- profiling overhead can cause observer distortion that thwarts fectiveness of this new profiling technique on the SPEC meaningful analysis.

We show that it is well-suited for deployment One way to keep the profiling scott mahlke phd thesis introduction minimal is to exploit hardware support.

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Furthermore, many recent microprocessor on the first page. Also, we head, they scott mahlke phd thesis introduction from limitations. First, the possible types do not duplicate the original execution, unlike other prior of profile thesis introduction are inherently defined by the features that the work [21, 29].

For these reasons, instant profiling can underlying microprocessor supports; thus, hardware profil- keep the computational overhead minimized. Due scott mahlke phd thesis introduction the overhead amor- nisms. In addition, PMU features are often very processor- tizing characteristics of dynamic translation techniques, specific, making profiling tools not portable.

Lastly, as the end users might observe significant latency degradation top design priorities are hardware validation and proces- for initial profiling phases even with low article source rates.

With sampling techniques, limit thesis introduction potential of Scott mahlke for datacenter applications, since PGO systems must be aware of both what and how to introduction we scott mahlke phd thesis introduction avoid making errors on profile data. Since our timize for effective optimization. Although PMUs imple- low overhead framework enables continuous thesis introduction on mented in recent microprocessors so far provide quite rich here runs, however, the accuracy of instant profil- information on where to focus optimization efforts, deciding ing gets closer to full profiling with a long enough appli- how to optimize is a considerably harder problem.

For exam- cation lifetime or thesis introduction instances. Since the most im- ple, sampling the program counter PC at a high rate yields portant applications consume /how-to-write-a-speech-in-mla-format.html most cycles, they will scott mahlke phd thesis introduction information to detect hot code, and scott mahlke phd thesis introduction PMUs have the most instances, run the longest, and yield the are even capable of giving finer information such as cache scott mahlke phd thesis introduction profiles.

Instant profiling can be article source to any type of far give phd thesis attention on how to optimize.

In addition, scott mahlke phd thesis introduction profiling is than hardware-based mechanisms. For instance, path pro- portable to other micro-architectures for the same reason. Other techniques such as value profiling [8] and data formation and overhead. Such high overheads prevent these mechanisms from tion 2 provides a scott mahlke phd thesis introduction explanation of dynamic instrumenta- consideration for profiling even loadtests for datacenter ap- tion systems and DynamoRIO which we harness as a base plications.

Section 3 then presents the design and implemen- In this work, we propose a novel instrumentation sam- tation details of our instant profiling framework. Section 4 pling technique, instant profiling, that uses dynamic binary describes how the framework further reduces latency degra- translation.

Instead of instrumenting the entire execution, in- dation by pre-populating its software scott mahlke phd thesis introduction cache. Section 5 stant profiling periodically interleaves native execution and explores tuning tradeoffs and evaluates performance.

By adjusting profiling duration and tion 6 discusses related work, followed by Section 7 outlin- frequency parameters, we can keep profiling overhead un- ing future work. Finally, we summarize the contributions and der a few percent, so that the framework can be used to scott mahlke phd thesis introduction in Section 8.

We have imple- mented the prototype framework of instant profiling on top thesis introduction. Background of DynamoRIO [6], scott mahlke phd we evaluate the possibility of con- tinuous profiling on real datacenter benchmarks.

Before we delve into scott mahlke phd thesis introduction details of instant profiling, we Instant profiling offers the following features: Computational overhead an overview of DynamoRIO upon which we implement the includes the thesis introduction consumed by the click here as well prototype framework of continue reading profiling.

Scott mahlke phd thesis introduction are many dynamic binary instru- mentation systems [6, 19, 22], sharing similar internal mech- anisms. There are two major sources of overhead for dynamic bi- scott mahlke phd thesis introduction instrumentation systems.

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