Computing Low-Rank Approximation of a Dense Matrix on Multicore CPUs with a GPU and Its Application to Solving a Hierarchically Semiseparable Linear System of Equations

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static-preview.hindawi.com/articles/sp/volume-2015/246019/figures/246019.fig.004a.svgz" name="246019.fig.004a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>Effect of condition estimator</td></tr><tr class="fig-image" id="b"><td><object data="https://static-preview.hindawi.com/articles/sp/volume-2015/246019/figures/246019.fig.004b.svgz" name="246019.fig.004b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>Effect of tuning the postprocess</td></tr></table>

<div>Solution time of original StruMF (left bar) and after modification (right bars), using ICE and tuning the postprocess in left and right plots.</div>

Scientific Programming

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Figure 4

Figure 4: Computing Low-Rank Approximation of a Dense Matrix on Multicore CPUs with a GPU and Its Application to Solving a Hierarchically Semiseparable Linear System of Equations 

Figure 4 | Computing Low-Rank Approximation of a Dense Matrix on Multicore CPUs with a GPU and Its Application to Solving a Hierarchically Semiseparable Linear System of Equations 