[心得] Matlab Distributed Computing

看板MATLAB作者lihgong (Q.Q)時間18年前 (2006/08/16 09:20)推噓2(2推 0噓 4→)

留言6則, 2人參與討論串1/1

測試環境 Computer1: P4 3.4G, 1G RAM Win XP SP2, Matlab 2006a computer name: QQMOU Job-Manager name: QQMOU_jobmanager Worker name: QQMOU_worker Computer2: P4 2.8G, 1G RAM Win XP SP2, Matlab 2006a computer name: mychat-7b73b358 Worker name: QQMOU_worker1 測試結果 Dist-comp: 131.18 sec Computer1: 226.42 sec 結論利用 dist-computing 確實可以加速然而 dist-computing 協定本身的 overhead 頗重但是當運算量極大時, protocol 的 overhead 可以忽略以上結果顯示, 運用兩台速度相近的電腦計算, 得到 42% 的加速 ---- 建構 cluster 1. 設定 MDCE server $MATLAB\toolbox\distcomp\bin\win32\mdce install $MATLAB\toolbox\distcomp\bin\win32\mdce start 2. 設定 job-manager (僅需一台) $MATLAB\toolbox\distcomp\bin\win32\startjobmanager.bat -name QQMOU_jobmanager -clean -v ps. 我的 job manager 裝在 computer 1 QQMOU_jobmanager 是名字, 可以自己取 3. 設定 worker (在每一台都要設定) Computer 1: $MATLAB\toolbox\distcomp\bin\win32\startworker.bat -name QQMOU_worker -jobmanager QQMOU_jobmanager -jobmanagerhost QQMOU -clean -v Computer 2: $MATLAB\toolbox\distcomp\bin\win32\startworker.bat -name QQMOU_worker1 -jobmanager QQMOU_jobmanager -jobmanagerhost QQMOU -clean -v 4. 檢查設定 (可以在每一個 node 上做) $MATLAB\toolbox\distcomp\bin\win32\nodestatus.bat -infolevel 2 ---- 分別用 dist-computing 和單機執行以下函數: [fun.m] function x = fun(times) x = 0; for i=1:times x = x + sum(randn(1,10000)); x = x / 10000; end 單機執行 tic % 啟動碼表 fun(30000); fun(30000); fun(30000); fun(30000); fun(30000); fun(30000); fun(30000); fun(30000); fun(30000); fun(30000); fun(30000); fun(30000); toc % 結束碼表並計算時間 dist-computing 版本 tic % 碼表啟動 % 設定 jobmanager 的訊息 jobmanager_name = 'QQMOU_jobmanager'; jobmanager_hostname = 'QQMOU'; % 先連線到 job manager jm = findResource('scheduler', 'type', 'jobmanager', ... 'name',jobmanager_name,'LookupURL',jobmanager_hostname); % 在 job manager 裡建立一個 job j = createJob(jm); % 設定檔案分享 set(j,'FileDependencies',{'fun.m'}) % 描述 job 裡的 tasks createTask(j, @fun, 1, {30000}); createTask(j, @fun, 1, {30000}); createTask(j, @fun, 1, {30000}); createTask(j, @fun, 1, {30000}); createTask(j, @fun, 1, {30000}); createTask(j, @fun, 1, {30000}); createTask(j, @fun, 1, {30000}); createTask(j, @fun, 1, {30000}); createTask(j, @fun, 1, {30000}); createTask(j, @fun, 1, {30000}); createTask(j, @fun, 1, {30000}); createTask(j, @fun, 1, {30000}); % 提交 job submit(j); waitForState(j); % 取回運算資料 results = getAllOutputArguments(j) % 運算資料存放的形式是 cell % 可以用 cell2mat 轉回 matrix % 砍掉已經結束的 jobs destroy(j); toc ---- Reference [1] Matlab Online Help, Distributed Computing Toolbox [2] Distributed Computing Toolbox User's Guide http://www.mathworks.com/access/helpdesk/help/pdf_doc/distcomp/distcomp.pdf [3] MATLAB Distributed Computing Engine System Administrator's Guide http://www.mathworks.com/access/helpdesk/help/pdf_doc/mdce/mdce.pdf [4] "Stage 2: Configure Your Cluster" http://0rz.net/be1Jx [5] "Stage 4: Test Your Distributed Computing Environment" http://0rz.net/011Hq -- ※ 發信站: 批踢踢實業坊(ptt.cc) ◆ From: 140.113.128.237