+ dave's comments

Rizwana Begum
1 parent 44d949a6
Showing 6 changed files with 26 additions and 24 deletions
inefficiency.tex
inefficiency_speedup.tex
introduction.tex
optimal_performance.tex
performance_clusters.tex
system_methodology.tex
@@ -180,13 +180,13 @@ consumption.
 %CPU2006 benchmarks with highest error of 10\% except for $gobmk (18\%)$ and $lbm
 %(24\%)$. 
 %%%%% END OF MODEL %%%%%%
-In this work we demonstrate how to use inefficiency, deferring predicting and 
+In this work we demonstrate how to use inefficiency and defer both predicting and 
 optimizing $E_{min}$ to future work.
 \subsection{Managing Inefficiency}
 %
 Future energy management algorithms need to tune system settings to keep the
-system within specified inefficiency budget and deliver the best performance. 
+system within the specified inefficiency budget and deliver the best performance. 
 %
 Techniques that use predictors such as instructions-per-cycle (IPC) to decide
 when to use DVFS or migrate threads can be extended to operate under given
@@ -202,7 +202,7 @@ under performance constraints, some have the potential to be modified to work
 under energy constraints and thus could operate under 
 inefficiency budget~\cite{bitirgen2008coordinated,deng2012coscale,chen2011coordinating,fan2005synergy,felter2005performance,li2007cross,raghavendra2008no}.
 %
-We leave building some of these algorithms into a system as future work.
+We leave incorporating some of these algorithms into a system as future work.
 %
 In this paper, we characterize the optimal performance point under different
 inefficiency constraints and illustrate that the stability of these points
@@ -27,7 +27,7 @@ Figure~\ref{heatmaps} plots the speedup and inefficiency for three workloads
 operating with various CPU and memory frequencies. As the figure shows, the
 ability of a workload to trade-off energy and performance using CPU and memory
 frequency, depends on its mix of CPU and memory instructions. For CPU intensive
-workloads like \textit{bzip2}, speedup varies with only CPU frequency, and
+workloads like \textit{bzip2}, speedup varies only with CPU frequency;
 memory frequency has no impact on speedup. For workloads that have balanced CPU
 and memory intensive phases like \textit{gobmk}, speedup varies with both CPU
 and memory frequency. The \textit{milc} benchmark has some memory intensive
@@ -44,7 +44,7 @@ We make three major observations:
 efficiently.} At the lowest frequencies, 100MHz and 200MHz for CPU and
 memory respectively, \textit{gobmk} takes the longest to execute. These settings slow down the application so much
 that its overall energy consumption increases, thereby resulting in 
-inefficiency of 1.55 for \textit{gobmk}. Algorithms that choose these frequency settings spend
+inefficiency of 1.55. Algorithms that choose these frequency settings spend
 55\% more energy without any performance improvement.
 %The converse is also true
 %as noted by our second observation.
@@ -19,10 +19,10 @@ Still other hardware energy-performance tradeoffs are on the horizon, arising
 from capabilities such as memory frequency scaling~\cite{david2011memory} and nanosecond-speed DVFS
 emerging in next-generation hardware designs~\cite{6084810}. 
-We envision a next-generation smartphone capable of scaling both voltage and
-frequency of CPU and only frequency of memory.
+We envision a next-generation smartphone capable of CPU DVFS (Dynamic Voltage
+and Frequency Scaling) and memory DFS (Dynamic Frequency Scaling).
 %
-While the addition of memory DVFS can be used to improve energy-constrained
+While the addition of memory DFS can be used to improve energy-constrained
 performance, the larger frequency state space compared to CPU DVFS alone also
 provides more incorrect settings that waste energy or degrade performance.
 %
@@ -33,7 +33,7 @@ energy constraints.
 Our work represents two advances over previous efforts.
 %
 First, while previous works have explored energy minimizations using DVFS
-under performance constraints focusing on reducing slack~\cite{deng2012coscale}, we are the first to
+under performance constraints focusing on reducing slack, we are the first to
 study the potential DVFS settings under an energy constraint.
 %
 Specifying performance constraints for servers is appropriate, since they are
@@ -75,7 +75,7 @@ performance.
 %
 \item We study the energy-performance trade-offs of systems that are capable
 of both CPU and memory DVFS for multiple applications. We show that poor
-frequency selection can both hurt performance and energy consumption.
+frequency selection can hurt both performance and energy consumption.
 %
 \item We characterize the optimal frequency settings for multiple
 applications and inefficiency budgets. We introduce \textit{performance
@@ -87,10 +87,10 @@ management algorithms.
 %
 \end{enumerate}
-We use the \texttt{Gem5} simulator, the Android smartphone platform and Linux
+We use the \texttt{gem5} simulator, the Android smartphone platform and Linux
 kernel, and an empirical power model to (1) measure the inefficiency of
 several applications for a wide range of frequency settings, (2) compute
-performance clusters, and (3) study how they evolve.
+performance clusters, and (3) study how performance clusters evolve.
 %
 We are currently constructing a complete system to study tuning algorithms
 that can build on our insights to adaptively choose frequency settings at
@@ -107,7 +107,7 @@ highest performance). For example, \textit{bzip2} is CPU bound and therefore
 its performance at memory frequency of 200MHz is within 3\% of performance at a
 memory frequency of 800MHz while the CPU is running at 1000MHz. By sacrificing that
 3\% of performance, the system could have consumed 1/4 the memory background
-energy saving 2.7\% of the system energy and staying well under the given inefficiency budget.
+energy, saving 2.7\% of the system energy and staying well under the given inefficiency budget.
 %\end{enumerate}
 We believe that, if the user is willing to sacrifice some performance under
@@ -48,7 +48,7 @@ the system.
 \subsection{Performance Clusters}
 We search for the performance clusters using an algorithm that is similar to the approach we used to find the optimal settings. We
-first filter the settings that fall within a given inefficiency budget, and
+first filter the settings that fall within a given inefficiency budget and
 then search for the optimal settings in the first pass. In the second pass, we find all of the
 settings that have a speedup within the specified \textit{cluster threshold} of the optimal performance.
@@ -95,7 +95,7 @@ compromising performance by setting low inefficiency budgets to save energy.
 Figures~\ref{clusters-gobmk}(c),~\ref{clusters-gobmk}(d) plot the
 performance clusters for \textit{gobmk} for inefficiency budget of 1.3 and
-cluster thresholds of 1\% and 5\% respectively. As we saw in
+cluster thresholds of 1\% and 5\% respectively. As we observed in
 Figure~\ref{gobmk-optimal}, the optimal settings for \textit{gobmk} change
 every sample (of length 10 million instructions) and follows
 application phases (CPI). Figure~\ref{clusters-gobmk}(c) shows that by
@@ -118,7 +118,8 @@ Figures~\ref{clusters-gobmk}(a),~\ref{clusters-gobmk}(c) plot the performance
 clusters for \textit{gobmk} for two different inefficiency budgets of 1.0 and 1.3 for
 cluster threshold of 1\%. 
 %\XXXnote{reword next sentence? -Dave}
-Not all of the stable regions increase in length with increasing inefficiency but instead depends on the workload. 
+Not all of the stable regions increase in length with increasing inefficiency;
+this trend varies with workloads.
 %Increase in the length of stable regions with increase in
 %inefficiency is a 
 %function of workload characteristics. 
@@ -344,8 +345,8 @@ runs at one setting, sample 8-9 runs at another setting and sample 10 runs at a
 different setting due to the availability of more (and better) choices.
 %\XXXnote{sounds wordy -Dave}. 
 In our system, we observed only a small improvement in performance (\textless
-1\%) with higher number of frequency steps when
-tuning is free as optimal
+1\%) with an increased number of frequency steps when
+tuning is free, as optimal
 settings in both cases were off by only a few MHz. It is the balance between the
 tuning overhead and the energy-performance savings that is
 critical in deciding the correct size of the search space.
@@ -2,7 +2,7 @@
 \label{sec-sys-methodology}
 Energy management algorithms must tune the underlying hardware components to
 keep the system within the given inefficiency budget. Hardware components
-provide multiple knobs that can be tuned to trade-off performance for energy
+provide multiple "knobs" that can be tuned to trade-off performance for energy
 savings. For example, the energy consumed by the CPU can be managed by tuning
 its frequency and voltage. 
 %DRAM energy can be
@@ -121,11 +121,12 @@ being 1.25V.
 %0.02V/30MHz. The voltage and frequency pairs match with the frequency steps
 %used by the Nexus S. 
-For the memory system, we simulated a LPDDR3 single channel, one rank memory access using an open-page
-policy. Timing and current parameters for LPDDR3 are configured as specified in
-data sheets from Micron~\cite{micronspec-url}. Memory clock domain is configured with a
-frequency range of 200MHz to 800MHz. As mentioned earlier, we did not scale memory
-voltage. The power supplies---VDD and VDD2---for LPDDR3 are fixed at 1.8V and 1.2V respectively. 
+For the memory system, we simulated a LPDDR3 single channel, one rank memory
+using an open-page access policy. Timing and current parameters for LPDDR3 are
+configured as specified in data sheets from Micron~\cite{micronspec-url}. Memory
+clock domain is configured with a frequency range of 200MHz to 800MHz. As
+mentioned earlier, we did not scale memory voltage. The power supplies---VDD and
+VDD2---for LPDDR3 are fixed at 1.8V and 1.2V respectively. 
 We first simulated 12 integer and 9 floating point SPEC CPU2006
 benchmarks~\cite{henning2006spec}, with each benchmark either running to