Specified variable and added change to the last review point.
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@@ -102,8 +102,13 @@ MPI processes. Each process was set to spawn $10$ threads, resulting in a total
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$100$ threads working in parallel. We include results for $1$ million MC cycles
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in Appendix \ref{sec:additional_results}
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$\boldsymbol{Rewrite}$ We used a profiler to make sure the program was fully optimized which found that the
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workload was balanced, the threads was not left idle to long/not a lot of downtime.
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To evaluate the performance of the parallelization, we used a profiler. The
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assessment output can be found in Appendix \ref{sec:additional_results} in Figure
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\ref{fig:scorep_assessment}. The assessment shows a lower score for the MPI load
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balance, compared to the OpenMP load balance. The master process gatheres all the
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data using blocking communication, resulting in the other processes waiting. This
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results in one process, the master, having to work more. The OpenMP load balance
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score is very good, suggesting the threads are not left idle for long periods.
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In Figure \ref{fig:phase_energy_10M}, for the larger lattices we observe a sharper
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increase in $\langle \epsilon \rangle$ in the temperature range $T \in [2.25, 2.35]$. %]
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@@ -179,10 +184,10 @@ the lattice size increase toward infinity, $1/L$ approaches zero. %
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\end{figure}
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We used linear regression to find the intercept $\beta_{0}$, which gives us an estimated value
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of the critical temperature for a lattice of infinite size. The estimated critical temperature
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is $T_{c \text{num}} \approx 2.2693 J/k_{B}$. We also compared the
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is $T_{c}^{*}(L = \infty) \approx 2.2693 J/k_{B}$. We also compared the
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estimate with the analytical solution, the relative error of our estimate is
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\begin{equation*}
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\text{Relative error} = \frac{T_{c \text{ numerical}} - T_{c \text{ analytical}}}{T_{c \text{ analytical}}} \approx 5.05405 \cdot 10^{-5} J/k_{B}
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\text{Relative error} = \frac{T_{c}^{*} - T_{c \text{ analytical}}}{T_{c \text{ analytical}}} \approx 5.05405 \cdot 10^{-5} J/k_{B}
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\end{equation*}
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\end{document}
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