<p>We consider two natural variants of the problem of minimum spanning tree (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="37" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text {MST}\)</EquationSource> </InlineEquation>) of a graph in the parallel setting: <i>MST verification</i> (verifying if a given tree is an <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="37" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text {MST}\)</EquationSource> </InlineEquation>) and the <i>sensitivity analysis of an MST</i> (finding the lowest cost replacement edge for each edge of the <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="37" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text {MST}\)</EquationSource> </InlineEquation>). These two problems have been studied extensively for sequential algorithms and for parallel algorithms in the <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq4.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="52" /> </InlineMediaObject> <EquationSource Format="TEX">\(\textrm{PRAM}\)</EquationSource> </InlineEquation> model of computation. In this paper, we extend the study to the standard model of <i>Massive Parallel Computation</i> (<InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq5.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="39" /> </InlineMediaObject> <EquationSource Format="TEX">\(\textrm{MPC}\)</EquationSource> </InlineEquation>). It is known that for graphs of diameter <i>D</i>, the connectivity problem can be solved in <InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq6.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="141" /> </InlineMediaObject> <EquationSource Format="TEX">\(O(\log D + \log \log n)\)</EquationSource> </InlineEquation> rounds on an <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq5.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="39" /> </InlineMediaObject> <EquationSource Format="TEX">\(\textrm{MPC}\)</EquationSource> </InlineEquation> with <i>low local memory</i> (each machine can store only <InlineEquation ID="IEq8"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq8.gif" Format="GIF" Height="21" Rendition="HTML" Resolution="72" Type="Linedraw" Width="43" /> </InlineMediaObject> <EquationSource Format="TEX">\(O(n^{\delta })\)</EquationSource> </InlineEquation> words for an arbitrary constant <InlineEquation ID="IEq9"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq9.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="40" /> </InlineMediaObject> <EquationSource Format="TEX">\(\delta &gt; 0\)</EquationSource> </InlineEquation>) and with <i>linear global memory</i>, that is, with <i>optimal utilization</i>. However, for the related task of finding an <InlineEquation ID="IEq10"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="37" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text {MST}\)</EquationSource> </InlineEquation>, we need <InlineEquation ID="IEq11"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq11.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="89" /> </InlineMediaObject> <EquationSource Format="TEX">\(\Omega (\log D_{\text {MST}})\)</EquationSource> </InlineEquation> rounds, where <InlineEquation ID="IEq12"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq12.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="41" /> </InlineMediaObject> <EquationSource Format="TEX">\(D_{\text {MST}}\)</EquationSource> </InlineEquation> denotes the diameter of the minimum spanning tree. The state of the art upper bound for <InlineEquation ID="IEq13"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="37" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text {MST}\)</EquationSource> </InlineEquation> is <InlineEquation ID="IEq14"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq14.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="61" /> </InlineMediaObject> <EquationSource Format="TEX">\(O(\log n)\)</EquationSource> </InlineEquation> rounds; the result follows by simulating existing <InlineEquation ID="IEq15"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq4.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="52" /> </InlineMediaObject> <EquationSource Format="TEX">\(\textrm{PRAM}\)</EquationSource> </InlineEquation> algorithms. While this bound may be optimal for general graphs, the benchmark of connectivity and lower bound for <InlineEquation ID="IEq16"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="37" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text {MST}\)</EquationSource> </InlineEquation> suggest the target bound of <InlineEquation ID="IEq17"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq17.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="91" /> </InlineMediaObject> <EquationSource Format="TEX">\(O(\log D_\text {MST})\)</EquationSource> </InlineEquation> rounds, or possibly <InlineEquation ID="IEq18"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq18.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="167" /> </InlineMediaObject> <EquationSource Format="TEX">\(O(\log D_\text {MST} + \log \log n)\)</EquationSource> </InlineEquation> rounds. As for now, we do not know if this bound is achievable for the <InlineEquation ID="IEq19"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="37" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text {MST}\)</EquationSource> </InlineEquation> problem on an <InlineEquation ID="IEq20"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq5.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="39" /> </InlineMediaObject> <EquationSource Format="TEX">\(\textrm{MPC}\)</EquationSource> </InlineEquation> with low local memory and linear global memory. In this paper, we show that two natural variants of the <InlineEquation ID="IEq21"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="37" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text {MST}\)</EquationSource> </InlineEquation> problem: <InlineEquation ID="IEq22"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="37" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text {MST}\)</EquationSource> </InlineEquation> verification and sensitivity analysis of an <InlineEquation ID="IEq23"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="37" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text {MST}\)</EquationSource> </InlineEquation>, can be completed in <InlineEquation ID="IEq24"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq24.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="73" /> </InlineMediaObject> <EquationSource Format="TEX">\(O(\log D_T)\)</EquationSource> </InlineEquation> rounds on an <InlineEquation ID="IEq25"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq5.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="39" /> </InlineMediaObject> <EquationSource Format="TEX">\(\textrm{MPC}\)</EquationSource> </InlineEquation> with low local memory and with linear global memory, that is, with optimal utilization; here <InlineEquation ID="IEq26"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq26.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="25" /> </InlineMediaObject> <EquationSource Format="TEX">\(D_T\)</EquationSource> </InlineEquation> is the diameter of the input “candidate <InlineEquation ID="IEq27"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="453_2025_1332_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="37" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text {MST}\)</EquationSource> </InlineEquation> ” <i>T</i>. The algorithms asymptotically match our lower bound, conditioned on the 1-vs-2-cycle conjecture.</p>

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Log-Diameter MST Verification and Sensitivity in MPC

  • Sam Coy,
  • Artur Czumaj,
  • Gopinath Mishra,
  • Anish Mukherjee

摘要

We consider two natural variants of the problem of minimum spanning tree ( \(\text {MST}\) ) of a graph in the parallel setting: MST verification (verifying if a given tree is an \(\text {MST}\) ) and the sensitivity analysis of an MST (finding the lowest cost replacement edge for each edge of the \(\text {MST}\) ). These two problems have been studied extensively for sequential algorithms and for parallel algorithms in the \(\textrm{PRAM}\) model of computation. In this paper, we extend the study to the standard model of Massive Parallel Computation ( \(\textrm{MPC}\) ). It is known that for graphs of diameter D, the connectivity problem can be solved in \(O(\log D + \log \log n)\) rounds on an \(\textrm{MPC}\) with low local memory (each machine can store only \(O(n^{\delta })\) words for an arbitrary constant \(\delta > 0\) ) and with linear global memory, that is, with optimal utilization. However, for the related task of finding an \(\text {MST}\) , we need \(\Omega (\log D_{\text {MST}})\) rounds, where \(D_{\text {MST}}\) denotes the diameter of the minimum spanning tree. The state of the art upper bound for \(\text {MST}\) is \(O(\log n)\) rounds; the result follows by simulating existing \(\textrm{PRAM}\) algorithms. While this bound may be optimal for general graphs, the benchmark of connectivity and lower bound for \(\text {MST}\) suggest the target bound of \(O(\log D_\text {MST})\) rounds, or possibly \(O(\log D_\text {MST} + \log \log n)\) rounds. As for now, we do not know if this bound is achievable for the \(\text {MST}\) problem on an \(\textrm{MPC}\) with low local memory and linear global memory. In this paper, we show that two natural variants of the \(\text {MST}\) problem: \(\text {MST}\) verification and sensitivity analysis of an \(\text {MST}\) , can be completed in \(O(\log D_T)\) rounds on an \(\textrm{MPC}\) with low local memory and with linear global memory, that is, with optimal utilization; here \(D_T\) is the diameter of the input “candidate \(\text {MST}\) T. The algorithms asymptotically match our lower bound, conditioned on the 1-vs-2-cycle conjecture.