Commit bf41694afb1b02585ece2f87942338199a6d959b

Authored by Rizwana Begum
1 parent 42bf1102

edits

optimal_performance.tex
@@ -97,7 +97,7 @@ algorithm presented by CoScale~\cite{deng2012coscale} takes 5us to find optimal @@ -97,7 +97,7 @@ algorithm presented by CoScale~\cite{deng2012coscale} takes 5us to find optimal
97 frequency settings, time taken by PLLs to change voltage and frequency in commercial processors is in the 97 frequency settings, time taken by PLLs to change voltage and frequency in commercial processors is in the
98 order of 10s of microseconds. 98 order of 10s of microseconds.
99 Reducing the frequency at which tuning algorithms need to re-tune is critical to 99 Reducing the frequency at which tuning algorithms need to re-tune is critical to
100 -reduce the cost of tuning overhead on application performance. 100 +reduce the impact of tuning overhead on application performance.
101 101
102 %\item 102 %\item
103 \noindent \textit{Limited energy performance trade-off options.} Choosing the 103 \noindent \textit{Limited energy performance trade-off options.} Choosing the
performance_clusters.tex
@@ -26,7 +26,7 @@ the system. @@ -26,7 +26,7 @@ the system.
26 We search for the performance clusters using an algorithm that is similar to the approach we used to find the optimal settings. We 26 We search for the performance clusters using an algorithm that is similar to the approach we used to find the optimal settings. We
27 first filter the settings that fall within a given inefficiency budget and 27 first filter the settings that fall within a given inefficiency budget and
28 then search for the optimal settings in the first pass. In the second pass, we find all of the 28 then search for the optimal settings in the first pass. In the second pass, we find all of the
29 -settings that have a speedup within the specified \textit{cluster threshold} of the optimal performance. 29 +settings that have a speedup within the specified cluster threshold of the optimal performance.
30 \begin{figure}[t] 30 \begin{figure}[t]
31 \centering 31 \centering
32 \includegraphics[width=\columnwidth]{./figures/plots/496/stable_line_plots/lbm_stable_lineplot_annotated_5.pdf} 32 \includegraphics[width=\columnwidth]{./figures/plots/496/stable_line_plots/lbm_stable_lineplot_annotated_5.pdf}
@@ -96,13 +96,13 @@ Figures~\ref{clusters-gobmk}(c),~\ref{clusters-gobmk}(d) plot the @@ -96,13 +96,13 @@ Figures~\ref{clusters-gobmk}(c),~\ref{clusters-gobmk}(d) plot the
96 performance clusters for \textit{gobmk} for inefficiency budget of 1.3 and 96 performance clusters for \textit{gobmk} for inefficiency budget of 1.3 and
97 cluster thresholds of 1\% and 5\% respectively. As we observed in 97 cluster thresholds of 1\% and 5\% respectively. As we observed in
98 Figure~\ref{gobmk-optimal}, the optimal settings for \textit{gobmk} change 98 Figure~\ref{gobmk-optimal}, the optimal settings for \textit{gobmk} change
99 -every sample (of length 10 million instructions) and follows 99 +every sample (of length 10 million instructions) at inefficiency of 1.3 and follow
100 application phases (CPI). Figure~\ref{clusters-gobmk}(c) shows that by 100 application phases (CPI). Figure~\ref{clusters-gobmk}(c) shows that by
101 allowing just 1\% performance degradation, the number of settings 101 allowing just 1\% performance degradation, the number of settings
102 available to choose from increase. For example, for sample 11, the 102 available to choose from increase. For example, for sample 11, the
103 -optimal settings were at 1000MHz CPU and 500MHz memory. With 1\% 103 +optimal settings were at 920MHz CPU and 580MHz memory. With 1\%
104 cluster threshold, the range of available frequencies increases to 104 cluster threshold, the range of available frequencies increases to
105 -970MHz-1000MHz for CPU and 420MHz-580MHz for memory. With a 5\% 105 +900MHz-9200MHz for CPU and 420MHz-580MHz for memory. With a 5\%
106 cluster threshold, the range of available frequencies increases 106 cluster threshold, the range of available frequencies increases
107 further as shown in Figure~\ref{clusters-gobmk}(d). With an increase in number of available settings, the 107 further as shown in Figure~\ref{clusters-gobmk}(d). With an increase in number of available settings, the
108 probability of finding common settings in two consecutive samples 108 probability of finding common settings in two consecutive samples