\small
ONETEP performs transforms in each dimension so each FFT only operates on 50\%
-zeroes instead of 87.5\% of the naïve strategy.
+zeroes instead of the 87.5\% of the naïve strategy.
\vspace{1em}
\frame{
-\frametitle{Performance Results with FFTW\footnote{Core i7-2600, 3.4GHz, 8MB L2
+\frametitle{Performance Results with FFTW\footnote{Intel Core i7-2600, 3.4GHz, 8MB L2
cache, FFTW 3.3}}
\footnotesize
\frame{
-\frametitle{Performance Results with FFTW\footnote{Core i7-2600, 3.4GHz, 8MB L2
+\frametitle{Performance Results with FFTW\footnote{Intel Core i7-2600, 3.4GHz, 8MB L2
cache, FFTW 3.3}}
\footnotesize
-We can filter the results to those which FFTW likes best (products of small
+Filtering the results to those which FFTW likes best (products of small
primes). Specifically, sizes of the form $2^a3^b5^c7^d11^e13^f$ where $e+f<2$.
\centering
\item In our standalone benchmarks, our results give around a 35\% reduction in
execution time over ONETEP's approach for FFTW's preferred sizes.
-\item In practise, we found the actual reduction to be a lot less and overall
+\item In practice, we found the actual reduction to be a lot less and overall
reduction in execution time to usually be less than 5\%.
\item When doing Fourier interpolation, ONETEP spends a lot of time in its
\frame{
-\frametitle{ONETEP Interpolation Routine Timings\footnote{Core i7-2600, 3.4GHz,
-8MB L2 182 cache, FFTW 3.3}}
+\frametitle{ONETEP Interpolation Routine Timings\footnote{ONETEP 3.3.9.5, Intel
+Core i7-2600, 3.4GHz, 8MB L2 cache, FFTW 3.3}}
\centering
\resizebox{0.65\textwidth}{!}{
\frame{
-\frametitle{ONETEP Total Execution Time Timings\footnote{Core i7-2600, 3.4GHz,
-8MB L2 182 cache, FFTW 3.3}}
+\frametitle{ONETEP Total Execution Time Timings\footnote{ONETEP 3.3.9.5, Intel
+Core i7-2600, 3.4GHz, 8MB L2 cache, FFTW 3.3}}
\centering
\resizebox{0.65\textwidth}{!}{