Check in the draft of the introduction chapter.

abstract.tex

@@ -2,40 +2,38 @@
 \addcontentsline{toc}{chapter}{Abstract}
 \vspace{-1em}
 %context: connection between profiling and optimizations.
-To understand and improve application performance, developers spend a lot of
-time on observing problematic runtime behaviors of computer
-applications. Understanding the root causes of the performance problems is just
+To understand and improve software performance, developers spend a lot of
+time on observing problematic run-time behaviors of computer
+applications. Understanding root causes of performance problems is
 the first step to improving performance. The state of the art is continuous
 sample-based profiling. These systems periodically sample software and hardware
 events and analyze samples to help developers discover opportunities for
 improvements. As for any sampling process, its fidelity depends on the
-sample rate. The higher sample rates profilers can achieve, then the finer-grain
-behaviors they can observe. The sampling rate gates software and hardware
+sample rate. The higher the sample rates profilers can achieve, then the
+finer-grain behaviors they can observe. Thus, sample rates gate software and hardware
 innovations. If you cannot observe it, you cannot improve it.
 
 
 %problem:
 
-Unfortunately, the sampling rate of current profilers is too low. Despite the GHz
+Unfortunately, the sampling rate of current profilers is too low. Despite the gigahertz
 speeds of modern processors, the sampling frequency of
-current continuous profilers has been at a standstill --- between 1 KHz and 100
-KHz. The Million cycles gap between two sequential samples prevents the
+current continuous profilers has been at a standstill --- between\,1 KHz and
+100\,KHz. The million cycle gap between two sequential samples prevents the
 profilers from observing fine-grain behaviors, therefore, missing many optimization
-opportunities.
+opportunities. The history of science shows that an order of magnitude or more improvement in
+measurement fidelity has always led to fundamental new discoveries. 
 
-
-The history of science shows that an order of magnitude or more improvement in
-measurement fidelity always leads to fundamental new
-discoveries. My thesis is that increasing the sample rates of continuous
-hardware and software profilers by orders of magnitude will lead to fundamental discoveries of new
-behaviors, accurate root causes, and sound optimizations.
+%Block lock
+\textbf{My thesis is that sample rates of continuous hardware and software profilers can be increased by orders of magnitude leading
+to fundamental discoveries of new behaviors, accurate root causes, and sound optimizations.}
 
 
 
 %contributions
 The key contributions of this thesis are: 1) \shim, a continuous profiler that samples
 at resolutions as fine as 15 cycles; three to five orders of magnitude finger
-than current continuous profilers. 2) Tailer, a tail latency analyzer that
+than current continuous profilers. 2) Tailor, a tail latency analyzer that
 profiles web services using SHIM profiler and analyzes the root causes of long
 tail latency. 3) Elfen, a job scheduler based on the sampling ideas in SHIM, that
 borrows idle cycles from underutilized SMT cores for batch workloads without
@@ -43,10 +41,9 @@ interfering with latency-critical requests.
 
 
 %significance
-My thesis fundamentally alters what software and hardware signals are observable on existing systems, develops
+My thesis fundamentally alters which software and hardware signals are observable on existing systems, develops
 new approaches of analyzing large volumes of profiling data, and
-demonstrates that high frequency profiling has the potential to stimulate new software and
-hardware optimizations.
+demonstrates that high frequency profiling stimulate new software and hardware optimizations.
 
 %%% Local Variables: 
 %%% mode: latex

elfen.tex

+\chapter{Elfen}
+\label{cha:elfen}
+\section{Summary}

intro.tex

 \chapter{Introduction}
 \label{cha:intro}
+%point out the problem
+This thesis addresses the challenge of
+significantly increasing sample rates of continuous hardware and software
+profilers and the opportunities of using the new profiling technique to discover
+new optimizations.
 
+\section{Problem Statement}
+\label{sec:problemstatement}
 
-\section{Thesis Statement}
-\label{sec:thesisstatement}
-I believe A is better than B.
+%observing and optimizations
+Optimizing software is challenging for developers but also extremely rewarding. To design
+sound optimizations, developers have to understand root causes of performance
+problems, otherwise, imagined optimizations rarely work. Most of the time, the
+root causes are not obvious. The better developers can
+observe behaviors of software and hardware, the easier they can discover hidden root
+causes. 
 
 
-\section{Introduction}
-\label{sec:problemstatement}
-Put your introduction here. You could use \textbackslash fix\{ABCDEFG.\} to
-leave your comments, see the box at the left side. \fix{You have to rewrite your
-thesis!!!} 
+%higher sampling rate -> observating better
+Among observing techniques, sample-based continuous profiling that periodically
+samples hardware and software events is the most popular
+and convenient one. Developers pose hypotheses and configure these profilers to sample interesting events
+expecting that sequential samples reveal signals of problematic run-time
+behaviors, and that correlations between the events provide information about
+where root causes hide. As shown in the Nyquist-Shannon sampling theorem, to
+recover the full information of a continuous signal with the frequency B, the
+signal has to be sampled at least at frequency 2B. Many interesting software and hardware behaviors occur with extremely
+high frequencies, for example, the current executing function may change every a few
+hundred cycles. If developers want to observe these behaviors directly, they better have a high sample-rate profiler.
+
+
+%limitation of current profilers
+In last four decades, following Moore's law, the frequency of processors has been increased
+from megahertz to gigahertz, and software and hardware have been becoming
+more and more complex. However, Moore's law has little impact on
+continuous profiling. Today, on gigahertz processors, the sampling frequency
+of state-of-the-art continuous profiling tools, such as Intel Vtune and Linux
+perf, is fixed at 1\,KHz by default and can be tuned up to 100\,KHz maximally. Such low sampling frequency
+gives developers a low fidelity view that binds developers to root causes.  We
+use an example to demonstrate if we can improve sample rates by orders of magnitude, a high fidelity view
+that is fundamentally better than low sampling rates can be shown to
+developers. Variations in instruction per cycle (IPC) is an important behavior
+for developers to understand how software utilizes CPU resources.  Figure~\ref{fig:ipcTimeline}
+shows IPC timelines for a same short period of \lusearch benchmark at three
+sampling frequencies: 1 KHz, 100 KHz, and 10 MHz, on a 4-way 3.4\,GHz Intel
+i7-4700K processors. For each sample, two hardware performance counters, the number of cycles
+and the retired instruction, are read to calculate the sampling period's average IPC from two consecutive samples. Sampling at 100\,KHz shows IPC varying slowly over time
+whereas sampling at 10\,MHz reveals substantial high-frequency variations in IPC. 
+
+\begin{figure}
+\includegraphics[width=\columnwidth]{./figs/intro-IPC-timeline.pdf}
+\caption{ IPC timeline for Lusearch.  Sampling with 10 MHz exposes behavior unseen by existing profilers (red, blue).\label{fig:ipcTimeline}}
+\label{fig:ipcTimeline}
+\end{figure}
+
+\section{Opportunities and Contributions}
+From discovering bacteria to identifying the DNA structure, uncountable examples in
+the history of science show improvements in measurement techniques always unlock
+fundamental new discoveries. The aim of this thesis is to demonstrate that this is true for
+continuous profiling.
+
+This thesis presents \shim, a continuous profiler sampling at resolutions orders
+of magnitude finer than existing profilers, and two optimizations based upon on
+\shim: (1) \elfen, a job scheduler improves the datacenter utilization
+significantly, and (2) \tailor, a tail latency analyzer that helps developers to diagnose and eliminate long tail latency problems of web services.
+
+\subsection{SHIM}
+%problem
+The reason why sample rates of exiting profiling tools are low is that they take
+an interrupt and then sample software and hardware events~\citep{vtune:intel, perf:wiki}. The
+possibility of over-whelming the kernel's capacity to service interrupts places
+practical limits on their maximum resolution~\citep{perfwarn:source}. This interrupt-based
+sampling technique was deeply rooted in single-core processors. Today, the
+multicore processors are everywhere. They provide an opportunity to remove this
+limitation by freeing sampling from interrupting. On multicore processors, an observer thread on one
+core can observe another core's software and hardware events directly without
+sending an interrupt to the observed core, thus, the sampling
+frequency is only limited by how fast the observer thread reads the events. 
+
+In this thesis, we present \shim, a continuous profiler that samples at resolutions
+as fine as 15 cycles, orders of magnitude finer than existing profilers. A \shim
+observer thread executes simultaneously with the application thread it
+observes, but on a separate hardware context, exploiting unutilized hardware on a different
+core or on the same core with Simultaneous Multithreading (SMT).  
+ Instead of using interrupts or inserting instrumentation, which
+ substantially perturb applications, \shim efficiently observes
+events in a separate thread by simply reading hardware counters and
+memory locations. \shim views time-varying software and hardware events as
+\emph{signals}.   A \shim observer thread reads hardware
+performance counter signals and memory locations that store software signals
+(e.g., method and loop identifiers). \shim treats software and hardware
+data uniformly by reading (sampling) memory locations and
+performance counters together at very high frequencies, e.g., 10s to 1000s of
+cycles.  The observer thread logs and/or aggregates 
+signals. Further online or offline analysis acts on this
+data. The sampling frequency of \shim is not limited by interrupt handling, but
+only by reading performance counters and memory locations.
+
+% Just as in signal processing, high frequency sampling of rates, such as IPC,
+% is subject to noise. For instance,  slow reads or interrupts may disturb
+% the sampling measurement code and thus skew results at
+% extremely high frequencies,
+% To improve the fidelity of rate
+% metrics, we introduce \emph{double-time error correction}
+% (\dte), which automatically identifies and discards noisy samples by
+% taking redundant timing measurements.  \dte separately measures the
+% period between the start of two consecutive samples and the period
+% between the end of the samples. It uses the global clock as the ground
+% truth.  Both measurements observe the same application period and one of the
+% two sampling periods. Since each sampling period is a fixed number of instructions,
+% code, all sampling periods should take the same amount of time. Thus if the periods differ, the measurement was perturbed and \dte
+% discards it. 
+
+\subsection{Elfen}
+%problem
+Web services from search to games to stock trading impose strict
+Service Level Objectives (SLOs) on tail latency. Meeting these
+objectives is challenging because the computational demand of each request is
+highly variable and load is bursty. % and diurnal.  
+Consequently, many servers run at
+low utilization (10 to 45\%); turn off simultaneous
+multithreading (SMT); and execute only a single service --- wasting
+hardware, energy, and money. Although co-running batch jobs with
+latency critical % web service 
+\emph{requests} to utilize multiple SMT hardware contexts (lanes)  is
+appealing, unmitigated sharing of core resources induces
+non-linear effects on tail latency and SLO violations.
+
+Since interactive services are widely deployed in many
+datacenters, their poor utilization incurs enormous commensurate
+capital and operating costs. Even small improvements 
+substantially improve profitability.
+
+
+%elfen
+In this thesis, we introduce principled borrowing, a scheduling technique based
+on \shim, that dynamically identifies idle cycles and
+run batch workloads by borrowing hardware resources from latency critical
+workloads without violating SLOs. The
+\emph{\Elfen} scheduler executes secondary batch threads
+in a reserved  SMT \emph{batch lane} mutually exclusively with latency-critical primary
+requests which execute in a distinct SMT \emph{request lane}. We instrument
+batch threads with \shim sampling instructions that continuously monitor
+paired request lanes. Batch threads start to execute only when their paired request lane is idle, quickly
+stepping out of the way when the budget is exhausted.
 
+\subsection{Tailor}
+%problem
 
 
-\section{Thesis Outline}
+\section{Thesis Structure}
 \label{sec:outline}
-How many chapters you have? You may have Chapter~\ref{cha:background},
-Chapter~\ref{cha:design}, Chapter~\ref{cha:methodology},
-Chapter~\ref{cha:result}, and Chapter~\ref{cha:conc}.
+The body of this thesis is structured around the three key contributions
+outlined above. Chapter~\ref{cha:shim} explains the \shim
+profiler, Chapter~\ref{cha:elfen} discusses the \elfen scheduler, and
+Chapter~\ref{cha:tailor} introduces the Tailor analyzer. 
+Finally, Chapter~\ref{cha:conc} concludes the thesis, summarizing how my
+contributions address the challenge of substantially improving continuous
+profiling and the opportunities of discovering new optimizations, and projecting
+future works.

macros.tex

@@ -19,16 +19,57 @@
 % Your macros                                                                  %
 \newcommand{\ttool}{\textsc{Shim}\xspace}
 \newcommand{\tool}{\textsc{Shim}\xspace}
+\newcommand{\Nnap}{Nanonap\xspace}
+\newcommand{\nnap}{\textcode{nanonap}\xspace}
+\newcommand{\nnaps}{\textcode{nanonaps}\xspace}
 \newcommand{\shim}{\textsc{Shim}\xspace}
+\newcommand{\elfen}{\textsc{Elfen}\xspace}
+\newcommand{\Elfen}{\textsc{Elfen}\xspace}
+\newcommand{\relay}{\textsc{Elfen}\xspace}
+\newcommand{\Relay}{\textsc{Elfen}\xspace}
+\newcommand{\oursystem}{\textsc{Elfen}\xspace}
+\newcommand{\shimbatch}{\textsc{Elfen}\xspace}
 \newcommand{\dte}{\textsc{DTE}\xspace} % DTR: double-time
-                                % noise reduction
-\newcommand{\anr}{\textsc{DTE}\xspace} %: automatic noise reduction (until we think of something better).
+\newcommand{\anr}{\textsc{DTE}\xspace} %: automatic noise reduction (until we
+                                %think of something better).
+\newcommand{\tailor}{\textsc{Tailor}\xspace}
 
 
+\newcommand*{\textbm}[1]{\textsf{#1}}
+
+\newcommand{\specjvm}{\textbm{SPECjvm}\xspace}
+\newcommand{\jess}{\textbm{jess}\xspace}
+\newcommand{\raytrace}{\textbm{raytrace}\xspace}
+\newcommand{\db}{\textbm{db}\xspace}
+\newcommand{\javac}{\textbm{javac}\xspace}
+\newcommand{\jack}{\textbm{jack}\xspace}
+\newcommand{\compress}{\textbm{compress}\xspace}
+\newcommand{\mpegaudio}{\textbm{mpegaudio}\xspace}
+\newcommand{\mtrt}{\textbm{mtrt}\xspace}
+\newcommand{\jbb}{\textbm{jbb2000}\xspace}
+\newcommand{\specjbb}{\textbm{SPECjbb2000}\xspace}
+\newcommand{\newspecjbb}{\textbm{SPECjbb2005}\xspace}
+\newcommand{\psjbb}{\textbm{pseudojbb}\xspace}
+\newcommand{\pjbb}{\textbm{pjbb2005}\xspace}
+\newcommand{\dacapo}{\textbm{DaCapo}\xspace}
+\newcommand{\dacapover}{\textbm{DaCapo v.06-10-MR2}\xspace}
+\newcommand{\antlr}{\textbm{antlr}\xspace}
+\newcommand{\bloat}{\textbm{bloat}\xspace}
+\newcommand{\eclipse}{\textbm{eclipse}\xspace}
+\newcommand{\fop}{\textbm{fop}\xspace}
+\newcommand{\hsqldb}{\textbm{hsqldb}\xspace}
+\newcommand{\jython}{\textbm{jython}\xspace}
+\newcommand{\pmd}{\textbm{pmd}\xspace}
+\newcommand{\xalan}{\textbm{xalan}\xspace}
+\newcommand{\chart}{\textbm{chart}\xspace}
+\newcommand{\luindex}{\textbm{luindex}\xspace}
+\newcommand{\lusearch}{\textbm{lusearch}\xspace}
+\newcommand{\lusearchfix}{\textbm{lusearch-fix}\xspace}
+\newcommand{\avrora}{\textbm{avrora}\xspace}
+\newcommand{\sunflow}{\textbm{sunflow}\xspace}
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 % Here's an example abbreviation macro. Notice the \xspace at the end, to ensure
 % it gets the right spacing after it.
 \newcommand{\jikesrvm}{Jikes RVM\xspace}
 % We can also make things sans-serif, which might be better style.
-\newcommand{\avrora}{\textsf{avrora}\xspace}

shim.tex

+\chapter{SHIM}
+\label{cha:shim}
+\section{Summary}

tailor.tex

+\chapter{Tailor}
+\label{cha:tailor}
+\section{Summary}

thesis.tex

@@ -63,10 +63,9 @@
 \input{intro}
 
 %% Chapters
-\input{background}
-\input{design}
-\input{methodology}
-\input{results}
+\input{shim}
+\input{elfen}
+\input{tailor}
 \input{conclusion}
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%